Computational Memory Lab - User contributions [en]

PEERS

2015-10-28T14:26:28Z

Healeym: /* Information for researchers */

The '''Penn Electrophysiology of Encoding and Retrieval Study (PEERS)''' is a multi-session experiment looking at scalp EEG during free recall and recognition. We recruit both younger adults (16-30) and older adults (60-90).

Please see the [[#How to get involved|information below]] if you are interested in volunteering for this study.

== Information for volunteers ==
===Studying the brain===

At the [[Main Page| Computational Memory Lab]], we use brain recordings to better understand how human memory works. We are devoted to learning how people form and retrieve memories. Our hope is that what we learn in our experiments will pave the way for therapies to improve the lives of people with brain disorders and restore normal memory function to those who have lost it.

===About our tasks===

The Penn Electrophysiology of Encoding and Retrieval Study focuses on episodic memory. Episodic memory is memory for specific events that happened in a specific place and time (e.g., your 16th birthday party or your breakfast this morning). As such, episodic memory places the events of our lives on an autobiographical timeline. They allow us to remember whether we took our medicine this morning and where we parked our car today. Because these memories are unique to each person individually, we must find a controlled way to learn about this form of human memory. In this study, we use lists of words with each individual word representing an "episode" in time. Very simply, we will ask you to study lists of words and then recall them in any order.

<center>
<gallery widths=450px heights=350px perrow=2>
File:ScalpTest.jpg|Our experiment
File:EEGNet.jpg|One of our nets
</gallery>
</center>

===What is EEG?===

"EEG" stands for electroencephalogram. There are many different types of EEG nets, and you may even have worn one before. The EEG nets we use do not require gel or scalp abrasion. The electrodes are housed above a sponge, which sits on your scalp, that is soaked in an electrolyte solution to allow for good conductivity of your brain's electrical activity. This solution is comprised of baby shampoo (to dissolve the oils on your scalp), distilled water, and potassium chloride (a kind of salt). Although it is rare, some people do experience mild irritation from the solution.

=== How to get involved ===
You must meet the following criteria:

* You must be right-handed
* English must be the first language learned to speak
* You must be able to sit still for up to two hours

Because the study involves over 20 sessions, be aware that there is some time commitment involved. Generally we ask that our participants come in at least twice a week over the course of two or three months.

A note: you must be able to take out any ear- or eyebrow-area jewelry you have. Also, some hairstyles may interfere with the net coming in contact with your scalp, such as non-removable braids, dreadlocks, or very thick, long hair. We should be able to tell you if this is an issue as soon as you come in, but if you have any questions about these requirements, feel free to ask.

Contact us at [mailto:memorylab@psych.upenn.edu memorylab@psych.upenn.edu] or 215-746-0407 to see if we are running sessions for which you may qualify.

== Information for researchers ==

PEERS is an extended experiment consisting of 20 sessions of free recall and recognition memory tasks, followed by 2 sessions of standardized psychometric tests. High-density scalp EEG is recorded during free recall/recognition sessions.

The following publications draw on the PEERS dataset:
* Healey, M. K. and Kahana, M. J. (in press) A four–component model of age–related memory change. Psychological Review. [[Publications#HealKaha15|(more)]]
* Healey, M. K., Crutchley, P., and Kahana, M. J. (2014). Individual differences in memory search and their relation to intelligence. Journal of Experimental Psychology: General, 143(4), 1553–1569 [[Publications#HealEtal14|(more)]]
* Healey, M. K. and Kahana, M. J. (2014). Is memory search governed by universal principles or idiosyncratic strategies? Journal of Experimental Psychology: General, 143, 575–596 [[Publications#HealKaha14|(more)]]
* Lohnas, L. J. and Kahana, M. J. (2014a). Compound cuing in free recall. Journal of Experimental Psychology: Learning, Memory and Cogntion, 40(1), 12-24 [[Publications#LohnKaha12a|(more)]]
* Lohnas, L. J. and Kahana, M. J. (2013). Parametric effects of word frequency effect in memory for mixed frequency lists. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(6), 1943–1946. [[Publications#LohnKaha13|(more)]]
* Miller, J. F., Kahana, M. J., and Weidemann, C. T. (2012). Recall termination in free recall. ''Memory & Cognition'', 40(4), 540–550. [[Publications#MillEtal12|(more)]]

(List is current as of Oct 2015. See [[Publications]] for all lab publications.)

=== PEERS dataset ===

The entirety of the PEERS dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]. Please see [[Data Archive]] for datasets used in individual publications.

PEERS

2015-10-28T14:20:07Z

Healeym: /* How to get involved */

The '''Penn Electrophysiology of Encoding and Retrieval Study (PEERS)''' is a multi-session experiment looking at scalp EEG during free recall and recognition. We recruit both younger adults (16-30) and older adults (60-90).

Please see the [[#How to get involved|information below]] if you are interested in volunteering for this study.

== Information for volunteers ==
===Studying the brain===

At the [[Main Page| Computational Memory Lab]], we use brain recordings to better understand how human memory works. We are devoted to learning how people form and retrieve memories. Our hope is that what we learn in our experiments will pave the way for therapies to improve the lives of people with brain disorders and restore normal memory function to those who have lost it.

===About our tasks===

The Penn Electrophysiology of Encoding and Retrieval Study focuses on episodic memory. Episodic memory is memory for specific events that happened in a specific place and time (e.g., your 16th birthday party or your breakfast this morning). As such, episodic memory places the events of our lives on an autobiographical timeline. They allow us to remember whether we took our medicine this morning and where we parked our car today. Because these memories are unique to each person individually, we must find a controlled way to learn about this form of human memory. In this study, we use lists of words with each individual word representing an "episode" in time. Very simply, we will ask you to study lists of words and then recall them in any order.

<center>
<gallery widths=450px heights=350px perrow=2>
File:ScalpTest.jpg|Our experiment
File:EEGNet.jpg|One of our nets
</gallery>
</center>

===What is EEG?===

"EEG" stands for electroencephalogram. There are many different types of EEG nets, and you may even have worn one before. The EEG nets we use do not require gel or scalp abrasion. The electrodes are housed above a sponge, which sits on your scalp, that is soaked in an electrolyte solution to allow for good conductivity of your brain's electrical activity. This solution is comprised of baby shampoo (to dissolve the oils on your scalp), distilled water, and potassium chloride (a kind of salt). Although it is rare, some people do experience mild irritation from the solution.

=== How to get involved ===
You must meet the following criteria:

* You must be right-handed
* English must be the first language learned to speak
* You must be able to sit still for up to two hours

Because the study involves over 20 sessions, be aware that there is some time commitment involved. Generally we ask that our participants come in at least twice a week over the course of two or three months.

A note: you must be able to take out any ear- or eyebrow-area jewelry you have. Also, some hairstyles may interfere with the net coming in contact with your scalp, such as non-removable braids, dreadlocks, or very thick, long hair. We should be able to tell you if this is an issue as soon as you come in, but if you have any questions about these requirements, feel free to ask.

Contact us at [mailto:memorylab@psych.upenn.edu memorylab@psych.upenn.edu] or 215-746-0407 to see if we are running sessions for which you may qualify.

== Information for researchers ==

PEERS is an extended experiment consisting of 20 sessions of free recall and recognition memory tasks, followed by 2 sessions of standardized psychometric tests. High-density scalp EEG is recorded during free recall/recognition sessions.

The following publications draw on the PEERS dataset:

* Healey, M. K., Crutchley, P., and Kahana, M. J. Individual differences in memory search and their relation to intelligence. ''JEP: General'' (in press). [[Publications#HealEtal13b|(more)]]
* Healey, M. K. and Kahana, M. J. Is memory search governed by universal principles or idiosyncratic strategies? ''JEP: General'' (in press). [[Publications#HealKaha13|(more)]]
* Lohnas, L. J. and Kahana, M. J. Compound cueing in free recall. ''JEP:LMC'' (in press). [[Publications#LohnKaha12a|(more)]]
* Lohnas, L. J. and Kahana, M. J. Parametric effects of word frequency effect in memory for mixed frequency lists. ''JEP:LMC'' (in press). [[Publications#LohnKaha12|(more)]]
* Miller, J. F., Kahana, M. J., and Weidemann, C. T. (2012). Recall termination in free recall. ''Memory & Cognition'', 40(4), 540–550. [[Publications#MillEtal12|(more)]]

(List is current as of May 2013. See [[Publications]] for all lab publications.)

=== PEERS dataset ===

The entirety of the PEERS dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]. Please see [[Data Archive]] for datasets used in individual publications.

PEERS

2015-10-28T14:19:24Z

Healeym: /* How to get involved */

The '''Penn Electrophysiology of Encoding and Retrieval Study (PEERS)''' is a multi-session experiment looking at scalp EEG during free recall and recognition. We recruit both younger adults (16-30) and older adults (60-90).

Please see the [[#How to get involved|information below]] if you are interested in volunteering for this study.

== Information for volunteers ==
===Studying the brain===

At the [[Main Page| Computational Memory Lab]], we use brain recordings to better understand how human memory works. We are devoted to learning how people form and retrieve memories. Our hope is that what we learn in our experiments will pave the way for therapies to improve the lives of people with brain disorders and restore normal memory function to those who have lost it.

===About our tasks===

The Penn Electrophysiology of Encoding and Retrieval Study focuses on episodic memory. Episodic memory is memory for specific events that happened in a specific place and time (e.g., your 16th birthday party or your breakfast this morning). As such, episodic memory places the events of our lives on an autobiographical timeline. They allow us to remember whether we took our medicine this morning and where we parked our car today. Because these memories are unique to each person individually, we must find a controlled way to learn about this form of human memory. In this study, we use lists of words with each individual word representing an "episode" in time. Very simply, we will ask you to study lists of words and then recall them in any order.

<center>
<gallery widths=450px heights=350px perrow=2>
File:ScalpTest.jpg|Our experiment
File:EEGNet.jpg|One of our nets
</gallery>
</center>

===What is EEG?===

"EEG" stands for electroencephalogram. There are many different types of EEG nets, and you may even have worn one before. The EEG nets we use do not require gel or scalp abrasion. The electrodes are housed above a sponge, which sits on your scalp, that is soaked in an electrolyte solution to allow for good conductivity of your brain's electrical activity. This solution is comprised of baby shampoo (to dissolve the oils on your scalp), distilled water, and potassium chloride (a kind of salt). Although it is rare, some people do experience mild irritation from the solution.

=== How to get involved ===
You must meet the following criteria:

* You must be right-handed
* English must be the first language learned to speak
* You must be able to sit still for up to two hours

Because the study is a total of 22 sessions long, be aware that there is some time commitment involved. Generally we ask that our participants come in at least twice a week over the course of two or three months.

A note: you must be able to take out any ear- or eyebrow-area jewelry you have. Also, some hairstyles may interfere with the net coming in contact with your scalp, such as non-removable braids, dreadlocks, or very thick, long hair. We should be able to tell you if this is an issue as soon as you come in, but if you have any questions about these requirements, feel free to ask.

Contact us at [mailto:memorylab@psych.upenn.edu memorylab@psych.upenn.edu] or 215-746-0407 to see if we are running sessions for which you may qualify.

== Information for researchers ==

PEERS is an extended experiment consisting of 20 sessions of free recall and recognition memory tasks, followed by 2 sessions of standardized psychometric tests. High-density scalp EEG is recorded during free recall/recognition sessions.

The following publications draw on the PEERS dataset:

* Healey, M. K., Crutchley, P., and Kahana, M. J. Individual differences in memory search and their relation to intelligence. ''JEP: General'' (in press). [[Publications#HealEtal13b|(more)]]
* Healey, M. K. and Kahana, M. J. Is memory search governed by universal principles or idiosyncratic strategies? ''JEP: General'' (in press). [[Publications#HealKaha13|(more)]]
* Lohnas, L. J. and Kahana, M. J. Compound cueing in free recall. ''JEP:LMC'' (in press). [[Publications#LohnKaha12a|(more)]]
* Lohnas, L. J. and Kahana, M. J. Parametric effects of word frequency effect in memory for mixed frequency lists. ''JEP:LMC'' (in press). [[Publications#LohnKaha12|(more)]]
* Miller, J. F., Kahana, M. J., and Weidemann, C. T. (2012). Recall termination in free recall. ''Memory & Cognition'', 40(4), 540–550. [[Publications#MillEtal12|(more)]]

(List is current as of May 2013. See [[Publications]] for all lab publications.)

=== PEERS dataset ===

The entirety of the PEERS dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]. Please see [[Data Archive]] for datasets used in individual publications.

PEERS

2015-10-28T14:17:49Z

Healeym: /* About our tasks */

The '''Penn Electrophysiology of Encoding and Retrieval Study (PEERS)''' is a multi-session experiment looking at scalp EEG during free recall and recognition. We recruit both younger adults (16-30) and older adults (60-90).

Please see the [[#How to get involved|information below]] if you are interested in volunteering for this study.

== Information for volunteers ==
===Studying the brain===

At the [[Main Page| Computational Memory Lab]], we use brain recordings to better understand how human memory works. We are devoted to learning how people form and retrieve memories. Our hope is that what we learn in our experiments will pave the way for therapies to improve the lives of people with brain disorders and restore normal memory function to those who have lost it.

===About our tasks===

The Penn Electrophysiology of Encoding and Retrieval Study focuses on episodic memory. Episodic memory is memory for specific events that happened in a specific place and time (e.g., your 16th birthday party or your breakfast this morning). As such, episodic memory places the events of our lives on an autobiographical timeline. They allow us to remember whether we took our medicine this morning and where we parked our car today. Because these memories are unique to each person individually, we must find a controlled way to learn about this form of human memory. In this study, we use lists of words with each individual word representing an "episode" in time. Very simply, we will ask you to study lists of words and then recall them in any order.

<center>
<gallery widths=450px heights=350px perrow=2>
File:ScalpTest.jpg|Our experiment
File:EEGNet.jpg|One of our nets
</gallery>
</center>

===What is EEG?===

"EEG" stands for electroencephalogram. There are many different types of EEG nets, and you may even have worn one before. The EEG nets we use do not require gel or scalp abrasion. The electrodes are housed above a sponge, which sits on your scalp, that is soaked in an electrolyte solution to allow for good conductivity of your brain's electrical activity. This solution is comprised of baby shampoo (to dissolve the oils on your scalp), distilled water, and potassium chloride (a kind of salt). Although it is rare, some people do experience mild irritation from the solution.

=== How to get involved ===
You must meet the following criteria:

* You must be right-handed
* English must be the first language learned to speak
* You must be aged 18-30
* You must be affiliated with a University. This means an undergrad or grad student, a recent graduate, or taking classes over the Summer. (This requirement is stipulated in our NIH grant.)
* You must be able to sit still for up to two hours

Because the study is a total of 22 sessions long, be aware that there is some time commitment involved. Generally we ask that our participants come in at least twice a week over the course of two or three months.

A note: you must be able to take out any ear- or eyebrow-area jewelry you have. Also, some hairstyles may interfere with the net coming in contact with your scalp, such as non-removable braids, dreadlocks, or very thick, long hair. We should be able to tell you if this is an issue as soon as you come in, but if you have any questions about these requirements, feel free to ask.

Contact us at [mailto:memorylab@psych.upenn.edu memorylab@psych.upenn.edu] or 215-746-0407 to see if we are running sessions for which you may qualify.

== Information for researchers ==

PEERS is an extended experiment consisting of 20 sessions of free recall and recognition memory tasks, followed by 2 sessions of standardized psychometric tests. High-density scalp EEG is recorded during free recall/recognition sessions.

The following publications draw on the PEERS dataset:

* Healey, M. K., Crutchley, P., and Kahana, M. J. Individual differences in memory search and their relation to intelligence. ''JEP: General'' (in press). [[Publications#HealEtal13b|(more)]]
* Healey, M. K. and Kahana, M. J. Is memory search governed by universal principles or idiosyncratic strategies? ''JEP: General'' (in press). [[Publications#HealKaha13|(more)]]
* Lohnas, L. J. and Kahana, M. J. Compound cueing in free recall. ''JEP:LMC'' (in press). [[Publications#LohnKaha12a|(more)]]
* Lohnas, L. J. and Kahana, M. J. Parametric effects of word frequency effect in memory for mixed frequency lists. ''JEP:LMC'' (in press). [[Publications#LohnKaha12|(more)]]
* Miller, J. F., Kahana, M. J., and Weidemann, C. T. (2012). Recall termination in free recall. ''Memory & Cognition'', 40(4), 540–550. [[Publications#MillEtal12|(more)]]

(List is current as of May 2013. See [[Publications]] for all lab publications.)

=== PEERS dataset ===

The entirety of the PEERS dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]. Please see [[Data Archive]] for datasets used in individual publications.

PEERS

2015-10-28T14:11:33Z

Healeym: /* Studying the brain */

The '''Penn Electrophysiology of Encoding and Retrieval Study (PEERS)''' is a multi-session experiment looking at scalp EEG during free recall and recognition. We recruit both younger adults (16-30) and older adults (60-90).

Please see the [[#How to get involved|information below]] if you are interested in volunteering for this study.

== Information for volunteers ==
===Studying the brain===

At the [[Main Page| Computational Memory Lab]], we use brain recordings to better understand how human memory works. We are devoted to learning how people form and retrieve memories. Our hope is that what we learn in our experiments will pave the way for therapies to improve the lives of people with brain disorders and restore normal memory function to those who have lost it.

===About our tasks===

The Penn Electrophysiology of Encoding and Retrieval Study focuses on episodic memory. This is your memory for everyday events (including people, places, and things) in time. Because these memories are unique to each person individually, we must find a controlled way to learn about this form of human memory. In this study, we use lists of words, each individual word representing an "episode" in time. Very simply, we will ask you to study lists of words and then recall them in any order.

<center>
<gallery widths=450px heights=350px perrow=2>
File:ScalpTest.jpg|Our experiment
File:EEGNet.jpg|One of our nets
</gallery>
</center>

===What is EEG?===

"EEG" stands for electroencephalogram. There are many different types of EEG nets, and you may even have worn one before. The EEG nets we use do not require gel or scalp abrasion. The electrodes are housed above a sponge, which sits on your scalp, that is soaked in an electrolyte solution to allow for good conductivity of your brain's electrical activity. This solution is comprised of baby shampoo (to dissolve the oils on your scalp), distilled water, and potassium chloride (a kind of salt). Although it is rare, some people do experience mild irritation from the solution.

=== How to get involved ===
You must meet the following criteria:

* You must be right-handed
* English must be the first language learned to speak
* You must be aged 18-30
* You must be affiliated with a University. This means an undergrad or grad student, a recent graduate, or taking classes over the Summer. (This requirement is stipulated in our NIH grant.)
* You must be able to sit still for up to two hours

Because the study is a total of 22 sessions long, be aware that there is some time commitment involved. Generally we ask that our participants come in at least twice a week over the course of two or three months.

A note: you must be able to take out any ear- or eyebrow-area jewelry you have. Also, some hairstyles may interfere with the net coming in contact with your scalp, such as non-removable braids, dreadlocks, or very thick, long hair. We should be able to tell you if this is an issue as soon as you come in, but if you have any questions about these requirements, feel free to ask.

Contact us at [mailto:memorylab@psych.upenn.edu memorylab@psych.upenn.edu] or 215-746-0407 to see if we are running sessions for which you may qualify.

== Information for researchers ==

PEERS is an extended experiment consisting of 20 sessions of free recall and recognition memory tasks, followed by 2 sessions of standardized psychometric tests. High-density scalp EEG is recorded during free recall/recognition sessions.

The following publications draw on the PEERS dataset:

* Healey, M. K., Crutchley, P., and Kahana, M. J. Individual differences in memory search and their relation to intelligence. ''JEP: General'' (in press). [[Publications#HealEtal13b|(more)]]
* Healey, M. K. and Kahana, M. J. Is memory search governed by universal principles or idiosyncratic strategies? ''JEP: General'' (in press). [[Publications#HealKaha13|(more)]]
* Lohnas, L. J. and Kahana, M. J. Compound cueing in free recall. ''JEP:LMC'' (in press). [[Publications#LohnKaha12a|(more)]]
* Lohnas, L. J. and Kahana, M. J. Parametric effects of word frequency effect in memory for mixed frequency lists. ''JEP:LMC'' (in press). [[Publications#LohnKaha12|(more)]]
* Miller, J. F., Kahana, M. J., and Weidemann, C. T. (2012). Recall termination in free recall. ''Memory & Cognition'', 40(4), 540–550. [[Publications#MillEtal12|(more)]]

(List is current as of May 2013. See [[Publications]] for all lab publications.)

=== PEERS dataset ===

The entirety of the PEERS dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]. Please see [[Data Archive]] for datasets used in individual publications.

PEERS

2015-10-28T14:09:48Z

Healeym:

The '''Penn Electrophysiology of Encoding and Retrieval Study (PEERS)''' is a multi-session experiment looking at scalp EEG during free recall and recognition. We recruit both younger adults (16-30) and older adults (60-90).

Please see the [[#How to get involved|information below]] if you are interested in volunteering for this study.

== Information for volunteers ==
===Studying the brain===

At the [[Main Page| Computational Memory Lab]], we use brain recordings to better understand how human memory works. We are devoted to learning how people form and retrieve memories. Eventually, we hope this information will be used to improve the lives of people with brain disorders and restore normal memory function to those who have lost it.

===About our tasks===

The Penn Electrophysiology of Encoding and Retrieval Study focuses on episodic memory. This is your memory for everyday events (including people, places, and things) in time. Because these memories are unique to each person individually, we must find a controlled way to learn about this form of human memory. In this study, we use lists of words, each individual word representing an "episode" in time. Very simply, we will ask you to study lists of words and then recall them in any order.

<center>
<gallery widths=450px heights=350px perrow=2>
File:ScalpTest.jpg|Our experiment
File:EEGNet.jpg|One of our nets
</gallery>
</center>

===What is EEG?===

"EEG" stands for electroencephalogram. There are many different types of EEG nets, and you may even have worn one before. The EEG nets we use do not require gel or scalp abrasion. The electrodes are housed above a sponge, which sits on your scalp, that is soaked in an electrolyte solution to allow for good conductivity of your brain's electrical activity. This solution is comprised of baby shampoo (to dissolve the oils on your scalp), distilled water, and potassium chloride (a kind of salt). Although it is rare, some people do experience mild irritation from the solution.

=== How to get involved ===
You must meet the following criteria:

* You must be right-handed
* English must be the first language learned to speak
* You must be aged 18-30
* You must be affiliated with a University. This means an undergrad or grad student, a recent graduate, or taking classes over the Summer. (This requirement is stipulated in our NIH grant.)
* You must be able to sit still for up to two hours

Because the study is a total of 22 sessions long, be aware that there is some time commitment involved. Generally we ask that our participants come in at least twice a week over the course of two or three months.

A note: you must be able to take out any ear- or eyebrow-area jewelry you have. Also, some hairstyles may interfere with the net coming in contact with your scalp, such as non-removable braids, dreadlocks, or very thick, long hair. We should be able to tell you if this is an issue as soon as you come in, but if you have any questions about these requirements, feel free to ask.

Contact us at [mailto:memorylab@psych.upenn.edu memorylab@psych.upenn.edu] or 215-746-0407 to see if we are running sessions for which you may qualify.

== Information for researchers ==

PEERS is an extended experiment consisting of 20 sessions of free recall and recognition memory tasks, followed by 2 sessions of standardized psychometric tests. High-density scalp EEG is recorded during free recall/recognition sessions.

The following publications draw on the PEERS dataset:

* Healey, M. K., Crutchley, P., and Kahana, M. J. Individual differences in memory search and their relation to intelligence. ''JEP: General'' (in press). [[Publications#HealEtal13b|(more)]]
* Healey, M. K. and Kahana, M. J. Is memory search governed by universal principles or idiosyncratic strategies? ''JEP: General'' (in press). [[Publications#HealKaha13|(more)]]
* Lohnas, L. J. and Kahana, M. J. Compound cueing in free recall. ''JEP:LMC'' (in press). [[Publications#LohnKaha12a|(more)]]
* Lohnas, L. J. and Kahana, M. J. Parametric effects of word frequency effect in memory for mixed frequency lists. ''JEP:LMC'' (in press). [[Publications#LohnKaha12|(more)]]
* Miller, J. F., Kahana, M. J., and Weidemann, C. T. (2012). Recall termination in free recall. ''Memory & Cognition'', 40(4), 540–550. [[Publications#MillEtal12|(more)]]

(List is current as of May 2013. See [[Publications]] for all lab publications.)

=== PEERS dataset ===

The entirety of the PEERS dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]. Please see [[Data Archive]] for datasets used in individual publications.

Jobs

2015-10-26T14:02:45Z

Healeym:

'''The Computational Memory Lab is currently seeking applications for the following positions:'''

[https://www.hr.upenn.edu/career/salary-offers Click here for information on Penn's salary structure]

== Research Assistant ==

The Computational Memory Laboratory in the Department of Psychology at the University of Pennsylvania is seeking to recruit a part-time Research Assistant to assist with federally funded studies of human memory processes and how these processes change across the adult lifespan. The project is aimed at using computational models to interpret behavioral and neural data both on healthy memory function in young adults and age-related impairments in memory performance. The successful candidate will join a team of research scientists studying the ways in which the brain stores and retrieves verbal and spatial memories and how these processes are affected by aging.

This would be an ideal position for someone interested in ultimately pursuing graduate training in cognitive neuroscience, medicine, psychology, or bioengineering.

Major responsibilities include carrying out experiments on human memory by means of high-density scalp EEG recordings; annotating vocal responses in memory tasks; assisting the research team in processing and analyzing these behavioral and electrophysiological data; supervising a team of undergraduate research assistants; and assisting in general lab administration (grants, progress reports, IRB protocols). This position requires an individual who possesses excellent interpersonal, organizational, and scientific skills. This individual must be able to work independently with limited oversight to maximize the amount of high-quality data collected.

To apply, submit a cover letter, unofficial transcripts, and a resume to memorylab@psych.upenn.edu

== Senior Data Analyst ([http://www.darpa.mil/program/restoring-active-memory DARPA RAM Project]) ==

The Computational Memory Lab is hiring a Senior Data Analyst. The selected applicant will lead the development of novel machine learning algorithms to decode cognitive states based upon multichannel intracranial time series data from human and nonhuman subjects. He/she will implement dimensionality reduction techniques and evaluate and implement large-scale data processing architectures (Hadoop, Hive, Spark, etc.) to manage the analysis of hundreds of terabytes of neural and behavioral data. He/she will interface with principal investigators and senior research staff at leading neuroscience institutions and medical device companies, and present results to project sponsors. The ideal candidate will possess exceptional statistical and programming skills, and the ability to communicate complex concepts through sophisticated data visualizations.

'''Required Qualifications'''

*PhD in computer science, statistics, engineering, neuroscience or directly related quantitative field, or MS with at least 4 years relevant post-graduate experience.
*Background in machine learning, regression modeling, feature discovery/selection, optimization, exploratory data analysis, data mining, pattern recognition.
*Experience with the development and implementation of novel machine learning techniques.
*Experience with Python, C/C++ and object-oriented programming techniques.
*Experience with scientific computing languages (Python, R, SAS, Matlab).

'''Preferred Qualifications'''

*Experience with neural time series analysis.
*Experience with large-scale data storage processing architectures (Hadoop, Hive, Spark, etc.).
*Experience with collaborative software development.
*Experience with Windows, Mac and Linux development environments.
*Good communication, interpersonal, and leadership skills.

[https://jobs.hr.upenn.edu/postings/12092 '''Apply online at https://jobs.hr.upenn.edu/postings/12092''']

== Senior Scientific Programmer ([http://www.darpa.mil/program/restoring-active-memory DARPA RAM Project]) ==

The Computational Memory Lab is hiring a Senior Scientific Programmer to lead the development of software tools and computational resources needed to develop a novel brain stimulation therapy for patients with memory impairment. This groundbreaking neuro-engineering project is part of President Obama’s BRAIN Initiative.

The selected applicant will lead the development of technical computing software, experimental programming libraries, cluster computing resources, and data transfer protocols. He/she will interface with senior research staff at multiple institutions and equipment vendors, and lead the development of a real-time system for closed-loop brain recording and stimulation, with high data acquisition and computational loads and low-latency requirements. He/she will manage the configuration of the closed-loop brain recording and stimulation system, including system updates and technical support to multiple clinical sites. Finally, he/she will lead the development and maintenance of systems to transfer experimental data from clinical sites to a centralized server. The ideal candidate will possess exceptional system development skills, past experience in mathematical programming, and the ability to develop and enhance a hybrid system implemented in multiple computer languages.

'''Required Qualifications'''

*Bachelor’s degree with at least 5 years relevant experience or Master’s degree with at least 3 years relevant experience
*Proficiency with C/C++ and Python
*Experience with scientific / statistical computing techniques and languages (MATLAB, SciPy, NumPy, etc.)
*Experience with Windows, Mac or Linux or Unix development environments

'''Preferred Qualifications'''

*PhD in computer science, neuroscience, bioengineering, mathematics or physics
*Experience with real-time computing and threading
*Experience working in a fast-paced collaborative software development setting

[http://jobs.hr.upenn.edu/postings/11057 '''Apply online at http://jobs.hr.upenn.edu/postings/11057.''']

== Scientific Software Developer ([http://www.darpa.mil/program/restoring-active-memory DARPA RAM Project]) ==

This position is responsible for developing and maintaining state-of-the-art tools to conduct human memory experiments and to develop new therapies to treat memory disorders. You will be responsible for the development and testing of experimental programming libraries, and data analysis of large neurophysiology data sets. You will integrate applications with other system components, create system and user-level documentation, and develop architectures to store and analyze large data sets. The position will be supervised by the project director and will interface extensively with project scientists, engineers and clinicians.

'''Required Qualifications'''

*Experience with Python, Matlab, or C/C++ required.
*Ability to implement, understand, and maintain mathematical and scientific codes.

'''Preferred Qualifications'''

*Master’s or PhD in mathematics, computer science, engineering, or other scientific field preferred.
*Experience with Big Data technologies, including Hadoop and Spark. SQL database programming.
*Developing or maintaining public software libraries.
*Identifying technical and algorithmic needs for research teams.
*Software engineering, including algorithms, design, data structures, and object-oriented techniques.

[http://jobs.hr.upenn.edu/postings/10538 '''Apply online at http://jobs.hr.upenn.edu/postings/10538.''']



For more information on our research, please click [[Research| here]].

Main Page

2015-10-05T21:20:42Z

Healeym: /* Computational models of human memory */

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[[File:CML_Logo.png|center|link=]]

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| width="110pt" | [[#Computational models of human memory|Computational models of human memory]]
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| width="110pt" | [[#Human spatial memory and cognition|Human spatial memory and cognition]]
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{| cellpadding="20" style="margin: 0 auto;"
| width="700pt" | The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients. Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1). Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the ''subsequent memory effect'' (see Fig. 3). Two of our ongoing, large-scale data collection projects are the [[Penn Electrophysiology of Encoding and Retrieval Study]] ([[PEERS]]), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found [http://memory.psych.upenn.edu/files/misc/ALL_ltpFR1_1-20_16-04-2015.pdf here]); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.
|
{| cellpadding="20" style="border: 1px solid darkgray;"
| width="100pt" style="background-color:#dddddd;"| [[File:fhm_cover.png|175px|link=Foundations of Human Memory]]
<big>[[Foundations of Human Memory]]</big> by [[Michael J. Kahana]] Please [[Foundations of Human Memory|click here]] for more information and errata.
|}
|}

== Computational models of human memory ==
To explain the processes underlying encoding, organization and retrieval of episodic memories, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed a class of retrieved-context models. These models assume that the input to the memory system itself produces contextual drift, and that the current state of context is used to retrieve items from memory. The temporal context model (TCM; [[Publications#HowaKaha02|Howard and Kahana, 2002]]) was introduced to explain recency and contiguity effects in free recall. Specifically, recency effects appear because the context at the time of the memory test is most similar to the context associated with recent items. When an item is retrieved at test, it reinstates the context active when that item was studied. Because this context overlaps with the encoding context of the items' neighbors, a contiguity effect results. Consistent with experimental data, TCM and its variants also predict that recency and contiguity effects are approximately time-scale invariant ([[Publications#SedeEtal08|Sederberg, Howard, and Kahana, 2008]]). [[Publications#PolyEtal09|Polyn, Norman, and Kahana (2009)]] developed the Context Maintenance and Retrieval model (CMR), which is a generalized version of TCM that accounts for the influence of non-temporal associations (e.g., semantic knowledge) on recall dynamics. MATLAB scripts to run the CMR model [[CMR|can be downloaded here]].

Despite the vast stores of memories we accumulate over a lifetime of experience, the human memory system is often able to target just the right information, seemingly effortlessly. How does the memory system accomplish this feat? Most previous models made the simplifying assumption that memory search is automatically restricted to a target list, largely bypassing the need to target the right information. [[Publications#LohnEtal14| Lohnas, Polyn, and Kahana (2015)]] developed CMR2 to address this issue. In CMR2, memory accumulates across multiple experimental lists, and temporal context is used both to focus retrieval on a target list and to censor retrieved information when its match to the current context indicates that it was learned in a non-target list. The model simultaneously accounts for a wide range of intralist and interlist phenomena, including the pattern of prior-list intrusions observed in free recall, build-up of and release from proactive interference, and the ability to selectively target retrieval of items on specific prior lists (Jang & Huber, 2008; Shiffrin, 1970). [[Publications#HealKaha15|Healey and Kahana (2015)]] used CMR2 to better understand why memory tends to get worse as we age. By fitting CMR2 to the performance of individual younger and older adults, they identified deficits in four critical processes: sustaining attention across a study episode, generating retrieval cues, resolving competition, and screening for inaccurate memories (intrusions). Healey and Kahana extended CMR2 to simulate a recognition memory task using the same mechanisms the free recall model uses to reject intrusions. Without fitting any additional parameters, the model accounts for age differences in recognition memory accuracy. Confirming a prediction of the model, free recall intrusion rates correlate positively with recognition false alarm rates. MATLAB scripts to run the CMR2 model [[Publications#LohnEtal14|can be downloaded here]].

{| style="margin: 0 auto;" cellpadding="20"
|[[File:cmr_neuro.png|none|thumb|400px|''Fig. 1:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR.]]
|[[File:cmr2_data.png|center|thumb|600px|''click to enlarge'' ''Fig. 2:'' '''Data and CMR2 Simulations.''' A. This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag. CMR2 captures older adult's temporal contiguity deficit. B. Release from proactive interference (PI). Participants studied lists of three items, each drawn from the same semantic category. After three lists from the same semantic category, subjects were then presented with a new set of three lists from a different semantic category. Top: Data from Loess (1967), note the scalloped pattern: performance declines across same-category lists and improves when a new category is presented. Bottom: CMR2 simulations capture the release from PI pattern. C. CMR2 simulations of hit and false alarm rates in recognition. D. As predicted by CMR2, recognition false alarm rates correlate with free recall intrusion rates. ]]
|}

== Neural oscillatory correlates of episodic memory ==

We investigate the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical surface of the brain or in the medial temporal lobes (including hippocampus) as part of the clinical process of localizing seizure foci. One focus of this research is to determine the oscillatory correlates of successful episodic memory formation and retrieval. Analyses of such recordings have shown that 65 - 95 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall ([[Publications#SedeEtal03|Sederberg et al., 2003]]; [[Publications#SedeEtal06|Sederberg et al., 2006]]; [[Publications#BurkEtal14|Burke et al., 2014]]; [[Publications#LongEtal14|Long et al., 2014]]; for a video, click [[HFA_Encoding| here]] ). Gamma activity in hippocampus and neocortex likewise increases prior to successful recall ([[Publications#SedeEtal07|Sederberg et al., 2007]]; [[Publications#LegaEtal11a|Lega et al., 2011]]; [[Publications#BurkEtal14|Burke et al., 2014]]; for a video, click [[HFA_Retrieval| here]] ). The movie below illustrates these findings.

{| style="margin: auto; width: 560px" cellpadding="20"; class="wikitable"
|<HTML5video width="560" height="315" autoplay="true" loop="true">FR</HTML5video>
 ''Fig. 3:'' '''Free Recall Paradigm''' By using a free recall task in which participants study a list of words, we can measure episodic memory formation by comparing the spectral correlates associated with encoding items that are later recalled (red) or forgotten (blue). We record electroencephalographic (EEG) signals from subdurally implanted electrodes in patients with medically intractable epilepsy. We can extract spectral signals (power of a given frequency) from the raw EEG voltage traces for each item and measure when and where power at particular frequencies changes. Successful memory formation is associated with increases in gamma band (65 - 95 Hz) activity in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses can be performed on items during recall to assess when and where memories are retrieved. Successful memory retrieval is associated with increases in gamma band activity in the left neocortex and hippocampus as well as increases in theta band (4 -8 Hz) activity in right temporal lobe. ''' 
|}

The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. In [[Publications#MannEtal11a|Manning et al., 2011]], we sought to determine whether contextual reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. We examined ECoG activity from 69 neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded just prior to each recall with those recorded after the patient had studied each word. We found that, upon recalling a studied word, the recorded patterns of brain activity were not only similar to the patterns observed when the word was studied, but were also similar to the patterns observed during study of neighboring list words, with similarity decreasing reliably with positional distance (Fig. 4C), just as predicted by context reinstatement models of free recall. The degree to which individual patients exhibited this neural signature of contextual reinstatement was correlated with the contiguity effect as seen in Fig. 4D. In this way, the study provides neural evidence for contextual reinstatement in humans.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:neuralContext.png|center|thumb|650px|''Fig. 4:'' '''Neural evidence for contextual reinstatement in humans.''' A. After studying a list of 20 words and performing a brief distraction task, a participant recalls as many words as he can remember, in any order. ECoG activity is recorded during each study and recall event. The similarity between the recorded patterns is computed as a function of lag. B. Each dot marks the location of a single electrode [temporal lobe (1,815 electrodes), frontal lobe (1,737 electrodes), parietal lobe (512 electrodes), and occipital lobe (138 electrodes)]. C. Similarity between the activity recorded during recall of a word from serial position i and study of a word from serial position i + lag. (Black dot denotes study and recall of the same word, i.e., lag = 0.) D. Participants exhibiting stronger neural signatures of context reinstatement also exhibited more pronounced contiguity effects (as measured by a percentile-based temporal clustering score). (Figure after Manning et al., 2011.)]]
|}

== Human spatial memory and cognition ==

[[File:MillerF2.png|thumb|225px|''Fig. 7:'' '''Place responsive cells.''' (a) Firing-rate maps for a cell responsive to northward traversals located in the hippocampus of one of the participants. (b) A cell responsive to eastward traversals recorded from a participant's entorhinal cortex. (c) Regional distribution of place-responsive cells across the entire data set of 371 single units (H, hippocampus; A, amygdala; EC, entorhinal cortex; PHG, parahippocampal gyrus; Ant, anterior medial temporal lobe).]]

Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click [http://memory.psych.upenn.edu/files/misc/yc2_movie.mov here]. Using this approach, we have documented the existence and character of the 4-8 Hz theta rhythm in the human brain as participants learned to navigate through complex virtual environments ([[Publications#KahaEtal99b|Kahana et al., 1999]]; [[Publications#CaplEtal01|Caplan et al., 2001]]; [[Publications#CaplEtal03|Caplan et al., 2003]]; [[Publications#EkstEtal05|Ekstrom et al., 2005]]; [[Publications#JacoEtal09|Jacobs et al., 2010a]]). Recording individual neurons during virtual navigation, we have discovered "place cells" in the human brain. These cells, which are found primarily in the human hippocampus, become active when a given spatial location is being traversed [[Publications#EkstEtal03|Ekstrom et al. (2003)]]. We also identified several other cellular responses during navigation: cells that become active in response to viewing a salient landmark (from any location), cells that become active when searching for a particular goal location (irrespective of location or view), and cells that respond when traveling in a given direction (bearing/heading).

[[Publications#JacoEtal10|Jacobs et al. (2010b)]] examined recordings of single-neuron activity from neurosurgical patients playing a virtual-navigation video game. In addition to place cells, which encode the current virtual location, we describe a unique cell type, entorhinal cortex (EC) path cells, the activity of which indicates whether the patient is taking a clockwise or counterclockwise path around the virtual square road. We find that many EC path cells exhibit this directional activity throughout the environment, in contrast to hippocampal neurons, which primarily encode information about specific locations. More broadly, these findings support the hypothesis that EC encodes general properties of the current context (e.g., location or direction) that are used by hippocampus to build unique representations reflecting combinations of these properties.

'''Grid cells''' in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, [[Publications#JacoEtal13|Jacobs et al. (2013)]] identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

Theories of episodic memory suggest that memory encoding and retrieval are facilitated by '''spatiotemporal context''': a continually updated representation of location in space and time. Recent work in the lab sought to examine the role of spatial context in human episodic memory retrieval through a hybrid spatial and episodic memory task. [[Publications#MillEtal13.pdf|Miller et al. (2013)]] identified place cells as neurosurgical patients performed a delivery task which required them to deliver items to different stores in a virtual town. Following this navigation task, patients were asked to freely recall the items they had delivered. Place cell activation patterns during the navigational task were then compared to the subsequent activation patterns that occurred during the free recall task. Miller et al. found that neural activity during the retrieval of each delivered item was similar to the neural activity associated with the location where that item was encoded. These findings demonstrate context-specific reinstatement of place-responsive cell activity at the time of recall, supporting theories that implicate contextual reinstatement as the basis for memory retrieval.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:MillerF3.png|center|thumb|800px|''Fig. 8:'' '''Spatial context reinstatement.''' (a) Time courses of neural similarity between ensemble place-cell activity during navigation and during item recall for near, middle, and far spatial distance bins. Shaded regions indicate SEM across recalled items, the horizontal bars indicate statistically significant time points and the vertical dotted line indicates the onset of vocalization. (b) Average neural similarity for near, middle and far distance bins for the time period of -300 to 700ms relative to recall onset. Error bars indicate SEM across recalled items. (c) Neural similarity for near and far spatial distance bins for each participants (colored lines) and the participant average (black line) during -300 to 700ms relative to recall onset. Error bars indicate SEM across participants.]]
|}







[[Category:public]]

Main Page

2015-10-05T21:00:13Z

Healeym: /* Computational models of human memory */

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[[File:CML_Logo.png|center|link=]]

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| width="110pt" | [[#Computational models of human memory|Computational models of human memory]]
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Ecog-thumb.png|frameless|left|border|150px|link=#Neural oscillatory correlates of episodic memory]]
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| width="110pt" | [[#Neural oscillatory correlates of episodic memory|Neural oscillatory correlates of episodic memory]]
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| [[File:MillerThumb.png|frameless|left|border|150px|link=#Human spatial memory and cognition]]
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| width="110pt" | [[#Human spatial memory and cognition|Human spatial memory and cognition]]
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{| cellpadding="20" style="margin: 0 auto;"
| width="700pt" | The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients. Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1). Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the ''subsequent memory effect'' (see Fig. 3). Two of our ongoing, large-scale data collection projects are the [[Penn Electrophysiology of Encoding and Retrieval Study]] ([[PEERS]]), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found [http://memory.psych.upenn.edu/files/misc/ALL_ltpFR1_1-20_16-04-2015.pdf here]); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.
|
{| cellpadding="20" style="border: 1px solid darkgray;"
| width="100pt" style="background-color:#dddddd;"| [[File:fhm_cover.png|175px|link=Foundations of Human Memory]]
<big>[[Foundations of Human Memory]]</big> by [[Michael J. Kahana]] Please [[Foundations of Human Memory|click here]] for more information and errata.
|}
|}

== Computational models of human memory ==
To explain the processes underlying encoding, organization and retrieval of episodic memories, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed a class of retrieved-context models. These models assume that the input to the memory system itself produces contextual drift, and that the current state of context is used to retrieve items from memory. The temporal context model (TCM; [[Publications#HowaKaha02|Howard and Kahana, 2002]]) was introduced to explain recency and contiguity effects in free recall. Specifically, recency effects appear because the context at the time of the memory test is most similar to the context associated with recent items. When an item is retrieved at test, it reinstates the context active when that item was studied. Because this context overlaps with the encoding context of the items' neighbors, a contiguity effect results. Consistent with experimental data, TCM and its variants also predict that recency and contiguity effects are approximately time-scale invariant ([[Publications#SedeEtal08|Sederberg, Howard, and Kahana, 2008]]). [[Publications#PolyEtal09|Polyn, Norman, and Kahana (2009)]] developed the Context Maintenance and Retrieval model (CMR), which is a generalized version of TCM that accounts for the influence of non-temporal associations (e.g., semantic knowledge) on recall dynamics. MATLAB scripts to run the CMR model [[CMR|can be downloaded here]].

Despite the vast stores of memories we accumulate over a lifetime of experience, the human memory system is often able to target just the right information, seemingly effortlessly. How does the memory system accomplish this feat? Most previous models made the simplifying assumption that memory search is automatically restricted to a target list, largely bypassing the need to target the right information. [[Publications#LohnEtal14| Lohnas, Polyn, and Kahana (2015)]] developed CMR2 to address this issue. In CMR2, memory accumulates across multiple experimental lists, and temporal context is used both to focus retrieval on a target list, and to censor retrieved information when its match to the current context indicates that it was learned in a non target list. The model simultaneously accounts for a wide range of intralist and internist phenomena, including the pattern of prior-list intrusions observed in free recall, build-up of and release from proactive interference, and the ability to selectively target retrieval of items on specific prior lists (Jang & Huber, 2008; Shiffrin, 1970). [[Publications#HealKaha15|Healey and Kahana (2015)]] used CMR2 to better understand why memory tends to get worse as we age. By fitting CMR2 to the performance of individual younger and older adults, they identified deficits in four critical processes: sustaining attention across a study episode, generating retrieval cues, resolving competition, and screening for inaccurate memories (intrusions). Healey and Kahana extend CMR2 to simulate a recognition memory task using the same mechanisms the free recall model uses to reject intrusions. Without fitting any additional parameters, the model accounts for age differences in recognition memory accuracy. Confirming a prediction of the model, free recall intrusion rates correlate positively with recognition false alarm rates. MATLAB scripts to run the CMR2 model [[Publications#LohnEtal14|can be downloaded here]].

{| style="margin: 0 auto;" cellpadding="20"
|[[File:cmr_neuro.png|none|thumb|400px|''Fig. 1:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR.]]
|[[File:cmr2_data.png|center|thumb|600px|''click to enlarge'' ''Fig. 2:'' '''Data and CMR2 Simulations.''' A. This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag. CMR2 captures older adult's temporal contiguity deficit. B. Release from proactive interference (PI). Participants studied lists of three items, each drawn from the same semantic category. After three lists from the same semantic category, subjects were then presented with a new set of three lists from a different semantic category. Top: Data from Loess (1967), note the scalloped pattern: performance declines across same-category lists and improves when a new category is presented. Bottom: CMR2 simulations capture the release from PI pattern. C. CMR2 simulations of hit and false alarm rates in recognition. D. As predicted by CMR2, recognition false alarm rates correlate with free recall intrusion rates. ]]
|}

== Neural oscillatory correlates of episodic memory ==

We investigate the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical surface of the brain or in the medial temporal lobes (including hippocampus) as part of the clinical process of localizing seizure foci. One focus of this research is to determine the oscillatory correlates of successful episodic memory formation and retrieval. Analyses of such recordings have shown that 65 - 95 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall ([[Publications#SedeEtal03|Sederberg et al., 2003]]; [[Publications#SedeEtal06|Sederberg et al., 2006]]; [[Publications#BurkEtal14|Burke et al., 2014]]; [[Publications#LongEtal14|Long et al., 2014]]; for a video, click [[HFA_Encoding| here]] ). Gamma activity in hippocampus and neocortex likewise increases prior to successful recall ([[Publications#SedeEtal07|Sederberg et al., 2007]]; [[Publications#LegaEtal11a|Lega et al., 2011]]; [[Publications#BurkEtal14|Burke et al., 2014]]; for a video, click [[HFA_Retrieval| here]] ). The movie below illustrates these findings.

{| style="margin: auto; width: 560px" cellpadding="20"; class="wikitable"
|<HTML5video width="560" height="315" autoplay="true" loop="true">FR</HTML5video>
 ''Fig. 3:'' '''Free Recall Paradigm''' By using a free recall task in which participants study a list of words, we can measure episodic memory formation by comparing the spectral correlates associated with encoding items that are later recalled (red) or forgotten (blue). We record electroencephalographic (EEG) signals from subdurally implanted electrodes in patients with medically intractable epilepsy. We can extract spectral signals (power of a given frequency) from the raw EEG voltage traces for each item and measure when and where power at particular frequencies changes. Successful memory formation is associated with increases in gamma band (65 - 95 Hz) activity in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses can be performed on items during recall to assess when and where memories are retrieved. Successful memory retrieval is associated with increases in gamma band activity in the left neocortex and hippocampus as well as increases in theta band (4 -8 Hz) activity in right temporal lobe. ''' 
|}

The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. In [[Publications#MannEtal11a|Manning et al., 2011]], we sought to determine whether contextual reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. We examined ECoG activity from 69 neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded just prior to each recall with those recorded after the patient had studied each word. We found that, upon recalling a studied word, the recorded patterns of brain activity were not only similar to the patterns observed when the word was studied, but were also similar to the patterns observed during study of neighboring list words, with similarity decreasing reliably with positional distance (Fig. 4C), just as predicted by context reinstatement models of free recall. The degree to which individual patients exhibited this neural signature of contextual reinstatement was correlated with the contiguity effect as seen in Fig. 4D. In this way, the study provides neural evidence for contextual reinstatement in humans.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:neuralContext.png|center|thumb|650px|''Fig. 4:'' '''Neural evidence for contextual reinstatement in humans.''' A. After studying a list of 20 words and performing a brief distraction task, a participant recalls as many words as he can remember, in any order. ECoG activity is recorded during each study and recall event. The similarity between the recorded patterns is computed as a function of lag. B. Each dot marks the location of a single electrode [temporal lobe (1,815 electrodes), frontal lobe (1,737 electrodes), parietal lobe (512 electrodes), and occipital lobe (138 electrodes)]. C. Similarity between the activity recorded during recall of a word from serial position i and study of a word from serial position i + lag. (Black dot denotes study and recall of the same word, i.e., lag = 0.) D. Participants exhibiting stronger neural signatures of context reinstatement also exhibited more pronounced contiguity effects (as measured by a percentile-based temporal clustering score). (Figure after Manning et al., 2011.)]]
|}

== Human spatial memory and cognition ==

[[File:MillerF2.png|thumb|225px|''Fig. 7:'' '''Place responsive cells.''' (a) Firing-rate maps for a cell responsive to northward traversals located in the hippocampus of one of the participants. (b) A cell responsive to eastward traversals recorded from a participant's entorhinal cortex. (c) Regional distribution of place-responsive cells across the entire data set of 371 single units (H, hippocampus; A, amygdala; EC, entorhinal cortex; PHG, parahippocampal gyrus; Ant, anterior medial temporal lobe).]]

Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click [http://memory.psych.upenn.edu/files/misc/yc2_movie.mov here]. Using this approach, we have documented the existence and character of the 4-8 Hz theta rhythm in the human brain as participants learned to navigate through complex virtual environments ([[Publications#KahaEtal99b|Kahana et al., 1999]]; [[Publications#CaplEtal01|Caplan et al., 2001]]; [[Publications#CaplEtal03|Caplan et al., 2003]]; [[Publications#EkstEtal05|Ekstrom et al., 2005]]; [[Publications#JacoEtal09|Jacobs et al., 2010a]]). Recording individual neurons during virtual navigation, we have discovered "place cells" in the human brain. These cells, which are found primarily in the human hippocampus, become active when a given spatial location is being traversed [[Publications#EkstEtal03|Ekstrom et al. (2003)]]. We also identified several other cellular responses during navigation: cells that become active in response to viewing a salient landmark (from any location), cells that become active when searching for a particular goal location (irrespective of location or view), and cells that respond when traveling in a given direction (bearing/heading).

[[Publications#JacoEtal10|Jacobs et al. (2010b)]] examined recordings of single-neuron activity from neurosurgical patients playing a virtual-navigation video game. In addition to place cells, which encode the current virtual location, we describe a unique cell type, entorhinal cortex (EC) path cells, the activity of which indicates whether the patient is taking a clockwise or counterclockwise path around the virtual square road. We find that many EC path cells exhibit this directional activity throughout the environment, in contrast to hippocampal neurons, which primarily encode information about specific locations. More broadly, these findings support the hypothesis that EC encodes general properties of the current context (e.g., location or direction) that are used by hippocampus to build unique representations reflecting combinations of these properties.

'''Grid cells''' in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, [[Publications#JacoEtal13|Jacobs et al. (2013)]] identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

Theories of episodic memory suggest that memory encoding and retrieval are facilitated by '''spatiotemporal context''': a continually updated representation of location in space and time. Recent work in the lab sought to examine the role of spatial context in human episodic memory retrieval through a hybrid spatial and episodic memory task. [[Publications#MillEtal13.pdf|Miller et al. (2013)]] identified place cells as neurosurgical patients performed a delivery task which required them to deliver items to different stores in a virtual town. Following this navigation task, patients were asked to freely recall the items they had delivered. Place cell activation patterns during the navigational task were then compared to the subsequent activation patterns that occurred during the free recall task. Miller et al. found that neural activity during the retrieval of each delivered item was similar to the neural activity associated with the location where that item was encoded. These findings demonstrate context-specific reinstatement of place-responsive cell activity at the time of recall, supporting theories that implicate contextual reinstatement as the basis for memory retrieval.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:MillerF3.png|center|thumb|800px|''Fig. 8:'' '''Spatial context reinstatement.''' (a) Time courses of neural similarity between ensemble place-cell activity during navigation and during item recall for near, middle, and far spatial distance bins. Shaded regions indicate SEM across recalled items, the horizontal bars indicate statistically significant time points and the vertical dotted line indicates the onset of vocalization. (b) Average neural similarity for near, middle and far distance bins for the time period of -300 to 700ms relative to recall onset. Error bars indicate SEM across recalled items. (c) Neural similarity for near and far spatial distance bins for each participants (colored lines) and the participant average (black line) during -300 to 700ms relative to recall onset. Error bars indicate SEM across participants.]]
|}







[[Category:public]]

Main Page

2015-10-05T20:59:32Z

Healeym: /* Computational models of human memory */

__NOTITLE__
__NOTOC__
[[File:CML_Logo.png|center|link=]]

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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Models-thumb.png|frameless|left|border|150px|link=#Computational models of human memory]]
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| width="110pt" | [[#Computational models of human memory|Computational models of human memory]]
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Ecog-thumb.png|frameless|left|border|150px|link=#Neural oscillatory correlates of episodic memory]]
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| width="110pt" | [[#Neural oscillatory correlates of episodic memory|Neural oscillatory correlates of episodic memory]]
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:MillerThumb.png|frameless|left|border|150px|link=#Human spatial memory and cognition]]
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| width="110pt" | [[#Human spatial memory and cognition|Human spatial memory and cognition]]
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{| cellpadding="20" style="margin: 0 auto;"
| width="700pt" | The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients. Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1). Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the ''subsequent memory effect'' (see Fig. 3). Two of our ongoing, large-scale data collection projects are the [[Penn Electrophysiology of Encoding and Retrieval Study]] ([[PEERS]]), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found [http://memory.psych.upenn.edu/files/misc/ALL_ltpFR1_1-20_16-04-2015.pdf here]); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.
|
{| cellpadding="20" style="border: 1px solid darkgray;"
| width="100pt" style="background-color:#dddddd;"| [[File:fhm_cover.png|175px|link=Foundations of Human Memory]]
<big>[[Foundations of Human Memory]]</big> by [[Michael J. Kahana]] Please [[Foundations of Human Memory|click here]] for more information and errata.
|}
|}

== Computational models of human memory ==
To explain the processes underlying encoding, organization and retrieval of episodic memories, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed a class of retrieved-context models. These models assume that the input to the memory system itself produces contextual drift, and that the current state of context is used to retrieve items from memory. The temporal context model (TCM; [[Publications#HowaKaha02|Howard and Kahana, 2002]]) was introduced to explain recency and contiguity effects in free recall. Specifically, recency effects appear because the context at the time of the memory test is most similar to the context associated with recent items. When an item is retrieved at test, it reinstates the context active when that item was studied. Because this context overlaps with the encoding context of the items' neighbors, a contiguity effect results. Consistent with experimental data, TCM and its variants also predict that recency and contiguity effects are approximately time-scale invariant ([[Publications#SedeEtal08|Sederberg, Howard, and Kahana, 2008]]). [[Publications#PolyEtal09|Polyn, Norman, and Kahana (2009)]] developed the Context Maintenance and Retrieval model (CMR), which is a generalized version of TCM that accounts for the influence of non-temporal associations (e.g., semantic knowledge) on recall dynamics. MATLAB scripts to run the CMR model [[CMR|can be downloaded here]].

Despite the vast stores of memories we accumulate over a lifetime of experience, the human memory system is often able to target just the right information, seemingly effortlessly. How does the memory system accomplish this feat? Most previous models made the simplifying assumption that memory search is automatically restricted to a target list, largely bypassing the need to target the right information. [[Publications#LohnEtal14| Lohnas, Polyn, and Kahana (2015)]] developed CMR2 to address this issue. In CMR2, memory accumulates across multiple experimental lists, and temporal context is used both to focus retrieval on a target list, and to censor retrieved information when its match to the current context indicates that it was learned in a non target list. The model simultaneously accounts for a wide range of intralist and internist phenomena, including the pattern of prior-list intrusions observed in free recall, build-up of and release from proactive interference, and the ability to selectively target retrieval of items on specific prior lists (Jang & Huber, 2008; Shiffrin, 1970). [[Publications#HealKaha15|Healey and Kahana (2015)]] used CMR2 to better understand why memory tends to get worse as we age. By fitting CMR2 to the performance of individual younger and older adults, they identified deficits in four critical processes: sustaining attention across a study episode, generating retrieval cues, resolving competition, and screening for inaccurate memories (intrusions). Healey and Kahana extend CMR2 to simulate a recognition memory task using the same mechanisms the free recall model uses to reject intrusions. Without fitting any additional parameters, the model accounts for age differences in recognition memory accuracy. Confirming a prediction of the model, free recall intrusion rates correlate positively with recognition false alarm rates. MATLAB scripts to run the CMR2 model [[Publications#LohnEtal14|can be downloaded here]].

{| style="margin: 0 auto;" cellpadding="20"
|[[File:cmr_neuro.png|none|thumb|400px|''Fig. 1:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR.]]
|[[File:cmr2_data.png|center|thumb|600px|''click to enlarge'' ''Fig. 2:'' '''Data and CMR2 Simulations.''' A. This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag. CMR2 captures older adult's temporal contiguity deficit. B. Release from proactive interference (PI). Participants studied lists of three items, each drawn from the same semantic category. After three lists from the same semantic category, subjects were then presented with a new set of three lists from a different semantic category. Top: Data from Loess (1967), note the scalloped pattern: performance declines across same-category lists and improves when a new category is presented. Bottom: CMR2 simulations capture the release from PI pattern. C. CMR2 simulations of hit and false alarm rates in recognition. B. As predicted by CMR2, recognition false alarm rates correlate with free recall intrusion rates. ]]
|}

== Neural oscillatory correlates of episodic memory ==

We investigate the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical surface of the brain or in the medial temporal lobes (including hippocampus) as part of the clinical process of localizing seizure foci. One focus of this research is to determine the oscillatory correlates of successful episodic memory formation and retrieval. Analyses of such recordings have shown that 65 - 95 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall ([[Publications#SedeEtal03|Sederberg et al., 2003]]; [[Publications#SedeEtal06|Sederberg et al., 2006]]; [[Publications#BurkEtal14|Burke et al., 2014]]; [[Publications#LongEtal14|Long et al., 2014]]; for a video, click [[HFA_Encoding| here]] ). Gamma activity in hippocampus and neocortex likewise increases prior to successful recall ([[Publications#SedeEtal07|Sederberg et al., 2007]]; [[Publications#LegaEtal11a|Lega et al., 2011]]; [[Publications#BurkEtal14|Burke et al., 2014]]; for a video, click [[HFA_Retrieval| here]] ). The movie below illustrates these findings.

{| style="margin: auto; width: 560px" cellpadding="20"; class="wikitable"
|<HTML5video width="560" height="315" autoplay="true" loop="true">FR</HTML5video>
 ''Fig. 3:'' '''Free Recall Paradigm''' By using a free recall task in which participants study a list of words, we can measure episodic memory formation by comparing the spectral correlates associated with encoding items that are later recalled (red) or forgotten (blue). We record electroencephalographic (EEG) signals from subdurally implanted electrodes in patients with medically intractable epilepsy. We can extract spectral signals (power of a given frequency) from the raw EEG voltage traces for each item and measure when and where power at particular frequencies changes. Successful memory formation is associated with increases in gamma band (65 - 95 Hz) activity in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses can be performed on items during recall to assess when and where memories are retrieved. Successful memory retrieval is associated with increases in gamma band activity in the left neocortex and hippocampus as well as increases in theta band (4 -8 Hz) activity in right temporal lobe. ''' 
|}

The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. In [[Publications#MannEtal11a|Manning et al., 2011]], we sought to determine whether contextual reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. We examined ECoG activity from 69 neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded just prior to each recall with those recorded after the patient had studied each word. We found that, upon recalling a studied word, the recorded patterns of brain activity were not only similar to the patterns observed when the word was studied, but were also similar to the patterns observed during study of neighboring list words, with similarity decreasing reliably with positional distance (Fig. 4C), just as predicted by context reinstatement models of free recall. The degree to which individual patients exhibited this neural signature of contextual reinstatement was correlated with the contiguity effect as seen in Fig. 4D. In this way, the study provides neural evidence for contextual reinstatement in humans.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:neuralContext.png|center|thumb|650px|''Fig. 4:'' '''Neural evidence for contextual reinstatement in humans.''' A. After studying a list of 20 words and performing a brief distraction task, a participant recalls as many words as he can remember, in any order. ECoG activity is recorded during each study and recall event. The similarity between the recorded patterns is computed as a function of lag. B. Each dot marks the location of a single electrode [temporal lobe (1,815 electrodes), frontal lobe (1,737 electrodes), parietal lobe (512 electrodes), and occipital lobe (138 electrodes)]. C. Similarity between the activity recorded during recall of a word from serial position i and study of a word from serial position i + lag. (Black dot denotes study and recall of the same word, i.e., lag = 0.) D. Participants exhibiting stronger neural signatures of context reinstatement also exhibited more pronounced contiguity effects (as measured by a percentile-based temporal clustering score). (Figure after Manning et al., 2011.)]]
|}

== Human spatial memory and cognition ==

[[File:MillerF2.png|thumb|225px|''Fig. 7:'' '''Place responsive cells.''' (a) Firing-rate maps for a cell responsive to northward traversals located in the hippocampus of one of the participants. (b) A cell responsive to eastward traversals recorded from a participant's entorhinal cortex. (c) Regional distribution of place-responsive cells across the entire data set of 371 single units (H, hippocampus; A, amygdala; EC, entorhinal cortex; PHG, parahippocampal gyrus; Ant, anterior medial temporal lobe).]]

Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click [http://memory.psych.upenn.edu/files/misc/yc2_movie.mov here]. Using this approach, we have documented the existence and character of the 4-8 Hz theta rhythm in the human brain as participants learned to navigate through complex virtual environments ([[Publications#KahaEtal99b|Kahana et al., 1999]]; [[Publications#CaplEtal01|Caplan et al., 2001]]; [[Publications#CaplEtal03|Caplan et al., 2003]]; [[Publications#EkstEtal05|Ekstrom et al., 2005]]; [[Publications#JacoEtal09|Jacobs et al., 2010a]]). Recording individual neurons during virtual navigation, we have discovered "place cells" in the human brain. These cells, which are found primarily in the human hippocampus, become active when a given spatial location is being traversed [[Publications#EkstEtal03|Ekstrom et al. (2003)]]. We also identified several other cellular responses during navigation: cells that become active in response to viewing a salient landmark (from any location), cells that become active when searching for a particular goal location (irrespective of location or view), and cells that respond when traveling in a given direction (bearing/heading).

[[Publications#JacoEtal10|Jacobs et al. (2010b)]] examined recordings of single-neuron activity from neurosurgical patients playing a virtual-navigation video game. In addition to place cells, which encode the current virtual location, we describe a unique cell type, entorhinal cortex (EC) path cells, the activity of which indicates whether the patient is taking a clockwise or counterclockwise path around the virtual square road. We find that many EC path cells exhibit this directional activity throughout the environment, in contrast to hippocampal neurons, which primarily encode information about specific locations. More broadly, these findings support the hypothesis that EC encodes general properties of the current context (e.g., location or direction) that are used by hippocampus to build unique representations reflecting combinations of these properties.

'''Grid cells''' in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, [[Publications#JacoEtal13|Jacobs et al. (2013)]] identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

Theories of episodic memory suggest that memory encoding and retrieval are facilitated by '''spatiotemporal context''': a continually updated representation of location in space and time. Recent work in the lab sought to examine the role of spatial context in human episodic memory retrieval through a hybrid spatial and episodic memory task. [[Publications#MillEtal13.pdf|Miller et al. (2013)]] identified place cells as neurosurgical patients performed a delivery task which required them to deliver items to different stores in a virtual town. Following this navigation task, patients were asked to freely recall the items they had delivered. Place cell activation patterns during the navigational task were then compared to the subsequent activation patterns that occurred during the free recall task. Miller et al. found that neural activity during the retrieval of each delivered item was similar to the neural activity associated with the location where that item was encoded. These findings demonstrate context-specific reinstatement of place-responsive cell activity at the time of recall, supporting theories that implicate contextual reinstatement as the basis for memory retrieval.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:MillerF3.png|center|thumb|800px|''Fig. 8:'' '''Spatial context reinstatement.''' (a) Time courses of neural similarity between ensemble place-cell activity during navigation and during item recall for near, middle, and far spatial distance bins. Shaded regions indicate SEM across recalled items, the horizontal bars indicate statistically significant time points and the vertical dotted line indicates the onset of vocalization. (b) Average neural similarity for near, middle and far distance bins for the time period of -300 to 700ms relative to recall onset. Error bars indicate SEM across recalled items. (c) Neural similarity for near and far spatial distance bins for each participants (colored lines) and the participant average (black line) during -300 to 700ms relative to recall onset. Error bars indicate SEM across participants.]]
|}







[[Category:public]]

Main Page

2015-10-05T20:56:05Z

Healeym: /* Computational models of human memory */

__NOTITLE__
__NOTOC__
[[File:CML_Logo.png|center|link=]]

{| border="0" cellpadding="10" style="margin: 0 auto;"
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Models-thumb.png|frameless|left|border|150px|link=#Computational models of human memory]]
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| width="110pt" | [[#Computational models of human memory|Computational models of human memory]]
|}
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Ecog-thumb.png|frameless|left|border|150px|link=#Neural oscillatory correlates of episodic memory]]
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| width="110pt" | [[#Neural oscillatory correlates of episodic memory|Neural oscillatory correlates of episodic memory]]
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:MillerThumb.png|frameless|left|border|150px|link=#Human spatial memory and cognition]]
|-
| width="110pt" | [[#Human spatial memory and cognition|Human spatial memory and cognition]]
|}

|}

{| cellpadding="20" style="margin: 0 auto;"
| width="700pt" | The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients. Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1). Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the ''subsequent memory effect'' (see Fig. 3). Two of our ongoing, large-scale data collection projects are the [[Penn Electrophysiology of Encoding and Retrieval Study]] ([[PEERS]]), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found [http://memory.psych.upenn.edu/files/misc/ALL_ltpFR1_1-20_16-04-2015.pdf here]); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.
|
{| cellpadding="20" style="border: 1px solid darkgray;"
| width="100pt" style="background-color:#dddddd;"| [[File:fhm_cover.png|175px|link=Foundations of Human Memory]]
<big>[[Foundations of Human Memory]]</big> by [[Michael J. Kahana]] Please [[Foundations of Human Memory|click here]] for more information and errata.
|}
|}

== Computational models of human memory ==
To explain the processes underlying encoding, organization and retrieval of episodic memories, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed a class of retrieved-context models. These models assume that the input to the memory system itself produces contextual drift, and that the current state of context is used to retrieve items from memory. The temporal context model (TCM; [[Publications#HowaKaha02|Howard and Kahana, 2002]]) was introduced to explain recency and contiguity effects in free recall. Specifically, recency effects appear because the context at the time of the memory test is most similar to the context associated with recent items. When an item is retrieved at test, it reinstates the context active when that item was studied. Because this context overlaps with the encoding context of the items' neighbors, a contiguity effect results. Consistent with experimental data, TCM and its variants also predict that recency and contiguity effects are approximately time-scale invariant ([[Publications#SedeEtal08|Sederberg, Howard, and Kahana, 2008]]). [[Publications#PolyEtal09|Polyn, Norman, and Kahana (2009)]] developed the Context Maintenance and Retrieval model (CMR), which is a generalized version of TCM that accounts for the influence of non-temporal associations (e.g., semantic knowledge) on recall dynamics. MATLAB scripts to run the CMR model [[CMR|can be downloaded here]].

Despite the vast stores of memories we accumulate over a lifetime of experience, the human memory system is often able to target just the right information, seemingly effortlessly. How does the memory system accomplish this feat? Most previous models made the simplifying assumption that memory search is automatically restricted to a target list, largely bypassing the need to target the right information. [[Publications#LohnEtal14| Lohnas, Polyn, and Kahana (2015)]] developed CMR2 to address this issue. In CMR2, memory accumulates across multiple experimental lists, and temporal context is used both to focus retrieval on a target list, and to censor retrieved information when its match to the current context indicates that it was learned in a non target list. The model simultaneously accounts for a wide range of intralist and internist phenomena, including the pattern of prior-list intrusions observed in free recall, build-up of and release from proactive interference, and the ability to selectively target retrieval of items on specific prior lists (Jang & Huber, 2008; Shiffrin, 1970). [[Publications#HealKaha15|Healey and Kahana (2015)]] used CMR2 to better understand why memory tends to get worse as we age. By fitting CMR2 to the performance of individual younger and older adults, they identified deficits in four critical processes: sustaining attention across a study episode, generating retrieval cues, resolving competition, and screening for inaccurate memories (intrusions). Healey and Kahana extend CMR2 to simulate a recognition memory task using the same mechanisms the free recall model uses to reject intrusions. Without fitting any additional parameters, the model accounts for age differences in recognition memory accuracy. Confirming a prediction of the model, free recall intrusion rates correlate positively with recognition false alarm rates. MATLAB scripts to run the CMR2 model [[Publications#LohnEtal14|can be downloaded here]].

{| style="margin: 0 auto;" cellpadding="20"
|[[File:cmr_neuro.png|none|thumb|200px|''Fig. 1:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR.]]
|[[File:cmr2_data.png|center|thumb|400px|''click to enlarge'' ''Fig. 2:'' '''Data and CMR2 Simulations.''' A. This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag. CMR2 captures older adult's temporal contiguity deficit. B. Release from proactive interference (PI) in lists of categorized words. Participants studied lists of three items, each drawn from the same semantic category (sample words were used in the original study). After three lists from the same semantic category, subjects were then presented with a new set of three lists from a different semantic category. Top: Data from Loess (1967), note the scalloped pattern: performance declines across same-category lists and improves when a new category is pretend. Bottom: CMR2 simulations capture the release from PI pattern. C. CMR2 simulations of hit and false alarm rates in recognition. B. As predicted by CMR2, recognition false alarm rates correlate with free recall intrusion rate. ]]
|}

{| style="margin: 0 auto;" cellpadding="20"
|[[File:crp2a_square.jpg|none|thumb|200px|''Fig. 1:'' '''The contiguity effect in free recall.''' This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag.]]
|[[File:cmr.png|center|thumb|400px|''click to enlarge'' ''Fig. 2:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR. C. IRTs as a function of output position and total number of recalled items (4, 5, 6 or 7). D. Serial position curves for list lengths (LL) of 20, 30 and 40 items.]]
|}

== Neural oscillatory correlates of episodic memory ==

We investigate the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical surface of the brain or in the medial temporal lobes (including hippocampus) as part of the clinical process of localizing seizure foci. One focus of this research is to determine the oscillatory correlates of successful episodic memory formation and retrieval. Analyses of such recordings have shown that 65 - 95 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall ([[Publications#SedeEtal03|Sederberg et al., 2003]]; [[Publications#SedeEtal06|Sederberg et al., 2006]]; [[Publications#BurkEtal14|Burke et al., 2014]]; [[Publications#LongEtal14|Long et al., 2014]]; for a video, click [[HFA_Encoding| here]] ). Gamma activity in hippocampus and neocortex likewise increases prior to successful recall ([[Publications#SedeEtal07|Sederberg et al., 2007]]; [[Publications#LegaEtal11a|Lega et al., 2011]]; [[Publications#BurkEtal14|Burke et al., 2014]]; for a video, click [[HFA_Retrieval| here]] ). The movie below illustrates these findings.

{| style="margin: auto; width: 560px" cellpadding="20"; class="wikitable"
|<HTML5video width="560" height="315" autoplay="true" loop="true">FR</HTML5video>
 ''Fig. 3:'' '''Free Recall Paradigm''' By using a free recall task in which participants study a list of words, we can measure episodic memory formation by comparing the spectral correlates associated with encoding items that are later recalled (red) or forgotten (blue). We record electroencephalographic (EEG) signals from subdurally implanted electrodes in patients with medically intractable epilepsy. We can extract spectral signals (power of a given frequency) from the raw EEG voltage traces for each item and measure when and where power at particular frequencies changes. Successful memory formation is associated with increases in gamma band (65 - 95 Hz) activity in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses can be performed on items during recall to assess when and where memories are retrieved. Successful memory retrieval is associated with increases in gamma band activity in the left neocortex and hippocampus as well as increases in theta band (4 -8 Hz) activity in right temporal lobe. ''' 
|}

The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. In [[Publications#MannEtal11a|Manning et al., 2011]], we sought to determine whether contextual reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. We examined ECoG activity from 69 neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded just prior to each recall with those recorded after the patient had studied each word. We found that, upon recalling a studied word, the recorded patterns of brain activity were not only similar to the patterns observed when the word was studied, but were also similar to the patterns observed during study of neighboring list words, with similarity decreasing reliably with positional distance (Fig. 4C), just as predicted by context reinstatement models of free recall. The degree to which individual patients exhibited this neural signature of contextual reinstatement was correlated with the contiguity effect as seen in Fig. 4D. In this way, the study provides neural evidence for contextual reinstatement in humans.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:neuralContext.png|center|thumb|650px|''Fig. 4:'' '''Neural evidence for contextual reinstatement in humans.''' A. After studying a list of 20 words and performing a brief distraction task, a participant recalls as many words as he can remember, in any order. ECoG activity is recorded during each study and recall event. The similarity between the recorded patterns is computed as a function of lag. B. Each dot marks the location of a single electrode [temporal lobe (1,815 electrodes), frontal lobe (1,737 electrodes), parietal lobe (512 electrodes), and occipital lobe (138 electrodes)]. C. Similarity between the activity recorded during recall of a word from serial position i and study of a word from serial position i + lag. (Black dot denotes study and recall of the same word, i.e., lag = 0.) D. Participants exhibiting stronger neural signatures of context reinstatement also exhibited more pronounced contiguity effects (as measured by a percentile-based temporal clustering score). (Figure after Manning et al., 2011.)]]
|}

== Human spatial memory and cognition ==

[[File:MillerF2.png|thumb|225px|''Fig. 7:'' '''Place responsive cells.''' (a) Firing-rate maps for a cell responsive to northward traversals located in the hippocampus of one of the participants. (b) A cell responsive to eastward traversals recorded from a participant's entorhinal cortex. (c) Regional distribution of place-responsive cells across the entire data set of 371 single units (H, hippocampus; A, amygdala; EC, entorhinal cortex; PHG, parahippocampal gyrus; Ant, anterior medial temporal lobe).]]

Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click [http://memory.psych.upenn.edu/files/misc/yc2_movie.mov here]. Using this approach, we have documented the existence and character of the 4-8 Hz theta rhythm in the human brain as participants learned to navigate through complex virtual environments ([[Publications#KahaEtal99b|Kahana et al., 1999]]; [[Publications#CaplEtal01|Caplan et al., 2001]]; [[Publications#CaplEtal03|Caplan et al., 2003]]; [[Publications#EkstEtal05|Ekstrom et al., 2005]]; [[Publications#JacoEtal09|Jacobs et al., 2010a]]). Recording individual neurons during virtual navigation, we have discovered "place cells" in the human brain. These cells, which are found primarily in the human hippocampus, become active when a given spatial location is being traversed [[Publications#EkstEtal03|Ekstrom et al. (2003)]]. We also identified several other cellular responses during navigation: cells that become active in response to viewing a salient landmark (from any location), cells that become active when searching for a particular goal location (irrespective of location or view), and cells that respond when traveling in a given direction (bearing/heading).

[[Publications#JacoEtal10|Jacobs et al. (2010b)]] examined recordings of single-neuron activity from neurosurgical patients playing a virtual-navigation video game. In addition to place cells, which encode the current virtual location, we describe a unique cell type, entorhinal cortex (EC) path cells, the activity of which indicates whether the patient is taking a clockwise or counterclockwise path around the virtual square road. We find that many EC path cells exhibit this directional activity throughout the environment, in contrast to hippocampal neurons, which primarily encode information about specific locations. More broadly, these findings support the hypothesis that EC encodes general properties of the current context (e.g., location or direction) that are used by hippocampus to build unique representations reflecting combinations of these properties.

'''Grid cells''' in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, [[Publications#JacoEtal13|Jacobs et al. (2013)]] identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

Theories of episodic memory suggest that memory encoding and retrieval are facilitated by '''spatiotemporal context''': a continually updated representation of location in space and time. Recent work in the lab sought to examine the role of spatial context in human episodic memory retrieval through a hybrid spatial and episodic memory task. [[Publications#MillEtal13.pdf|Miller et al. (2013)]] identified place cells as neurosurgical patients performed a delivery task which required them to deliver items to different stores in a virtual town. Following this navigation task, patients were asked to freely recall the items they had delivered. Place cell activation patterns during the navigational task were then compared to the subsequent activation patterns that occurred during the free recall task. Miller et al. found that neural activity during the retrieval of each delivered item was similar to the neural activity associated with the location where that item was encoded. These findings demonstrate context-specific reinstatement of place-responsive cell activity at the time of recall, supporting theories that implicate contextual reinstatement as the basis for memory retrieval.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:MillerF3.png|center|thumb|800px|''Fig. 8:'' '''Spatial context reinstatement.''' (a) Time courses of neural similarity between ensemble place-cell activity during navigation and during item recall for near, middle, and far spatial distance bins. Shaded regions indicate SEM across recalled items, the horizontal bars indicate statistically significant time points and the vertical dotted line indicates the onset of vocalization. (b) Average neural similarity for near, middle and far distance bins for the time period of -300 to 700ms relative to recall onset. Error bars indicate SEM across recalled items. (c) Neural similarity for near and far spatial distance bins for each participants (colored lines) and the participant average (black line) during -300 to 700ms relative to recall onset. Error bars indicate SEM across participants.]]
|}







[[Category:public]]

File:Cmr2 data.png

2015-10-05T20:53:25Z

Healeym:

Main Page

2015-10-05T20:49:35Z

Healeym: /* Computational models of human memory */

__NOTITLE__
__NOTOC__
[[File:CML_Logo.png|center|link=]]

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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Models-thumb.png|frameless|left|border|150px|link=#Computational models of human memory]]
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| width="110pt" | [[#Computational models of human memory|Computational models of human memory]]
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Ecog-thumb.png|frameless|left|border|150px|link=#Neural oscillatory correlates of episodic memory]]
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| width="110pt" | [[#Neural oscillatory correlates of episodic memory|Neural oscillatory correlates of episodic memory]]
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| [[File:MillerThumb.png|frameless|left|border|150px|link=#Human spatial memory and cognition]]
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| width="110pt" | [[#Human spatial memory and cognition|Human spatial memory and cognition]]
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{| cellpadding="20" style="margin: 0 auto;"
| width="700pt" | The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients. Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1). Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the ''subsequent memory effect'' (see Fig. 3). Two of our ongoing, large-scale data collection projects are the [[Penn Electrophysiology of Encoding and Retrieval Study]] ([[PEERS]]), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found [http://memory.psych.upenn.edu/files/misc/ALL_ltpFR1_1-20_16-04-2015.pdf here]); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.
|
{| cellpadding="20" style="border: 1px solid darkgray;"
| width="100pt" style="background-color:#dddddd;"| [[File:fhm_cover.png|175px|link=Foundations of Human Memory]]
<big>[[Foundations of Human Memory]]</big> by [[Michael J. Kahana]] Please [[Foundations of Human Memory|click here]] for more information and errata.
|}
|}

== Computational models of human memory ==
To explain the processes underlying encoding, organization and retrieval of episodic memories, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed a class of retrieved-context models. These models assume that the input to the memory system itself produces contextual drift, and that the current state of context is used to retrieve items from memory. The temporal context model (TCM; [[Publications#HowaKaha02|Howard and Kahana, 2002]]) was introduced to explain recency and contiguity effects in free recall. Specifically, recency effects appear because the context at the time of the memory test is most similar to the context associated with recent items. When an item is retrieved at test, it reinstates the context active when that item was studied. Because this context overlaps with the encoding context of the items' neighbors, a contiguity effect results. Consistent with experimental data, TCM and its variants also predict that recency and contiguity effects are approximately time-scale invariant ([[Publications#SedeEtal08|Sederberg, Howard, and Kahana, 2008]]). [[Publications#PolyEtal09|Polyn, Norman, and Kahana (2009)]] developed the Context Maintenance and Retrieval model (CMR), which is a generalized version of TCM that accounts for the influence of non-temporal associations (e.g., semantic knowledge) on recall dynamics. MATLAB scripts to run the CMR model [[CMR|can be downloaded here]].

Despite the vast stores of memories we accumulate over a lifetime of experience, the human memory system is often able to target just the right information, seemingly effortlessly. How does the memory system accomplish this feat? Most previous models made the simplifying assumption that memory search is automatically restricted to a target list, largely bypassing the need to target the right information. [[Publications#LohnEtal14| Lohnas, Polyn, and Kahana (2015)]] developed CMR2 to address this issue. In CMR2, memory accumulates across multiple experimental lists, and temporal context is used both to focus retrieval on a target list, and to censor retrieved information when its match to the current context indicates that it was learned in a non target list. The model simultaneously accounts for a wide range of intralist and internist phenomena, including the pattern of prior-list intrusions observed in free recall, build-up of and release from proactive interference, and the ability to selectively target retrieval of items on specific prior lists (Jang & Huber, 2008; Shiffrin, 1970). [[Publications#HealKaha15|Healey and Kahana (2015)]] used CMR2 to better understand why memory tends to get worse as we age. By fitting CMR2 to the performance of individual younger and older adults, they identified deficits in four critical processes: sustaining attention across a study episode, generating retrieval cues, resolving competition, and screening for inaccurate memories (intrusions). MATLAB scripts to run the CMR2 model [[Publications#LohnEtal14|can be downloaded here]].

{| style="margin: 0 auto;" cellpadding="20"
|[[File:cmr_neuro.png|none|thumb|200px|''Fig. 1:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR.]]
|[[File:cmr2_data.png|center|thumb|400px|''click to enlarge'' ''Fig. 2:'' '''Data and CMR2 Simulations.''' A. This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag. CMR2 captures older adult's temporal contiguity deficit. B. Release from proactive interference (PI) in lists of categorized words. Participants studied lists of three items, each drawn from the same semantic category (sample words were used in the original study). After three lists from the same semantic category, subjects were then presented with a new set of three lists from a different semantic category. Top: Data from Loess (1967), note the scalloped pattern: performance declines across same-category lists and improves when a new category is pretend. Bottom: CMR2 simulations capture the release from PI pattern. C. CMR2 simulations of hit and false alarm rates in recognition. B. As predicted by CMR2, recognition false alarm rates correlate with free recall intrusion rate. ]]
|}

{| style="margin: 0 auto;" cellpadding="20"
|[[File:crp2a_square.jpg|none|thumb|200px|''Fig. 1:'' '''The contiguity effect in free recall.''' This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag.]]
|[[File:cmr.png|center|thumb|400px|''click to enlarge'' ''Fig. 2:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR. C. IRTs as a function of output position and total number of recalled items (4, 5, 6 or 7). D. Serial position curves for list lengths (LL) of 20, 30 and 40 items.]]
|}

== Neural oscillatory correlates of episodic memory ==

We investigate the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical surface of the brain or in the medial temporal lobes (including hippocampus) as part of the clinical process of localizing seizure foci. One focus of this research is to determine the oscillatory correlates of successful episodic memory formation and retrieval. Analyses of such recordings have shown that 65 - 95 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall ([[Publications#SedeEtal03|Sederberg et al., 2003]]; [[Publications#SedeEtal06|Sederberg et al., 2006]]; [[Publications#BurkEtal14|Burke et al., 2014]]; [[Publications#LongEtal14|Long et al., 2014]]; for a video, click [[HFA_Encoding| here]] ). Gamma activity in hippocampus and neocortex likewise increases prior to successful recall ([[Publications#SedeEtal07|Sederberg et al., 2007]]; [[Publications#LegaEtal11a|Lega et al., 2011]]; [[Publications#BurkEtal14|Burke et al., 2014]]; for a video, click [[HFA_Retrieval| here]] ). The movie below illustrates these findings.

{| style="margin: auto; width: 560px" cellpadding="20"; class="wikitable"
|<HTML5video width="560" height="315" autoplay="true" loop="true">FR</HTML5video>
 ''Fig. 3:'' '''Free Recall Paradigm''' By using a free recall task in which participants study a list of words, we can measure episodic memory formation by comparing the spectral correlates associated with encoding items that are later recalled (red) or forgotten (blue). We record electroencephalographic (EEG) signals from subdurally implanted electrodes in patients with medically intractable epilepsy. We can extract spectral signals (power of a given frequency) from the raw EEG voltage traces for each item and measure when and where power at particular frequencies changes. Successful memory formation is associated with increases in gamma band (65 - 95 Hz) activity in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses can be performed on items during recall to assess when and where memories are retrieved. Successful memory retrieval is associated with increases in gamma band activity in the left neocortex and hippocampus as well as increases in theta band (4 -8 Hz) activity in right temporal lobe. ''' 
|}

The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. In [[Publications#MannEtal11a|Manning et al., 2011]], we sought to determine whether contextual reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. We examined ECoG activity from 69 neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded just prior to each recall with those recorded after the patient had studied each word. We found that, upon recalling a studied word, the recorded patterns of brain activity were not only similar to the patterns observed when the word was studied, but were also similar to the patterns observed during study of neighboring list words, with similarity decreasing reliably with positional distance (Fig. 4C), just as predicted by context reinstatement models of free recall. The degree to which individual patients exhibited this neural signature of contextual reinstatement was correlated with the contiguity effect as seen in Fig. 4D. In this way, the study provides neural evidence for contextual reinstatement in humans.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:neuralContext.png|center|thumb|650px|''Fig. 4:'' '''Neural evidence for contextual reinstatement in humans.''' A. After studying a list of 20 words and performing a brief distraction task, a participant recalls as many words as he can remember, in any order. ECoG activity is recorded during each study and recall event. The similarity between the recorded patterns is computed as a function of lag. B. Each dot marks the location of a single electrode [temporal lobe (1,815 electrodes), frontal lobe (1,737 electrodes), parietal lobe (512 electrodes), and occipital lobe (138 electrodes)]. C. Similarity between the activity recorded during recall of a word from serial position i and study of a word from serial position i + lag. (Black dot denotes study and recall of the same word, i.e., lag = 0.) D. Participants exhibiting stronger neural signatures of context reinstatement also exhibited more pronounced contiguity effects (as measured by a percentile-based temporal clustering score). (Figure after Manning et al., 2011.)]]
|}

== Human spatial memory and cognition ==

[[File:MillerF2.png|thumb|225px|''Fig. 7:'' '''Place responsive cells.''' (a) Firing-rate maps for a cell responsive to northward traversals located in the hippocampus of one of the participants. (b) A cell responsive to eastward traversals recorded from a participant's entorhinal cortex. (c) Regional distribution of place-responsive cells across the entire data set of 371 single units (H, hippocampus; A, amygdala; EC, entorhinal cortex; PHG, parahippocampal gyrus; Ant, anterior medial temporal lobe).]]

Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click [http://memory.psych.upenn.edu/files/misc/yc2_movie.mov here]. Using this approach, we have documented the existence and character of the 4-8 Hz theta rhythm in the human brain as participants learned to navigate through complex virtual environments ([[Publications#KahaEtal99b|Kahana et al., 1999]]; [[Publications#CaplEtal01|Caplan et al., 2001]]; [[Publications#CaplEtal03|Caplan et al., 2003]]; [[Publications#EkstEtal05|Ekstrom et al., 2005]]; [[Publications#JacoEtal09|Jacobs et al., 2010a]]). Recording individual neurons during virtual navigation, we have discovered "place cells" in the human brain. These cells, which are found primarily in the human hippocampus, become active when a given spatial location is being traversed [[Publications#EkstEtal03|Ekstrom et al. (2003)]]. We also identified several other cellular responses during navigation: cells that become active in response to viewing a salient landmark (from any location), cells that become active when searching for a particular goal location (irrespective of location or view), and cells that respond when traveling in a given direction (bearing/heading).

[[Publications#JacoEtal10|Jacobs et al. (2010b)]] examined recordings of single-neuron activity from neurosurgical patients playing a virtual-navigation video game. In addition to place cells, which encode the current virtual location, we describe a unique cell type, entorhinal cortex (EC) path cells, the activity of which indicates whether the patient is taking a clockwise or counterclockwise path around the virtual square road. We find that many EC path cells exhibit this directional activity throughout the environment, in contrast to hippocampal neurons, which primarily encode information about specific locations. More broadly, these findings support the hypothesis that EC encodes general properties of the current context (e.g., location or direction) that are used by hippocampus to build unique representations reflecting combinations of these properties.

'''Grid cells''' in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, [[Publications#JacoEtal13|Jacobs et al. (2013)]] identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

Theories of episodic memory suggest that memory encoding and retrieval are facilitated by '''spatiotemporal context''': a continually updated representation of location in space and time. Recent work in the lab sought to examine the role of spatial context in human episodic memory retrieval through a hybrid spatial and episodic memory task. [[Publications#MillEtal13.pdf|Miller et al. (2013)]] identified place cells as neurosurgical patients performed a delivery task which required them to deliver items to different stores in a virtual town. Following this navigation task, patients were asked to freely recall the items they had delivered. Place cell activation patterns during the navigational task were then compared to the subsequent activation patterns that occurred during the free recall task. Miller et al. found that neural activity during the retrieval of each delivered item was similar to the neural activity associated with the location where that item was encoded. These findings demonstrate context-specific reinstatement of place-responsive cell activity at the time of recall, supporting theories that implicate contextual reinstatement as the basis for memory retrieval.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:MillerF3.png|center|thumb|800px|''Fig. 8:'' '''Spatial context reinstatement.''' (a) Time courses of neural similarity between ensemble place-cell activity during navigation and during item recall for near, middle, and far spatial distance bins. Shaded regions indicate SEM across recalled items, the horizontal bars indicate statistically significant time points and the vertical dotted line indicates the onset of vocalization. (b) Average neural similarity for near, middle and far distance bins for the time period of -300 to 700ms relative to recall onset. Error bars indicate SEM across recalled items. (c) Neural similarity for near and far spatial distance bins for each participants (colored lines) and the participant average (black line) during -300 to 700ms relative to recall onset. Error bars indicate SEM across participants.]]
|}







[[Category:public]]

Main Page

2015-10-05T20:42:18Z

Healeym: /* Computational models of human memory */

__NOTITLE__
__NOTOC__
[[File:CML_Logo.png|center|link=]]

{| border="0" cellpadding="10" style="margin: 0 auto;"
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Models-thumb.png|frameless|left|border|150px|link=#Computational models of human memory]]
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| width="110pt" | [[#Computational models of human memory|Computational models of human memory]]
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Ecog-thumb.png|frameless|left|border|150px|link=#Neural oscillatory correlates of episodic memory]]
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| width="110pt" | [[#Neural oscillatory correlates of episodic memory|Neural oscillatory correlates of episodic memory]]
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:MillerThumb.png|frameless|left|border|150px|link=#Human spatial memory and cognition]]
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| width="110pt" | [[#Human spatial memory and cognition|Human spatial memory and cognition]]
|}

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{| cellpadding="20" style="margin: 0 auto;"
| width="700pt" | The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients. Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1). Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the ''subsequent memory effect'' (see Fig. 3). Two of our ongoing, large-scale data collection projects are the [[Penn Electrophysiology of Encoding and Retrieval Study]] ([[PEERS]]), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found [http://memory.psych.upenn.edu/files/misc/ALL_ltpFR1_1-20_16-04-2015.pdf here]); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.
|
{| cellpadding="20" style="border: 1px solid darkgray;"
| width="100pt" style="background-color:#dddddd;"| [[File:fhm_cover.png|175px|link=Foundations of Human Memory]]
<big>[[Foundations of Human Memory]]</big> by [[Michael J. Kahana]] Please [[Foundations of Human Memory|click here]] for more information and errata.
|}
|}

== Computational models of human memory ==
To explain the processes underlying encoding, organization and retrieval of episodic memories, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed a class of retrieved-context models. These models assume that the input to the memory system itself produces contextual drift, and that the current state of context is used to retrieve items from memory. The temporal context model (TCM; [[Publications#HowaKaha02|Howard and Kahana, 2002]]) was introduced to explain recency and contiguity effects in free recall. Specifically, recency effects appear because the context at the time of the memory test is most similar to the context associated with recent items. When an item is retrieved at test, it reinstates the context active when that item was studied. Because this context overlaps with the encoding context of the items' neighbors, a contiguity effect results. Consistent with experimental data, TCM and its variants also predict that recency and contiguity effects are approximately time-scale invariant ([[Publications#SedeEtal08|Sederberg, Howard, and Kahana, 2008]]). [[Publications#PolyEtal09|Polyn, Norman, and Kahana (2009)]] developed the Context Maintenance and Retrieval model (CMR), which is a generalized version of TCM that accounts for the influence of non-temporal associations (e.g., semantic knowledge) on recall dynamics. MATLAB scripts to run the CMR model [[CMR|can be downloaded here]].

Despite the vast stores of memories we accumulate over a lifetime of experience, the human memory system is often able to target just the right information, seemingly effortlessly. How does the memory system accomplish this feat? Most previous models made the simplifying assumption that memory search is automatically restricted to a target list, largely bypassing the need to target the right information. [[Publications#LohnEtal14| Lohnas, Polyn, and Kahana (2015)]] developed CMR2 to address this issue. In CMR2, memory accumulates across multiple experimental lists, and temporal context is used both to focus retrieval on a target list, and to censor retrieved information when its match to the current context indicates that it was learned in a non target list. The model simultaneously accounts for a wide range of intralist and internist phenomena, including the pattern of prior-list intrusions observed in free recall, build-up of and release from proactive interference, and the ability to selectively target retrieval of items on specific prior lists (Jang & Huber, 2008; Shiffrin, 1970). [[Publications#HealKaha15|Healey and Kahana (2015)]] used CMR2 to better understand why memory tends to get worse as we age. By fitting CMR2 to the performance of individual younger and older adults, they identified deficits in four critical processes: sustaining attention across a study episode, generating retrieval cues, resolving competition, and screening for inaccurate memories (intrusions). MATLAB scripts to run the CMR2 model [[Publications#LohnEtal14|can be downloaded here]].

{| style="margin: 0 auto;" cellpadding="20"
|[[File:cmr_neuro.png|none|thumb|200px|''Fig. 1:'' '''he context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR.]]
|[[File:cmr.png|center|thumb|400px|''click to enlarge'' ''Fig. 2:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR. C. IRTs as a function of output position and total number of recalled items (4, 5, 6 or 7). D. Serial position curves for list lengths (LL) of 20, 30 and 40 items.]]
|}

{| style="margin: 0 auto;" cellpadding="20"
|[[File:crp2a_square.jpg|none|thumb|200px|''Fig. 1:'' '''The contiguity effect in free recall.''' This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag.]]
|[[File:cmr.png|center|thumb|400px|''click to enlarge'' ''Fig. 2:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR. C. IRTs as a function of output position and total number of recalled items (4, 5, 6 or 7). D. Serial position curves for list lengths (LL) of 20, 30 and 40 items.]]
|}

== Neural oscillatory correlates of episodic memory ==

We investigate the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical surface of the brain or in the medial temporal lobes (including hippocampus) as part of the clinical process of localizing seizure foci. One focus of this research is to determine the oscillatory correlates of successful episodic memory formation and retrieval. Analyses of such recordings have shown that 65 - 95 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall ([[Publications#SedeEtal03|Sederberg et al., 2003]]; [[Publications#SedeEtal06|Sederberg et al., 2006]]; [[Publications#BurkEtal14|Burke et al., 2014]]; [[Publications#LongEtal14|Long et al., 2014]]; for a video, click [[HFA_Encoding| here]] ). Gamma activity in hippocampus and neocortex likewise increases prior to successful recall ([[Publications#SedeEtal07|Sederberg et al., 2007]]; [[Publications#LegaEtal11a|Lega et al., 2011]]; [[Publications#BurkEtal14|Burke et al., 2014]]; for a video, click [[HFA_Retrieval| here]] ). The movie below illustrates these findings.

{| style="margin: auto; width: 560px" cellpadding="20"; class="wikitable"
|<HTML5video width="560" height="315" autoplay="true" loop="true">FR</HTML5video>
 ''Fig. 3:'' '''Free Recall Paradigm''' By using a free recall task in which participants study a list of words, we can measure episodic memory formation by comparing the spectral correlates associated with encoding items that are later recalled (red) or forgotten (blue). We record electroencephalographic (EEG) signals from subdurally implanted electrodes in patients with medically intractable epilepsy. We can extract spectral signals (power of a given frequency) from the raw EEG voltage traces for each item and measure when and where power at particular frequencies changes. Successful memory formation is associated with increases in gamma band (65 - 95 Hz) activity in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses can be performed on items during recall to assess when and where memories are retrieved. Successful memory retrieval is associated with increases in gamma band activity in the left neocortex and hippocampus as well as increases in theta band (4 -8 Hz) activity in right temporal lobe. ''' 
|}

The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. In [[Publications#MannEtal11a|Manning et al., 2011]], we sought to determine whether contextual reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. We examined ECoG activity from 69 neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded just prior to each recall with those recorded after the patient had studied each word. We found that, upon recalling a studied word, the recorded patterns of brain activity were not only similar to the patterns observed when the word was studied, but were also similar to the patterns observed during study of neighboring list words, with similarity decreasing reliably with positional distance (Fig. 4C), just as predicted by context reinstatement models of free recall. The degree to which individual patients exhibited this neural signature of contextual reinstatement was correlated with the contiguity effect as seen in Fig. 4D. In this way, the study provides neural evidence for contextual reinstatement in humans.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:neuralContext.png|center|thumb|650px|''Fig. 4:'' '''Neural evidence for contextual reinstatement in humans.''' A. After studying a list of 20 words and performing a brief distraction task, a participant recalls as many words as he can remember, in any order. ECoG activity is recorded during each study and recall event. The similarity between the recorded patterns is computed as a function of lag. B. Each dot marks the location of a single electrode [temporal lobe (1,815 electrodes), frontal lobe (1,737 electrodes), parietal lobe (512 electrodes), and occipital lobe (138 electrodes)]. C. Similarity between the activity recorded during recall of a word from serial position i and study of a word from serial position i + lag. (Black dot denotes study and recall of the same word, i.e., lag = 0.) D. Participants exhibiting stronger neural signatures of context reinstatement also exhibited more pronounced contiguity effects (as measured by a percentile-based temporal clustering score). (Figure after Manning et al., 2011.)]]
|}

== Human spatial memory and cognition ==

[[File:MillerF2.png|thumb|225px|''Fig. 7:'' '''Place responsive cells.''' (a) Firing-rate maps for a cell responsive to northward traversals located in the hippocampus of one of the participants. (b) A cell responsive to eastward traversals recorded from a participant's entorhinal cortex. (c) Regional distribution of place-responsive cells across the entire data set of 371 single units (H, hippocampus; A, amygdala; EC, entorhinal cortex; PHG, parahippocampal gyrus; Ant, anterior medial temporal lobe).]]

Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click [http://memory.psych.upenn.edu/files/misc/yc2_movie.mov here]. Using this approach, we have documented the existence and character of the 4-8 Hz theta rhythm in the human brain as participants learned to navigate through complex virtual environments ([[Publications#KahaEtal99b|Kahana et al., 1999]]; [[Publications#CaplEtal01|Caplan et al., 2001]]; [[Publications#CaplEtal03|Caplan et al., 2003]]; [[Publications#EkstEtal05|Ekstrom et al., 2005]]; [[Publications#JacoEtal09|Jacobs et al., 2010a]]). Recording individual neurons during virtual navigation, we have discovered "place cells" in the human brain. These cells, which are found primarily in the human hippocampus, become active when a given spatial location is being traversed [[Publications#EkstEtal03|Ekstrom et al. (2003)]]. We also identified several other cellular responses during navigation: cells that become active in response to viewing a salient landmark (from any location), cells that become active when searching for a particular goal location (irrespective of location or view), and cells that respond when traveling in a given direction (bearing/heading).

[[Publications#JacoEtal10|Jacobs et al. (2010b)]] examined recordings of single-neuron activity from neurosurgical patients playing a virtual-navigation video game. In addition to place cells, which encode the current virtual location, we describe a unique cell type, entorhinal cortex (EC) path cells, the activity of which indicates whether the patient is taking a clockwise or counterclockwise path around the virtual square road. We find that many EC path cells exhibit this directional activity throughout the environment, in contrast to hippocampal neurons, which primarily encode information about specific locations. More broadly, these findings support the hypothesis that EC encodes general properties of the current context (e.g., location or direction) that are used by hippocampus to build unique representations reflecting combinations of these properties.

'''Grid cells''' in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, [[Publications#JacoEtal13|Jacobs et al. (2013)]] identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

Theories of episodic memory suggest that memory encoding and retrieval are facilitated by '''spatiotemporal context''': a continually updated representation of location in space and time. Recent work in the lab sought to examine the role of spatial context in human episodic memory retrieval through a hybrid spatial and episodic memory task. [[Publications#MillEtal13.pdf|Miller et al. (2013)]] identified place cells as neurosurgical patients performed a delivery task which required them to deliver items to different stores in a virtual town. Following this navigation task, patients were asked to freely recall the items they had delivered. Place cell activation patterns during the navigational task were then compared to the subsequent activation patterns that occurred during the free recall task. Miller et al. found that neural activity during the retrieval of each delivered item was similar to the neural activity associated with the location where that item was encoded. These findings demonstrate context-specific reinstatement of place-responsive cell activity at the time of recall, supporting theories that implicate contextual reinstatement as the basis for memory retrieval.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:MillerF3.png|center|thumb|800px|''Fig. 8:'' '''Spatial context reinstatement.''' (a) Time courses of neural similarity between ensemble place-cell activity during navigation and during item recall for near, middle, and far spatial distance bins. Shaded regions indicate SEM across recalled items, the horizontal bars indicate statistically significant time points and the vertical dotted line indicates the onset of vocalization. (b) Average neural similarity for near, middle and far distance bins for the time period of -300 to 700ms relative to recall onset. Error bars indicate SEM across recalled items. (c) Neural similarity for near and far spatial distance bins for each participants (colored lines) and the participant average (black line) during -300 to 700ms relative to recall onset. Error bars indicate SEM across participants.]]
|}







[[Category:public]]

File:Cmr neuro.png

2015-10-05T20:42:05Z

Healeym:

File:Cmr.png

2015-10-05T20:37:37Z

Healeym: Healeym uploaded a new version of "File:Cmr.png"

Main Page

2015-10-05T20:00:04Z

Healeym: /* Computational models of human memory */

__NOTITLE__
__NOTOC__
[[File:CML_Logo.png|center|link=]]

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{| cellpadding="20" style="margin: 0 auto;"
| width="700pt" | The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients. Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1). Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the ''subsequent memory effect'' (see Fig. 3). Two of our ongoing, large-scale data collection projects are the [[Penn Electrophysiology of Encoding and Retrieval Study]] ([[PEERS]]), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found [http://memory.psych.upenn.edu/files/misc/ALL_ltpFR1_1-20_16-04-2015.pdf here]); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.
|
{| cellpadding="20" style="border: 1px solid darkgray;"
| width="100pt" style="background-color:#dddddd;"| [[File:fhm_cover.png|175px|link=Foundations of Human Memory]]
<big>[[Foundations of Human Memory]]</big> by [[Michael J. Kahana]] Please [[Foundations of Human Memory|click here]] for more information and errata.
|}
|}

== Computational models of human memory ==
To explain the processes underlying encoding, organization and retrieval of episodic memories, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed a class of retrieved-context models. These models assume that the input to the memory system itself produces contextual drift, and that the current state of context is used to retrieve items from memory. The temporal context model (TCM; [[Publications#HowaKaha02|Howard and Kahana, 2002]]) was introduced to explain recency and contiguity effects in free recall. Specifically, recency effects appear because the context at the time of the memory test is most similar to the context associated with recent items. When an item is retrieved at test, it reinstates the context active when that item was studied. Because this context overlaps with the encoding context of the items' neighbors, a contiguity effect results. Consistent with experimental data, TCM and its variants also predict that recency and contiguity effects are approximately time-scale invariant ([[Publications#SedeEtal08|Sederberg, Howard, and Kahana, 2008]]). [[Publications#PolyEtal09|Polyn, Norman, and Kahana (2009)]] developed the Context Maintenance and Retrieval model (CMR), which is a generalized version of TCM that accounts for the influence of non-temporal associations (e.g., semantic knowledge) on recall dynamics. MATLAB scripts to run the CMR model [[CMR|can be downloaded here]].

Despite the vast stores of memories we accumulate over a lifetime of experience, the human memory system is often able to target just the right information, seemingly effortlessly. How does the memory system accomplish this feat? Most previous models made the simplifying assumption that memory search is automatically restricted to a target list, largely bypassing the need to target the right information. [[Publications#LohnEtal14| Lohnas, Polyn, and Kahana (2015)]] developed CMR2 to address this issue. In CMR2, memory accumulates across multiple experimental lists, and temporal context is used both to focus retrieval on a target list, and to censor retrieved information when its match to the current context indicates that it was learned in a non target list. The model simultaneously accounts for a wide range of intralist and internist phenomena, including the pattern of prior-list intrusions observed in free recall, build-up of and release from proactive interference, and the ability to selectively target retrieval of items on specific prior lists (Jang & Huber, 2008; Shiffrin, 1970). [[Publications#HealKaha15|Healey and Kahana (2015)]] used CMR2 to better understand why memory tends to get worse as we age. By fitting CMR2 to the performance of individual younger and older adults, they identified deficits in four critical processes: sustaining attention across a study episode, generating retrieval cues, resolving competition, and screening for inaccurate memories (intrusions). MATLAB scripts to run the CMR2 model [[Publications#LohnEtal14|can be downloaded here]].

{| style="margin: 0 auto;" cellpadding="20"
|[[File:crp2a_square.jpg|none|thumb|200px|''Fig. 1:'' '''The contiguity effect in free recall.''' This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag.]]
|[[File:cmr.png|center|thumb|400px|''click to enlarge'' ''Fig. 2:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR. C. IRTs as a function of output position and total number of recalled items (4, 5, 6 or 7). D. Serial position curves for list lengths (LL) of 20, 30 and 40 items.]]
|}

== Neural oscillatory correlates of episodic memory ==

We investigate the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical surface of the brain or in the medial temporal lobes (including hippocampus) as part of the clinical process of localizing seizure foci. One focus of this research is to determine the oscillatory correlates of successful episodic memory formation and retrieval. Analyses of such recordings have shown that 65 - 95 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall ([[Publications#SedeEtal03|Sederberg et al., 2003]]; [[Publications#SedeEtal06|Sederberg et al., 2006]]; [[Publications#BurkEtal14|Burke et al., 2014]]; [[Publications#LongEtal14|Long et al., 2014]]; for a video, click [[HFA_Encoding| here]] ). Gamma activity in hippocampus and neocortex likewise increases prior to successful recall ([[Publications#SedeEtal07|Sederberg et al., 2007]]; [[Publications#LegaEtal11a|Lega et al., 2011]]; [[Publications#BurkEtal14|Burke et al., 2014]]; for a video, click [[HFA_Retrieval| here]] ). The movie below illustrates these findings.

{| style="margin: auto; width: 560px" cellpadding="20"; class="wikitable"
|<HTML5video width="560" height="315" autoplay="true" loop="true">FR</HTML5video>
 ''Fig. 3:'' '''Free Recall Paradigm''' By using a free recall task in which participants study a list of words, we can measure episodic memory formation by comparing the spectral correlates associated with encoding items that are later recalled (red) or forgotten (blue). We record electroencephalographic (EEG) signals from subdurally implanted electrodes in patients with medically intractable epilepsy. We can extract spectral signals (power of a given frequency) from the raw EEG voltage traces for each item and measure when and where power at particular frequencies changes. Successful memory formation is associated with increases in gamma band (65 - 95 Hz) activity in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses can be performed on items during recall to assess when and where memories are retrieved. Successful memory retrieval is associated with increases in gamma band activity in the left neocortex and hippocampus as well as increases in theta band (4 -8 Hz) activity in right temporal lobe. ''' 
|}

The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. In [[Publications#MannEtal11a|Manning et al., 2011]], we sought to determine whether contextual reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. We examined ECoG activity from 69 neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded just prior to each recall with those recorded after the patient had studied each word. We found that, upon recalling a studied word, the recorded patterns of brain activity were not only similar to the patterns observed when the word was studied, but were also similar to the patterns observed during study of neighboring list words, with similarity decreasing reliably with positional distance (Fig. 4C), just as predicted by context reinstatement models of free recall. The degree to which individual patients exhibited this neural signature of contextual reinstatement was correlated with the contiguity effect as seen in Fig. 4D. In this way, the study provides neural evidence for contextual reinstatement in humans.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:neuralContext.png|center|thumb|650px|''Fig. 4:'' '''Neural evidence for contextual reinstatement in humans.''' A. After studying a list of 20 words and performing a brief distraction task, a participant recalls as many words as he can remember, in any order. ECoG activity is recorded during each study and recall event. The similarity between the recorded patterns is computed as a function of lag. B. Each dot marks the location of a single electrode [temporal lobe (1,815 electrodes), frontal lobe (1,737 electrodes), parietal lobe (512 electrodes), and occipital lobe (138 electrodes)]. C. Similarity between the activity recorded during recall of a word from serial position i and study of a word from serial position i + lag. (Black dot denotes study and recall of the same word, i.e., lag = 0.) D. Participants exhibiting stronger neural signatures of context reinstatement also exhibited more pronounced contiguity effects (as measured by a percentile-based temporal clustering score). (Figure after Manning et al., 2011.)]]
|}

== Human spatial memory and cognition ==

[[File:MillerF2.png|thumb|225px|''Fig. 7:'' '''Place responsive cells.''' (a) Firing-rate maps for a cell responsive to northward traversals located in the hippocampus of one of the participants. (b) A cell responsive to eastward traversals recorded from a participant's entorhinal cortex. (c) Regional distribution of place-responsive cells across the entire data set of 371 single units (H, hippocampus; A, amygdala; EC, entorhinal cortex; PHG, parahippocampal gyrus; Ant, anterior medial temporal lobe).]]

Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click [http://memory.psych.upenn.edu/files/misc/yc2_movie.mov here]. Using this approach, we have documented the existence and character of the 4-8 Hz theta rhythm in the human brain as participants learned to navigate through complex virtual environments ([[Publications#KahaEtal99b|Kahana et al., 1999]]; [[Publications#CaplEtal01|Caplan et al., 2001]]; [[Publications#CaplEtal03|Caplan et al., 2003]]; [[Publications#EkstEtal05|Ekstrom et al., 2005]]; [[Publications#JacoEtal09|Jacobs et al., 2010a]]). Recording individual neurons during virtual navigation, we have discovered "place cells" in the human brain. These cells, which are found primarily in the human hippocampus, become active when a given spatial location is being traversed [[Publications#EkstEtal03|Ekstrom et al. (2003)]]. We also identified several other cellular responses during navigation: cells that become active in response to viewing a salient landmark (from any location), cells that become active when searching for a particular goal location (irrespective of location or view), and cells that respond when traveling in a given direction (bearing/heading).

[[Publications#JacoEtal10|Jacobs et al. (2010b)]] examined recordings of single-neuron activity from neurosurgical patients playing a virtual-navigation video game. In addition to place cells, which encode the current virtual location, we describe a unique cell type, entorhinal cortex (EC) path cells, the activity of which indicates whether the patient is taking a clockwise or counterclockwise path around the virtual square road. We find that many EC path cells exhibit this directional activity throughout the environment, in contrast to hippocampal neurons, which primarily encode information about specific locations. More broadly, these findings support the hypothesis that EC encodes general properties of the current context (e.g., location or direction) that are used by hippocampus to build unique representations reflecting combinations of these properties.

'''Grid cells''' in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, [[Publications#JacoEtal13|Jacobs et al. (2013)]] identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

Theories of episodic memory suggest that memory encoding and retrieval are facilitated by '''spatiotemporal context''': a continually updated representation of location in space and time. Recent work in the lab sought to examine the role of spatial context in human episodic memory retrieval through a hybrid spatial and episodic memory task. [[Publications#MillEtal13.pdf|Miller et al. (2013)]] identified place cells as neurosurgical patients performed a delivery task which required them to deliver items to different stores in a virtual town. Following this navigation task, patients were asked to freely recall the items they had delivered. Place cell activation patterns during the navigational task were then compared to the subsequent activation patterns that occurred during the free recall task. Miller et al. found that neural activity during the retrieval of each delivered item was similar to the neural activity associated with the location where that item was encoded. These findings demonstrate context-specific reinstatement of place-responsive cell activity at the time of recall, supporting theories that implicate contextual reinstatement as the basis for memory retrieval.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:MillerF3.png|center|thumb|800px|''Fig. 8:'' '''Spatial context reinstatement.''' (a) Time courses of neural similarity between ensemble place-cell activity during navigation and during item recall for near, middle, and far spatial distance bins. Shaded regions indicate SEM across recalled items, the horizontal bars indicate statistically significant time points and the vertical dotted line indicates the onset of vocalization. (b) Average neural similarity for near, middle and far distance bins for the time period of -300 to 700ms relative to recall onset. Error bars indicate SEM across recalled items. (c) Neural similarity for near and far spatial distance bins for each participants (colored lines) and the participant average (black line) during -300 to 700ms relative to recall onset. Error bars indicate SEM across participants.]]
|}







[[Category:public]]

Main Page

2015-09-29T19:08:51Z

Healeym: /* Computational models of human memory */

__NOTITLE__
__NOTOC__
[[File:CML_Logo.png|center|link=]]

{| border="0" cellpadding="10" style="margin: 0 auto;"
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:Ecog-thumb.png|frameless|left|border|150px|link=#Neural oscillatory correlates of episodic memory]]
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| width="110pt" | [[#Neural oscillatory correlates of episodic memory|Neural oscillatory correlates of episodic memory]]
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{| border="0" cellpadding="5" style="border: 1px solid darkgray;"
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| [[File:MillerThumb.png|frameless|left|border|150px|link=#Human spatial memory and cognition]]
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| width="110pt" | [[#Human spatial memory and cognition|Human spatial memory and cognition]]
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{| cellpadding="20" style="margin: 0 auto;"
| width="700pt" | The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients. Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1). Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the ''subsequent memory effect'' (see Fig. 3). Two of our ongoing, large-scale data collection projects are the [[Penn Electrophysiology of Encoding and Retrieval Study]] ([[PEERS]]), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found [http://memory.psych.upenn.edu/files/misc/ALL_ltpFR1_1-20_16-04-2015.pdf here]); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.
|
{| cellpadding="20" style="border: 1px solid darkgray;"
| width="100pt" style="background-color:#dddddd;"| [[File:fhm_cover.png|175px|link=Foundations of Human Memory]]
<big>[[Foundations of Human Memory]]</big> by [[Michael J. Kahana]] Please [[Foundations of Human Memory|click here]] for more information and errata.
|}
|}

== Computational models of human memory ==
To explain the processes underlying encoding, organization and retrieval of episodic memories, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed a class of retrieved-context models. These models assume that the input to the memory system itself produces contextual drift, and that the current state of context is used to retrieve items from memory. The temporal context model (TCM; [[Publications#HowaKaha02|Howard and Kahana, 2002]]) was introduced to explain recency and contiguity effects in free recall. Specifically, recency effects appear because the context at the time of the memory test is most similar to the context associated with recent items. When an item is retrieved at test, it reinstates the context active when that item was studied. Because this context overlaps with the encoding context of the items' neighbors, a contiguity effect results. Consistent with experimental data, TCM and its variants also predict that recency and contiguity effects are approximately time-scale invariant ([[Publications#SedeEtal08|Sederberg, Howard, and Kahana, 2008]]). [[Publications#PolyEtal09|Polyn, Norman, and Kahana (2009)]] developed the Context Maintenance and Retrieval model(CMR), which is a generalized version of TCM that accounts for the influence of non-temporal associations (e.g., semantic knowledge) on recall dynamics. [[Publications#LohnEtal14| Lohnas, Polyn, and Kahana (2015)]] developed CMR2 to account for how memories evolve and interact as a subject studies multiple lists in an experiment. Most recently, [[Publications#HealKaha15| Healey and Kahana (2015)]] have used CMR2 to better understand why memory tends to get worse as we age.

MATLAB scripts to run the CMR model [[CMR|can be downloaded here]].

{| style="margin: 0 auto;" cellpadding="20"
|[[File:crp2a_square.jpg|none|thumb|200px|''Fig. 1:'' '''The contiguity effect in free recall.''' This curve shows the probability of making a recall to serial position i+lag immediately following recall of serial position i---that is, the conditional-response probability (CRP) as a function of lag.]]
|[[File:cmr.png|center|thumb|400px|''click to enlarge'' ''Fig. 2:'' '''The context-maintenance and retrieval model.''' A. Schematic of CMR. When an item is studied its feature representation (fi) is activated on F. The feature representation contains both item and source features (e.g., size / animacy judgment). An associative weight matrix (MFC) allows each item to update a context representation that contains the item and source features of recently studied items. During study, the features of each item are associated with coactive context elements. During recall, the context representation reactivates (through MCF) the features of recently studied items. B. Hypothesized interactions between prefrontal cortex, temporal cortex, and medial temporal lobe during memory encoding and retrieval predicted by CMR. C. IRTs as a function of output position and total number of recalled items (4, 5, 6 or 7). D. Serial position curves for list lengths (LL) of 20, 30 and 40 items.]]
|}

== Neural oscillatory correlates of episodic memory ==

We investigate the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical surface of the brain or in the medial temporal lobes (including hippocampus) as part of the clinical process of localizing seizure foci. One focus of this research is to determine the oscillatory correlates of successful episodic memory formation and retrieval. Analyses of such recordings have shown that 65 - 95 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall ([[Publications#SedeEtal03|Sederberg et al., 2003]]; [[Publications#SedeEtal06|Sederberg et al., 2006]]; [[Publications#BurkEtal14|Burke et al., 2014]]; [[Publications#LongEtal14|Long et al., 2014]]; for a video, click [[HFA_Encoding| here]] ). Gamma activity in hippocampus and neocortex likewise increases prior to successful recall ([[Publications#SedeEtal07|Sederberg et al., 2007]]; [[Publications#LegaEtal11a|Lega et al., 2011]]; [[Publications#BurkEtal14|Burke et al., 2014]]; for a video, click [[HFA_Retrieval| here]] ). The movie below illustrates these findings.

{| style="margin: auto; width: 560px" cellpadding="20"; class="wikitable"
|<HTML5video width="560" height="315" autoplay="true" loop="true">FR</HTML5video>
 ''Fig. 3:'' '''Free Recall Paradigm''' By using a free recall task in which participants study a list of words, we can measure episodic memory formation by comparing the spectral correlates associated with encoding items that are later recalled (red) or forgotten (blue). We record electroencephalographic (EEG) signals from subdurally implanted electrodes in patients with medically intractable epilepsy. We can extract spectral signals (power of a given frequency) from the raw EEG voltage traces for each item and measure when and where power at particular frequencies changes. Successful memory formation is associated with increases in gamma band (65 - 95 Hz) activity in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses can be performed on items during recall to assess when and where memories are retrieved. Successful memory retrieval is associated with increases in gamma band activity in the left neocortex and hippocampus as well as increases in theta band (4 -8 Hz) activity in right temporal lobe. ''' 
|}

The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. In [[Publications#MannEtal11a|Manning et al., 2011]], we sought to determine whether contextual reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. We examined ECoG activity from 69 neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded just prior to each recall with those recorded after the patient had studied each word. We found that, upon recalling a studied word, the recorded patterns of brain activity were not only similar to the patterns observed when the word was studied, but were also similar to the patterns observed during study of neighboring list words, with similarity decreasing reliably with positional distance (Fig. 4C), just as predicted by context reinstatement models of free recall. The degree to which individual patients exhibited this neural signature of contextual reinstatement was correlated with the contiguity effect as seen in Fig. 4D. In this way, the study provides neural evidence for contextual reinstatement in humans.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:neuralContext.png|center|thumb|650px|''Fig. 4:'' '''Neural evidence for contextual reinstatement in humans.''' A. After studying a list of 20 words and performing a brief distraction task, a participant recalls as many words as he can remember, in any order. ECoG activity is recorded during each study and recall event. The similarity between the recorded patterns is computed as a function of lag. B. Each dot marks the location of a single electrode [temporal lobe (1,815 electrodes), frontal lobe (1,737 electrodes), parietal lobe (512 electrodes), and occipital lobe (138 electrodes)]. C. Similarity between the activity recorded during recall of a word from serial position i and study of a word from serial position i + lag. (Black dot denotes study and recall of the same word, i.e., lag = 0.) D. Participants exhibiting stronger neural signatures of context reinstatement also exhibited more pronounced contiguity effects (as measured by a percentile-based temporal clustering score). (Figure after Manning et al., 2011.)]]
|}

== Human spatial memory and cognition ==

[[File:MillerF2.png|thumb|225px|''Fig. 7:'' '''Place responsive cells.''' (a) Firing-rate maps for a cell responsive to northward traversals located in the hippocampus of one of the participants. (b) A cell responsive to eastward traversals recorded from a participant's entorhinal cortex. (c) Regional distribution of place-responsive cells across the entire data set of 371 single units (H, hippocampus; A, amygdala; EC, entorhinal cortex; PHG, parahippocampal gyrus; Ant, anterior medial temporal lobe).]]

Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click [http://memory.psych.upenn.edu/files/misc/yc2_movie.mov here]. Using this approach, we have documented the existence and character of the 4-8 Hz theta rhythm in the human brain as participants learned to navigate through complex virtual environments ([[Publications#KahaEtal99b|Kahana et al., 1999]]; [[Publications#CaplEtal01|Caplan et al., 2001]]; [[Publications#CaplEtal03|Caplan et al., 2003]]; [[Publications#EkstEtal05|Ekstrom et al., 2005]]; [[Publications#JacoEtal09|Jacobs et al., 2010a]]). Recording individual neurons during virtual navigation, we have discovered "place cells" in the human brain. These cells, which are found primarily in the human hippocampus, become active when a given spatial location is being traversed [[Publications#EkstEtal03|Ekstrom et al. (2003)]]. We also identified several other cellular responses during navigation: cells that become active in response to viewing a salient landmark (from any location), cells that become active when searching for a particular goal location (irrespective of location or view), and cells that respond when traveling in a given direction (bearing/heading).

[[Publications#JacoEtal10|Jacobs et al. (2010b)]] examined recordings of single-neuron activity from neurosurgical patients playing a virtual-navigation video game. In addition to place cells, which encode the current virtual location, we describe a unique cell type, entorhinal cortex (EC) path cells, the activity of which indicates whether the patient is taking a clockwise or counterclockwise path around the virtual square road. We find that many EC path cells exhibit this directional activity throughout the environment, in contrast to hippocampal neurons, which primarily encode information about specific locations. More broadly, these findings support the hypothesis that EC encodes general properties of the current context (e.g., location or direction) that are used by hippocampus to build unique representations reflecting combinations of these properties.

'''Grid cells''' in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, [[Publications#JacoEtal13|Jacobs et al. (2013)]] identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

Theories of episodic memory suggest that memory encoding and retrieval are facilitated by '''spatiotemporal context''': a continually updated representation of location in space and time. Recent work in the lab sought to examine the role of spatial context in human episodic memory retrieval through a hybrid spatial and episodic memory task. [[Publications#MillEtal13.pdf|Miller et al. (2013)]] identified place cells as neurosurgical patients performed a delivery task which required them to deliver items to different stores in a virtual town. Following this navigation task, patients were asked to freely recall the items they had delivered. Place cell activation patterns during the navigational task were then compared to the subsequent activation patterns that occurred during the free recall task. Miller et al. found that neural activity during the retrieval of each delivered item was similar to the neural activity associated with the location where that item was encoded. These findings demonstrate context-specific reinstatement of place-responsive cell activity at the time of recall, supporting theories that implicate contextual reinstatement as the basis for memory retrieval.

{| style="margin: 0 auto;" cellpadding="20"
|[[File:MillerF3.png|center|thumb|800px|''Fig. 8:'' '''Spatial context reinstatement.''' (a) Time courses of neural similarity between ensemble place-cell activity during navigation and during item recall for near, middle, and far spatial distance bins. Shaded regions indicate SEM across recalled items, the horizontal bars indicate statistically significant time points and the vertical dotted line indicates the onset of vocalization. (b) Average neural similarity for near, middle and far distance bins for the time period of -300 to 700ms relative to recall onset. Error bars indicate SEM across recalled items. (c) Neural similarity for near and far spatial distance bins for each participants (colored lines) and the participant average (black line) during -300 to 700ms relative to recall onset. Error bars indicate SEM across participants.]]
|}







[[Category:public]]

Data Archive

2015-02-09T19:52:51Z

Healeym:

The entirety of the [[PEERS]] dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]

Please also see our collection of [[Electrophysiological Data|available electrophysiological data here]].
<include nopre noesc src="http://memory.psych.upenn.edu/files/pages/dataArchive.html" />
[[Category:Public]]

Data Archive

2015-02-09T19:52:36Z

Healeym:

The entirety of the [[PEERS]] dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]

Please also see our collection of [[Electrophysiological Data|available electrophysiological data here]].

<include nopre noesc src="http://memory.psych.upenn.edu/files/pages/dataArchive.html" />

[[Category:Public]]

Data Archive

2015-02-09T19:52:17Z

Healeym:

The entirety of the [[PEERS]] dataset is available [http://memory.psych.upenn.edu/files/PEERS.data.tgz here]
<include nopre noesc src="http://memory.psych.upenn.edu/files/pages/dataArchive.html" />

Please also see our collection of [[Electrophysiological Data|available electrophysiological data here]].

[[Category:Public]]

Software

2014-11-13T16:41:12Z

Healeym: /* Data Analysis */

__NOTOC__
== Experiments ==

=== Foundational Libraries ===
* [http://pyepl.sourceforge.net PyEPL] (the Python Experiment-Programming Library) is a library for coding psychology experiments in Python. It supports presentation of both visual and auditory stimuli, and supports both manual (keyboard/joystick) and sound (microphone) input as responses. Visit the [http://pyepl.sourceforge.net PyEPL SourceForge page] for more information and downloads. ([[Publications#GellEtal07|Methods paper can be found here.]])
* [[PandaEPL]] is a cross-platform Python library for programming 3D spatial navigation experiments. ([[Publications#SolwEtal13|Methods paper can be found here.]])

=== Experiment Paradigms ===
PyEPL-based experiments used in the Kahana Lab.

* pyFGS: Face/Grating Sternberg task ([http://memory.psych.upenn.edu/files/software/experiments/pyFGS.tgz tgz])
* pyFR: Free Recall task ([http://memory.psych.upenn.edu/files/software/experiments/pyFR.tgz tgz])
* YellowCab II: Virtual Driving task ([http://memory.psych.upenn.edu/files/software/experiments/yellowcab2.tgz tgz (58.3 MB)])
* ycCross: YellowCab Variant ([http://memory.psych.upenn.edu/files/software/experiments/ycCross.tgz tgz (30.5 MB)])
* ycMagellan: [[PandaEPL]]-based YellowCab variant, as used in [[Publications#MannEtal13|Manning et al., submitted]] ([http://memory.psych.upenn.edu/files/software/experiments/ycMagellan.tgz experiment tgz (50.8 MB)], [http://memory.psych.upenn.edu/files/software/experiments/ycMagellan_buildings.tgz buildings tgz (3.1 GB)])
* Trackball: Blinking and eye-movement task ([http://memory.psych.upenn.edu/files/software/experiments/trackball.tgz tgz])
* Testsync: Simple program to send sync pulses ([http://memory.psych.upenn.edu/files/software/experiments/testsync.tgz tgz])

== Data Analysis ==
* [[TotalRecall|Penn TotalRecall]]: score and annotate behavioral audio files (replaces PyParse)
* [[behavioral_toolbox|Behavioral Toolbox]]: a suite of MATLAB functions to aid in analyzing behavioral Free Recall data
* Our EEG Toolbox is a set of Matlab functions to help in analyzing EEG data.
** '''Lab members and collaborators (e.g., members of the RAM team) should checkout the the most recent version from the lab’s SVN server''' (for instructions, see the internal wiki [https://memory.psych.upenn.edu/InternalWiki/Eeg_toolbox#Introduction EEG Toolbox page])
** The latest public release can be downloaded [http://memory.psych.upenn.edu/files/software/eeg_toolbox/eeg_toolbox.zip here (zip)]. Current version is 1.3.2, last update June 25, 2008.
** For documentation, please see <code><nowiki>eeg_toolbox/doc/doc.pdf</nowiki></code>. Additionally, use the <code><nowiki> help </nowiki></code> function in Matlab for assistance with individual functions.
** You can also find Dr Josh Jacob's [http://memory.psych.upenn.edu/files/misc/EEGtoolbox-goodversion.pdf "Introduction to the EEG toolbox" here].

== Simulation Packages ==
* The Context Maintenance and Retrieval model ([[CMR]]).
[[Category:public]]

Software

2014-11-13T16:40:18Z

Healeym: /* Data Analysis */

__NOTOC__
== Experiments ==

=== Foundational Libraries ===
* [http://pyepl.sourceforge.net PyEPL] (the Python Experiment-Programming Library) is a library for coding psychology experiments in Python. It supports presentation of both visual and auditory stimuli, and supports both manual (keyboard/joystick) and sound (microphone) input as responses. Visit the [http://pyepl.sourceforge.net PyEPL SourceForge page] for more information and downloads. ([[Publications#GellEtal07|Methods paper can be found here.]])
* [[PandaEPL]] is a cross-platform Python library for programming 3D spatial navigation experiments. ([[Publications#SolwEtal13|Methods paper can be found here.]])

=== Experiment Paradigms ===
PyEPL-based experiments used in the Kahana Lab.

* pyFGS: Face/Grating Sternberg task ([http://memory.psych.upenn.edu/files/software/experiments/pyFGS.tgz tgz])
* pyFR: Free Recall task ([http://memory.psych.upenn.edu/files/software/experiments/pyFR.tgz tgz])
* YellowCab II: Virtual Driving task ([http://memory.psych.upenn.edu/files/software/experiments/yellowcab2.tgz tgz (58.3 MB)])
* ycCross: YellowCab Variant ([http://memory.psych.upenn.edu/files/software/experiments/ycCross.tgz tgz (30.5 MB)])
* ycMagellan: [[PandaEPL]]-based YellowCab variant, as used in [[Publications#MannEtal13|Manning et al., submitted]] ([http://memory.psych.upenn.edu/files/software/experiments/ycMagellan.tgz experiment tgz (50.8 MB)], [http://memory.psych.upenn.edu/files/software/experiments/ycMagellan_buildings.tgz buildings tgz (3.1 GB)])
* Trackball: Blinking and eye-movement task ([http://memory.psych.upenn.edu/files/software/experiments/trackball.tgz tgz])
* Testsync: Simple program to send sync pulses ([http://memory.psych.upenn.edu/files/software/experiments/testsync.tgz tgz])

== Data Analysis ==
* [[TotalRecall|Penn TotalRecall]]: score and annotate behavioral audio files (replaces PyParse)
* [[behavioral_toolbox|Behavioral Toolbox]]: a suite of MATLAB functions to aid in analyzing behavioral Free Recall data
* Our EEG Toolbox is a set of Matlab functions to help in analyzing EEG data.
*'''* Lab members and collaborators (e.g., members of the RAM team) should checkout the the most recent version from the lab’s SVN server''' (for instructions, see the internal wiki [https://memory.psych.upenn.edu/InternalWiki/Eeg_toolbox#Introduction EEG Toolbox page])
** The latest public release can be downloaded [http://memory.psych.upenn.edu/files/software/eeg_toolbox/eeg_toolbox.zip here (zip)]. Current version is 1.3.2, last update June 25, 2008.
** For documentation, please see <code><nowiki>eeg_toolbox/doc/doc.pdf</nowiki></code>. Additionally, use the <code><nowiki> help </nowiki></code> function in Matlab for assistance with individual functions.
** You can also find Dr Josh Jacob's [http://memory.psych.upenn.edu/files/misc/EEGtoolbox-goodversion.pdf "Introduction to the EEG toolbox" here].

== Simulation Packages ==
* The Context Maintenance and Retrieval model ([[CMR]]).
[[Category:public]]