Difference between revisions of "Research"
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=== Human spatial memory and cognition ===
=== Human spatial memory and cognition ===
Revision as of 17:05, 20 February 2012
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 done on patients with implanted electrodes.
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 words for study, and then asking participants to recall the words. Using conditional probability and latency analyses (Kahana, M. J., 1996) one can quantify the way in which people transition from one recalled word to the next (see Fig. 1).
|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.|
Computational models of human memory
To explain the recency and contiguity effects, and more generally to understand the dynamics of retrieval from episodic memory, Kahana and colleagues (notably Marc Howard, Sean Polyn, Per Sederberg, and Lynn Lohnas) have developed retrieved temporal context models (the temporal context model, TCM, and more recently the context-maintenance and retrieval model, CMR). These distributed memory models specify the mechanisms of contextual drift and contextual retrieval. Through the drift mechanism, these models describe how a temporal code is created by the integration of recently retrieved contextual states. Rather than resulting from co-occurrence in short-term memory (the standard earlier view), retrieved context models posit that associative tendencies appear because recall of an item recovers the temporal context for the item, which in turn cues recall of subsequent items. Similarly, recency effects appear because the temporal context at the time of the memory test is most similar to the temporal context associated with recent items. Unlike short-term memory based models, TCM predicts that recency and associative effects should be approximately time-scale invariant (Howard, M. W. and Kahana, M. J., 1999, Sederberg et al., 2008).
Neural oscillatory correlates of episodic memory
In addition to behavioral and theoretical analyses of episodic memory, we also explore 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 through 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 44-100 Hz (gamma) brain oscillations increase while participants are studying words that they will successfully, as opposed to unsuccessfully, recall (Sederberg et al., 2006). The same distribution of gamma activity across both hippocampus and neocortex is reactivated just prior to recalling an item, with higher levels of gamma predicting whether or not the recalled item was actually studied (Sederberg et al., 2007; see Fig. 2).
The ability to reinstate contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. Our lab sought to determine whether context reinstatement may be observed in electrical signals recorded from the human brain during episodic recall (Manning et al., 2011). Analyzing electrocorticographic recordings taken as 69 neurosurgical patients studied and recalled lists of words, we uncovered a neural signature of context reinstatement. Upon recalling a studied item, we found that the recorded patterns of brain activity were not only similar to the patterns observed when the item was studied, but were also similar to the patterns observed during study of neighboring list items, with similarity decreasing reliably with positional distance (Fig. X, panels A & B). The degree to which individual patients displayed this neural signature of context reinstatement was correlated with their tendency to recall neighboring list items successively (Fig X, panel C).<<html(
Neural mechanisms underlying human reward learning and decision making
Recent studies in our lab have shown, for the first time, that the activity of individual neurons in the human basal ganglia are related to learning and decision making (Zaghloul et al. 2009, Zaghloul et al., in press). Through clinical collaborations, we directly recorded neural activity from single-neurons in the human basal-ganglia as participants performed a probabilistic learning and and selection task. We found that dopaminergic neurons in the substantia nigra were more active when participants received unexpected rewards compared to when they received expected rewards Zaghloul et al. 2009). This is consistent with current theories of human reinforcement learning that implicate dopaminergic neurons in encoding prediction error, a value that increases as rewards become more surprising (Niv et al 2009). Additionally, we found that neurons in the subthalamic nucleus were more active when participants had to choose between similarly attractive options (Zaghloul et al., in press). This also is consistent with current theories of human decision making which suggest that the subthalamic nucleus plays a critical role in making difficult "high-conflict" decisions (Frank et al 2006). By studying the neural mechanisms underlying human learning and decision making, we hope lay the neurophysiological groundwork for understanding the hypothesized role of the basal ganglia in addiction and pathological reward-seeking and impulsive behavior (Hyman et al., 2006).
activity from 15 SN cells recorded from ten participants.-~||<style="vertical-align: top; width: 50%;">Fig. X: Normalized STN ﬁring rates during high (solid) and low (dashed) conﬂict decision trials. Red line indicates presentation of stimulus pairs. Grey shaded region represents the interval between 100 and 400 ms after stimulus onset. Traces represent activity from 27 STN cells recorded from 14 participants. ||
Human spatial memory and cognition
Our lab is also interested in the neural mechanisms underlying human spatial cognition. In this work, we use virtual reality computer games (Fig. 3) in which participants learn the locations of landmarks in virtual environments. To download a sample of a YellowCab session, click <<ExtLink(/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 (Kahana et al., 1999; Caplan et al., 2001; Caplan et al., 2003; Ekstrom et al., 2005; 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 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), cells that respond when traveling in a given direction (bearing/heading, Jacobs et al., 2010b), and cells that respond along a particular route or path.<<html(
|Fig. 4: Firing-rate map of a right hippocampal cell showing significant place selectivity. Lettered squares (SA,SB,SC) indicate target store locations, white boxes indicate non-target buildings, red lines indicate the subject's trajectory, and the red square indicates regions of significantly high firing rate (all examples, p < 0.01).|