Recognition Memory

	Fig. 1. For every trial (alignment of colored bars is identical to the above figure) oscillations in the theta band (4-8 Hz) is gated for every trial (i.e., it turns on at the start of the trial and turns off at the end). Figure from Raghavachari et al (2001).

Our lab has worked on developing and testing the predictions of quantitative models of recognition memory. We have developed, together with Robert Sekuler, a mathematical model of recognition memory that goes beyond existing models in predicting the accuracy of participants' responses on individual lists (Kahana, M. J. and Sekuler, R., 2002). Our noisy exemplar model (NEMO) is based on the idea that judging whether an item was on a recently studied list depends not only on the similarity of the item to the items in the list (as in other standard models of recognition memory), but also on the similarities among the items within the studied list. NEMO is thus capturing a context effect in the data -- the similarity of a study item to the items in the list is judged relative to the overall level of similarity among the list items.

If one has just studied a list of highly dissimilar items, one would tend to judge a moderately similar item as having been on the studied list; if one has just studied a list of highly similar items, it will be relatively easy to reject the moderately similar item as not having been on the list.

We then wondered how this variation in difficulty due to similarity, which we called similarity-based interference. In this form of interference, you confuse the probe with similar items in the list you had to study. Similarity-based interference was correlated with oscillatory activity in the brain. In a scalp EEG study (van Vugt et al, submitted) we found that similarity-based interference was associated with the amplitude (power) of oscillations in the theta band (4-8 Hz) at the time the probe is shown. This similarity-based interference had a quite different neural signature from proactive interference. In trials with high proactive interference, you confuse the probe because you have recently seen it and cannot remember what list it was on, and this was associated with occipital gamma oscillations.

We are planning to further explore this link between mathematical modeling and brain oscillations. In particular: what is the role of theta oscillations in recognition memory? To address this question, we are currently repeating the study in intracranial EEG, which provides a much better spatial resolution than scalp EEG. Results from this study can be used to refine localization information for the theta and gamma effects. But more importantly, it can be used to study how information flows between brain areas (using statistical analysis tools like Granger causality), which will provide insights that would be impossible to obtain from other neuroimaging tools, because of the unique temporal and spatial resolution of iEEG.