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Michael J. Kahana, Ph.D.

Department of Psychology
Suite 302C
3401 Walnut Street
(office) (215)-746-3501
(lab) (215)-746-3500
(fax) (215)-746-6848
email:  kahana@sas.upenn.edu
website: http://memory.psych.upenn.edu
Click here for selected publications since Dr. Kahana's arrival at Penn

RESEARCH INTERESTS

RESEARCH TECHNIQUES

RESEARCH SUMMARY

Research in the Computational Memory Lab (CML) http://memory.psych.upenn.edu revolves around the study of human memory, combining approaches from traditional experimental psychology, computational modeling, and experimental neuroscience. Our research aims to develop and test theories that address both behavioral and physiological data on human memory function.  Much of our physiological data come from human depth electrode recordings, which are taken as part of neurosurgical treatments for drug-resistant epilepsy.  Such intracranial EEG recordings allow us to measure the responses of small groups of neurons (and in some cases individual cells) in the human brain, while patients perform memory tasks.  This approach provides a unique opportunity to study the neurobiology of human cognitive function. We also carry out non-invasive recordings of electrical activity at the scalp (scalp EEG), which provides a excellent compliment to our intracranial EEG (iEEG) research.  Whereas intracranial EEG can only be recorded from clinical populations who may exhibit slightly different brain function, scalp EEG can be recorded from normal, healthy young adults. By comparing the highly localized, artifact-free intracranial potentials with those obtained at the scalp, we are able to learn more about the electrophysiological correlates of memory than we would using either method alone.

The next three sections describe research being conducted in the CML within three specific problem domains: 1) mechanisms of episodic memory, 2) navigational spatial memory, and 3) recognition memory. Given the complexity of human memory, its reliance on a variety of brain structures and mechanisms, and its relevance to so many distinct, yet interrelated, facets of human experience, a multipronged approach to its study seems most profitable. Such an approach allows insights and methods developed within one domain to spill over into the others.

Mechanisms of Episodic Memory

Episodic (or autobiographical) memory is 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. We pioneered the use of conditional probability and latency analysis (Kahana 1996) to examine how participants transition from one recalled word to the next. These techniques quantify the order in which participants recall list items and the inter-response times between successive recalls. Our results enabled us to characterize the nature of semantic (meaning-based) and temporal (time-based) associations in episodic memory and provided a rich new set of findings to constrain theory (Kahana 1996; Chance and Kahana 1997; Howard and Kahana 1999; ?; Howard and Kahana 2002; Kahana and Jacobs 2000; Kahana, Howard, Zaromb, and Wingfield 2002; Addis and Kahana 2004; Kahana and Howard 2004).

Our experimental studies of recall order and inter-response times led to the development of a Temporal Context Model of episodic memory (TCM, Howard & Kahana, 2002). TCM is a distributed memory model that specifies the mechanisms of contextual drift and contextual retrieval. Through the drift mechanism, TCM describes how a temporal code is created by the integration of recently retrieved contextual states.  As such, TCM represents the first formal model of how memories become ‘episodic' (linked to the time when they occurred). TCM also provides an alternative explanation for associative tendencies in recall. Rather than resulting from co-occurrence in short-term memory (the standard earlier view), TCM suggests that these 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 time-scale invariant, as we have observed experimentally (Howard & Kahana, 1999, 2002). The TCM model also accounts for the finding that associations are nearly symmetrical for pairs of items studied together (Rizzuto & Kahana, 2001; Kahana, 2002) but highly asymmetrical for pairs studied as part of a list (Kahana & Caplan, 2002).

Navigation and Spatial Memory

Unlike memory for lists of meaningful items, which has been studied for more than a century, relatively little is known about how humans learn the structure of spatial environments during active navigation. This situation is beginning to change due to the advent of desktop virtual reality, which makes it relatively easy to design controlled virtual worlds and to study how participants navigate through them. While interesting in its own right, studies of virtual navigation have the added benefit of connecting human cognition with a rich literature on the physiology supporting this capacity in lower animals.

In separate studies, we examined whether the two key physiological markers of spatial navigation in rodents might have parallels in the human brain. When rodents navigate through a novel environment recordings of electrical activity from the hippocampus (and nearby brain structures) reveal a striking 4-10 Hz rhythmic oscillation known as the hippocampal theta rhythm. At the same time, certain cells in the hippocampus, termed place cells, increase their rate of activity when particular regions of the space are being traversed. These two phenomena figure prominently in animal models of learning and spatial navigation.

In a series of studies, we have documented the existence and character of the theta rhythm in the human brain as participants learned to navigate through complex virtual environments (Kahana et al., 1999; Caplan et al., 2000, 2001, 2003). These studies provided the first clear demonstration of cognitively-relevant theta rhythms in humans and developed important quantitative techniques used in the subsequent analysis of brain rhythms both by our group and by others.

Recently, we have gone beyond recording aggregate electrical activity from within brain regions to directly examine the behavior of individual brain cells. In collaboration with U.C.L.A. Neurosurgeon Dr. Itzhak Fried, we have identified place cells in the human brain. These cells, which are found primarily in the human hippocampus, become highly active when a given spatial location is being traversed from any direction. We also identified two other cellular responses in the human brain: cells that become active in response to viewing a salient landmark (from any location) and cells that become active when searching for a particular goal location (irrespective of location or view). Finally, we found a large number of cells that represent combinations of these three features. These results were recently by Ekstrom et al. (2003).

Recognition Memory

Recognition memory constitutes a third major focus of research in the CML. Here we are studying what may be considered the simplest of episodic memory tasks. Rather than asking participants to recall a long list of words or navigate through a complex town to find a specific landmark—both of which involve a complex sequence of behaviors— recognition memory tasks require participants to simply judge whether a target item had appeared in a just-presented list of items. In our studies, we have used relatively short lists of consonants, words, pictures, or simple visual textures. Some of these studies have focused on testing theories of the electrophysiological correlates of recognition memory performance (by testing patients with implanted electrodes, as discussed above). Other studies we conducted have focused on developing and testing abstract computational models of the hypothesized mental processes underlying recognition performance.

We found that during a recognition memory task, theta activity was increased during periods of high memory demands (Raghavachari et al, 2001), and furthermore that these oscillations reset immediately following the appearance of each list item, without altering the overall power of the ongoing oscillation (Rizzuto et al., 2003). These studies demonstrated a role for the theta rhythm in verbal (as contrasted with spatial) memory function. More importantly, they also showed that cognitive processes associated with memory encoding and retrieval can act to start and stop ongoing oscillations, suggesting a more direct role of brain waves in memory function than previously suspected (Kahana et al., 2001). Our lab has also 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 & Sekuler, 2002). Our model (termed the noisy exemplar model, or 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.

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