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We present the Context Maintenance and Retrieval (CMR) model of memory search, a generalized version of the temporal context model (TCM) of Howard and Kahana (2002a), which proposes that memory search is driven by an internally maintained context representation composed of stimulusrelated and source-related features. In the CMR model, organizational effects (the tendency for related items to cluster during the recall sequence) arise as a consequence of associations between active context elements and features of the studied material. Semantic clustering is due to longstanding context-to-item associations, whereas temporal clustering and source clustering are both due to associations formed during the study episode. A behavioral investigation of the three forms of organization provides data to constrain the CMR model, revealing interactions between the organizational factors. Finally, we discuss the implications of CMR for our understanding of a broad class of episodic memory phenomena, and suggest ways in which this theory may guide our exploration of the neural correlates of memory search.

Here we describe a functional magnetic resonance imaging study of humans engaged in memory search during a free recall task. Patterns of cortical activity associated with the study of three categories of pictures (faces, locations, and objects) were identified by a pattern-classification algorithm. The algorithm was used to track the reappearance of these activity patterns during the recall period. The reappearance of a given category's activity pattern correlates with verbal recalls made from that category and precedes the recall event by several seconds. This result is consistent with the hypothesis that category-specific activity is cueing the memory system to retrieve studied items.

The human memory system is remarkable in its capacity to focus its search on items learned in a given context. This capacity can be so precise that many leading models of human memory assume that only those items learned in the context of a recently studied list compete for recall. We sought to extend the explanatory scope of these models to include not only intralist phenomena, such as primacy and recency effects, but also interlist phenomena such as proactive and retroactive interference. Building on retrieved temporal context models of memory search (e.g., Polyn, Norman, & Kahana, 2009), we present a substantially revised theory in which 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 nontarget list. We show how the resulting model can simultaneously account 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). In a new experiment, we verify that subjects' error monitoring processes are consistent with those predicted by the model.

In many species, spatial navigation is supported by a network of place cells that exhibit increased firing whenever an animal is in a certain region of an environment. Does this neural representation of location form part of the spatiotemporal context into which episodic memories are encoded? We recorded medial temporal lobe neuronal activity as epilepsy patients performed a hybrid spatial and episodic memory task. We identified place-responsive cells active during virtual navigation and then asked whether the same cells activated during the subsequent recall of navigation-related memories without actual navigation. Place-responsive cell activity was reinstated during episodic memory retrieval. Neuronal firing during the retrieval of each memory was similar to the activity that represented the locations in the environment where the memory was initially encoded.

Psychological theories of memory posit that when people recall a past event, they not only recover the features of the event itself, but also recover information associated with other events that occurred nearby in time. The events surrounding a target event, and the thoughts they evoke, may be considered to represent a context for the target event, helping to distinguish that event from similar events experienced at different times. The ability to reinstate this contextual information during memory search has been considered a hallmark of episodic, or event-based, memory. We sought to determine whether context reinstatement may be observed in electrical signals recorded from the human brain during episodic recall. 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. The degree to which individual patients displayed this neural signature of context reinstatement was correlated with their tendency to recall neighboring list items successively. These effects were particularly strong in temporal lobe recordings. Our findings show that recalling a past event evokes a neural signature of the temporal context in which the event occurred, thus pointing to a neural basis for episodic memory.EEG | electrocorticography | oscillations | free recall | contiguity T he pivotal distinction between memory for facts (semantic memory) and memory for episodes or experiences (episodic memory) has been argued to reflect, at least in part, the reinstatement of a gradually changing context representation that reflects not only external conditions, but also an ever-changing internal context state (1, 2). According to this view, the unique quality of episodic memory is that in remembering an episode, we partially recover its associated mental context, and that this context information conveys some sense of when the experience took place, in terms of its relative position along our autobiographical time line.A number of laboratory memory tasks rely on episodic memory, including experimenter-cued tasks (e.g., item recognition and cued recall) and self-cued tasks (e.g., free recall). Performing these episodic memory tasks requires distinguishing the current list item from the rest of one's experience. According to early theories of episodic memory (e.g., 3, 4), context representations are composed of many features that fluctuate from moment to moment, gradually drifting through a multidimensional feature space. These contextual features may reflect environmental cues, recently studied items, participants' internal mental states, or may evolve randomly over time. During recall, the context representation forms part of th...

A challenge for theories of episodic memory is to determine how we focus memory search on a set of recently learned items. Cognitive theories suggest that the recall of an item representation is driven by an internally maintained context representation that integrates incoming information with a long time-scale. Neural investigations have shown that recalling an item revives the pattern of brain activity present during its study. To link these neural and cognitive approaches, we propose a framework in which context is maintained and updated in prefrontal cortex, and is associated with item information through hippocampal projections. The proposed framework is broadly consistent with neurobiological studies of temporal integration and with studies of memory deficits in individuals with prefrontal damage.

We present a new learning algorithm that leverages oscillations in the strength of neural inhibition to train neural networks. Raising inhibition can be used to identify weak parts of target memories, which are then strengthened. Conversely, lowering inhibition can be used to identify competitors, which are then weakened. To update weights, we apply the Contrastive Hebbian Learning equation to successive time steps of the network. The sign of the weight change equation varies as a function of the phase of the inhibitory oscillation. We show that the learning algorithm can memorize large numbers of correlated input patterns without collapsing and that it shows good generalization to test patterns that do not exactly match studied patterns.