Kenneth A. Norman

Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subjects brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.

Modeling Hippocampal and Neocortical Contributions to Recognition Memory: A Complementary-Learning-Systems Approach

The authors present a computational neural-network model of how the hippocampus and medial temporal lobe cortex (MTLC) contribute to recognition memory. The hippocampal component contributes by recalling studied details. The MTLC component cannot support recall, but one can extract a scalar familiarity signal from MTLC that tracks how well a test item matches studied items. The authors present simulations that establish key differences in the operating characteristics of the hippocampal-recall and MTLC-familiarity signals and identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the 2 signals. They also use the model to address the stochastic relationship between recall and familiarity and the effects of partial versus complete hippocampal lesions on recognition.

False recognition in younger and older adults: Exploring the characteristics of illusory memories

Roediger and McDermott (1995) demonstrated that when subjects hear a list of associates to a “theme word” that has itself not been presented, they frequently claim to recollect having heard the nonpresented theme word on the study list. In Experiment 1, we found that asking subjects to explain theirremember responses, by writing down exactly what they remembered about the item’s presentation at study, did not significantly diminish the rate ofremember false alarms to nonpresented theme words. We also found that older adults were relatively more susceptible than younger adults to this false-recognition effect. Subjects’ explanations suggested that both veridical and illusory memories were predominantly composed of associative information as opposed to sensory and contextual detail. In Experiment 2, we obtained quantitative evidence for this conclusion, using a paradigm in which subjects were asked focused questions about the contents of their recollective experience. Lastly, we found that both younger and older adults recalled more sensory and contextual detail in conjunction with studied items than with nonpresented theme words, although these differences were less pronounced in older adults.

Category-Specific Cortical Activity Precedes Retrieval During 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 categorys 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.

Hippocampal and neocortical contributions to memory: advances in the complementary learning systems framework

The complementary learning systems framework provides a simple set of principles, derived from converging biological, psychological and computational constraints, for understanding the differential contributions of the neocortex and hippocampus to learning and memory. The central principles are that the neocortex has a low learning rate and uses overlapping distributed representations to extract the general statistical structure of the environment, whereas the hippocampus learns rapidly using separated representations to encode the details of specific events while minimizing interference. In recent years, we have instantiated these principles in working computational models, and have used these models to address human and animal learning and memory findings, across a wide range of domains and paradigms. Here, we review a few representative applications of our models, focusing on two domains: recognition memory and animal learning in the fear-conditioning paradigm. In both domains, the models have generated novel predictions that have been tested and confirmed.

A context maintenance and retrieval model of organizational processes in free recall

The authors present the context maintenance and retrieval (CMR) model of memory search, a generalized version of the temporal context model of M. W. Howard and M. J. Kahana (2002a), which proposes that memory search is driven by an internally maintained context representation composed of stimulus-related 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, the authors discuss the implications of CMR for their understanding of a broad class of episodic memory phenomena and suggest ways in which this theory may guide exploration of the neural correlates of memory search.

Memory Strength and Repetition Suppression: Multimodal Imaging of Medial Temporal Cortical Contributions to Recognition

Declarative memory permits an organism to recognize stimuli that have been previously encountered, discriminating them from those that are novel. One basis for recognition is item memory strength, which may support the perception of stimulus familiarity. Though the medial temporal lobes are known to be critical for declarative memory, at present the neural mechanisms supporting perceived differences in memory strength remain poorly specified. Here, functional MRI (fMRI) and anatomically constrained magnetoencephalography (MEG) indexed correlates of graded memory strength in the human brain, focusing on medial temporal cortex. fMRI revealed a decrease in medial temporal cortical activation that tracked parametric levels of perceived memory strength. Anatomically constrained MEG current estimates revealed that strength-dependent signal reductions onset within 150-300 ms. Memory strength appears to be rapidly signaled by medial temporal cortex through repetition suppression (activation reductions), providing a basis for the subjective perception of stimulus familiarity or novelty.

Recollection, familiarity, and cortical reinstatement: a multivoxel pattern analysis.

Episodic memory retrieval is thought to involve reinstatement of the neurocognitive processes engaged when an episode was encoded. Prior fMRI studies and computational models have suggested that reinstatement is limited to instances in which specific episodic details are recollected. We used multivoxel pattern-classification analyses of fMRI data to investigate how reinstatement is associated with different memory judgments, particularly those accompanied by recollection versus a feeling of familiarity (when recollection is absent). Classifiers were trained to distinguish between brain activity patterns associated with different encoding tasks and were subsequently applied to recognition-related fMRI data to determine the degree to which patterns were reinstated. Reinstatement was evident during both recollection- and familiarity-based judgments, providing clear evidence that reinstatement is not sufficient for eliciting a recollective experience. The findings are interpreted as support for a continuous, recollection-related neural signal that has been central to recent debate over the nature of recognition memory processes.

False memories and aging

Although memory processes and systems usually operate reliably, they are sometimes prone to distortions and illusions. Here we review evidence indicating that cognitive aging is often associated with increased susceptibility to various kinds of false recollections. Accumulating data indicate that older adults frequently have special difficulties recollecting the source of information, which in turn renders them vulnerable to confusing perceived and imagined experiences, and to related kinds of memory distortions. Evidence from studies of false recall and recognition indicate that older adults are sometimes more likely than younger adults to remember events that never happened, reflecting the influence of indistinct encoding of events and the use of lenient criteria during retrieval. Neuroimaging studies suggest that age-related changes in medial temporal and frontal regions may play a role in the altered functioning of specific encoding and retrieval processes that give rise to memory distortions. Future studies of aging and false memories are likely to provide a promising avenue for illuminating basic mechanisms of memory distortion.

A Neural Network Model of Retrieval-Induced Forgetting

Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, the authors present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular oscillations in feedback inhibition to strengthen weak parts of target memories and to weaken competing memories. The authors use the model to address several puzzling findings relating to RIF, including why retrieval practice leads to more forgetting than simply presenting the target item, how RIF is affected by the strength of competing memories and the strength of the target (to-be-retrieved) memory, and why RIF sometimes generalizes to independent cues and sometimes does not. For all of these questions, the authors show that the model can account for existing results, and they generate novel predictions regarding boundary conditions on these results.