We present a computational neural network model of recognition memory based on the biological structures of the hippocampus and medial temporal lobe cortex (MTLC), which perform complementary learning functions. The hippocampal component of the model contributes to recognition by recalling specific studied details. MTLC can not support recall, but it is possible to extract a scalar familiarity signal from MTLC that tracks how well the test item matches studied items. We present simulations that establish key qualitative differences in the operating characteristics of the hippocampal recall and MTLC familiarity signals, and we identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the two signals. We also use the model to address the stochastic relationship between recall and familiarity (i.e., are they independent), and the effects of partial vs. complete hippocampal

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We present an overview of our computational approach towards understanding the different contributions of the neocortex and hippocampus in learning and memory. The approach is based on a set of principles derived from converging biological, psychological, and computational constraints. The most central principles are that the neocortex employs a slow learning rate and overlapping distributed representations to extract the general statistical structure of the environment, while the hippocampus learns rapidly using separated representations to encode the details of specific events while suffering minimal interference. Additional principles concern the nature of learning (error-driven and Hebbian), and recall of information via pattern completion. We summarize the results of applying these principles to a wide range of phenomena in conditioning, habituation, contextual learning, recognition memory, recall, and retrograde amnesia, and point to directions of current development. 2 Computat...

Memory evolved to supply useful, timely information to the organism’s decision-making systems. Therefore, decision rules, multiple memory systems, and the search engines that link them should have coevolved to mesh in a coadapted, functionally interlocking way. This adaptationist perspective suggested the scope hypothesis: When a generalization is retrieved from semantic memory, episodic memories that are inconsistent with it should be retrieved in tandem to place boundary conditions on the scope of the generalization. Using a priming paradigm and a decision task involving person memory, the authors tested and confirmed this hypothesis. The results support the view that priming is an evolved adaptation. They further show that dissociations between memory systems are not—and should not be—absolute: Independence exists for some tasks but not others. Memory is a gift of nature, the ability of living organisms to retain and to utilize acquired information or knowledge.... Owners of biological memory systems are capable of behaving more appropriately at a later time because of their experiences at an earlier time, a feat not possible for organisms without memory. (Tulving, 1995a, p. 751) If there is one proposition on which all psychologists seem to

Studies on the ecology of animal memory have focused on the benefits of memory while implicitly assuming that there are costs as well. Here I discuss probable costs of memory by relying on knowledge from molecular biology and physiology, which indicates that the maintenance of accurate information in animals is an active and costly process of maintenance and repair. Redundancy probably plays a key role in ensuring a high level of accuracy; its cost is in terms of additional tissue, which increases body mass and energetic expenditure. Examining the magnitude and cost of redundancy at the neurobiological and behavioral levels can help us understand the cost of memory in particular and cognitive abilities in general. � 1999 Academic Press

This essay examines the proposal that psychological essentialism results from a history of natural selection acting on human representation and inference systems. It has been argued that the features that distinguish essentialist representational systems are especially well suited for representing natural kinds. If the evolved function of essentialism is to exploit the rich inductive potential of such kinds, then it must be subserved by cognitive mechanisms that carry out at least three distinct functions: identifying these kinds in the envi - ronment, constructing essentialized representations of them, and constraining inductive infer - ences about kinds. Moreover, there are different kinds of kinds, ranging from nonliving sub - stances to biological taxa to within-species kinds such as sex, and the causal processes that render these categories coherent for the purposes of inductive generalization vary. If the evolved function of essentialism is to support inductive generalization under ignorance of true causes, and if kinds of kinds vary in the implicit assumptions that support valid inductive inferences about them, then we expect different, functionally incompatible modes of essen - tialist thinking for different kinds. In particular, there should be differences in how biological and nonbiological substances, biological taxa, and biological and social role kinds are essen - tialized. The functional differences between these kinds of essentialism are discussed.

It seems to be a common feeling that animals learn to see, and this feeling, together with the reemergence of computer learning paradigms that mimic many forms of human learning, has raised hopes that learning is the key to the computer vision problem. Indeed, it seems clear that Nature does not "program" all our visual capabilities into the genome, and we certainly know that programming a computer with a closed-form solution to the vision problem is a daunting task. This aim of this informal and elementary report (basically a term paper) is to cast doubt on the idea that biological systems learn to see. The complex process of development, beginning at fertilization and ending with a mature individual, could be considered to have genetic ("nature") and learning ("nurture") processes as logical endpoints or opposite poles. This report mostly considers what goes on between those endpoints, and is meant to raise the possibility that some of the least understood processes in biology are re...

Previous research suggests that early performance of amnesic individuals in a probabilistic category learning task is relatively unimpaired. When combined with impaired declarative knowledge, this is taken as evidence for the existence of separate implicit and explicit memory systems. The present study contains a more fine-grained analysis of learning than earlier studies. Using a dynamic lens model approach with plausible learning models, we found that the learning process is indeed indistinguishable between an amnesic and control group. However, in contrast to earlier findings, we found that explicit knowledge of the task structure is also good in both the amnesic and the control group. This is inconsistent with a crucial prediction from the multiple-systems account. The results can be explained from a single system account and previously found differences in later categorization performance can be accounted for by a difference in learning rate. Over the past decades, neuropsychology has shown increasing interest in probabilistic category learning. In a widely-used categorization task, known as the “weather prediction” task (Knowlton, Squire, & Gluck, 1994), the objective is to predict the weather (sunny or rainy) on the basis of four cues (tarot cards with different geometric patterns). Introducing the task as a variant of the medical diagnosis task (Gluck & Bower, 1988), Knowlton et al. (1994) found that, relative to controls, early categorization performance was not impaired in amnesia. Amnesic patients with medial temporal lobe or diencephalic lesions showed apparent normal learning, despite their severe declarative memory problems. One explanation for this finding, favoured by many authors (e.g. Ashby, Alfonso-Reese, Turken, & Waldron,

Our understanding of political phenomena, including political attitudes and sophistication, can be enriched by incorporating the theories and tools of cognitive neuroscience— in particular, the cognitive neuroscience of nonconscious habitual cognition (akin to bicycle riding). From this perspective, different types of informational “building blocks” can be construed from which different types of political attitudes may arise. A reflectionreflexion model is presented that describes how these blocks combine to produce a given political attitude as a function of goals, primes, expertise, and inherent conflict in considerations relevant to the attitude. The ways in which neuroimaging methods can be used to test hypotheses of political cognition are reviewed. KEY WORDS: social cognitive neuroscience, automaticity, habit, political sophistication Scholars since Plato and Aristotle have asked themselves many questions about the intriguingly political nature of the human mind. It is unlikely, however, that many have asked themselves whether political thinking is like riding a bicycle. This isn’t altogether surprising, of course, given that casting a vote and pedaling down the road seem like very different behaviors. Beneath this surface of dissimilarity, however, political thinking and bike riding may frequently depend on flexing a common set of mental “muscles ” that support the formation and expression of habits across a variety of domains (Lieberman, 2000). Political thinking and bicycle riding may seem to be very dissimilar behaviors. But in some circumstances, they may both depend on a common set of mental “muscles ” that support the formation and expression of habits across a variety of domains