We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic and phonological representations that capture better the relevant structure among the written and spoken forms of words. In a number of simulation experiments, networks using the new representations learn to read both regular and exception words, including low-frequency exception words, and yet are still able to read pronounceable nonwords as well as skilled readers. A mathematical analysis of the effects of word frequency and spelling-sound consistency in a related but simpler system serves to clarify the close relationship of these factors in influencing naming latencies. These insights are verified in subsequent simulations, including an attractor network that reproduces the naming latency data directly in its time to settle on a response. Further analyses of the network's ability to reproduce data on impaired reading in surface dyslexia support a view of the reading system that incorporates a graded division-of-labor between semantic and phonological processes. Such a view is consistent with the more general Seidenberg and McClelland framework and has some similarities with---but also important differences from---the standard dual-route account.

This article describes a 2-systems model that explains social behavior as a joint function of reflective and impulsive processes. In particular, it is assumed that social behavior is controlled by 2 interacting systems that follow different operating principles. The reflective system generates behavioral decisions that are based on knowledge about facts and values, whereas the impulsive system elicits behavior through associative links and motivational orientations. The proposed model describes how the 2 systems interact at various stages of processing, and how their outputs may determine behavior in a synergistic or antagonistic fashion. It extends previous models by integrating motivational components that allow more precise predictions of behavior. The implications of this reflective–impulsive model are applied to various phenomena from social psychology and beyond. Extending previous dual-process accounts, this model is not limited to specific domains of mental functioning and attempts to integrate cognitive, motivational, and behavioral mechanisms. In the history of attempts to discover the causes of human behavior, the most widespread explanations are based on the assumption that human beings do what they believe is good for them. Thus, they are construed as “rational animals ” capable of recognizing the value or utility of their actions. At the same time, however, it is obvious that human beings do not always act this way; that is, under certain circumstances people behave in ways that do not reflect their values. To account for this phenomenon, to which the Greek philosophers gave the name akrasia (e.g., Mele, 1992), several strategies have been pursued. The first strategy assumes ignorance or lack of knowledge on the part of the actor. Socrates, for example, claimed that if people only knew what is good for them, they would act accordingly. A similar position is held by modern economists who imply that irrational decisions This article received the 2003 Theoretical Innovation Price of the

A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.

A relatively simple model of the phonological loop (A. D. Baddeley, 1986), a component of working memory, has proved capable of accommodating a great deal of experimental evidence from normal adult participants, children, and neuropsychological patients. Until recently, however, the role of this subsystem in everyday cognitive activities was unclear. In this article the authors review studies of word learning by normal adults and children, neuropsychological patients, and special developmental populations, which provide evidence that the phonological loop plays a crucial role in learning the novel phonological forms of new words. The authors propose that the primary purpose for which the phonological loop evolved is to store unfamiliar sound patterns while more permanent memory records are being constructed. Its use in retaining sequences of familiar words is, it is argued, secondary. Baddeley and Hitch (1974) considered the possibility that short-term memory (STM) may serve as a general working memory designed to support complex cognitive activities. This suggestion led to the development of a specific multicomponent model of working memory and has subsequently contributed to

The authors describe a model of autobiographical memory in which memories are transitory mental constructions within a self-memory system (SMS). The SMS contains an autobiographical knowledge base and current goals of the working self. Within the SMS, control processes modulate access to the knowledge base by successively shaping cues used to activate autobiographical memory knowledge structures and, in this way, form specific memories. The relation of the knowledge base to active goals is reciprocal, and the knowledge base "grounds " the goals of the working self. It is shown how this model can be used to draw together a wide range of diverse data from cognitive, social, developmental, personality, clinical, and neuropsychological autobiographical memory research. Autobiographical memory is of fundamental significance for the self, for emotions, and for the experience of personhood, that is, for the experience of enduring as an individual, in a culture, over time. As a consequence autobiographical memory is researched in many different subareas of psychology, for example, cognitive, social, developmental, clinical, and neuropsychology to name only some of the most prominent. However, research findings and

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Two connectionist frameworks, GRAIN (McClelland, 1993) and BSB (Anderson, 1991), and the diffusion model (Ratcliff, 1978) were evaluated using data from a signal detection task. Subjects were asked to choose one of two possible responses to a stimulus and were provided feedback about whether the choice was correct. The dependent variables included response probabilities, reaction times for correct and error responses, and reaction time distributions, and the independent variables were stimulus value, stimulus probability, and lag from an abrupt switch in stimulus probability. The diffusion model accounted for all aspects of the asymptotic data, including error reaction times, which had previously been a problem. The connectionist models accounted for many aspects of the data adequately, but each failed to a greater or lesser degree in important ways except for one model very similar to the diffusion model. The connectionist learning mechanisms were unable to account for initial learning or abrupt changes in stimulus probability. The results provide an advance in the development of the diffusion model and show that the long tradition of reaction time research and theory is a fertile domain for development and testing of connectionist assumptions about how decisions are generated over time.

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

This review organizes a variety of phenomena related to emotional self-report. In doing so, the authors offer an accessibility model that specifies the types of factors that contribute to emotional self-reports under different reporting conditions. One important distinction is between emotion, which is episodic, experiential, and contextual, and beliefs about emotion, which are semantic, conceptual, and decontex-tualized. This distinction is important in understanding the discrepancies that often occur when people are asked to report on feelings they are currently experiencing versus those that they are not currently experiencing. The accessibility model provides an organizing framework for understanding self-reports of emotion and suggests some new directions for research. Emotions are momentary experiences that are intimately tied to the ebb and flow of everyday life, but people also possess gener-representations, whereas emotions are episodic occurrences, emo-tional experience and beliefs about emotion often diverge. This less-than-perfect correspondence has important consequences for understanding emotional self-report. In this article, we explore the

We present a theoretical framework for understanding the roles of the hippocampus and neocortex in learning and memory. This framework incorporates a theme found in many theories of hippocampal function, that the hippocampus is responsible for developing conjunctive representations binding together stimulus elements into a unitary rep- resentation that can later be recalled from partial input cues. This idea appears problematic, however, because it is contradicted by the fact that hippocampally lesioned rats can learn nonlinear discrimination problems that require conjunctive representations. Our framework accommodates this finding by establishing a principled division of labor between the cortex and hippocampus, where the cortex is responsible for slow learning that integrates over multiple experiences to extract generalities, while the hippocampus performs rapid learning of the arbitrary contents of individual experiences. This framework shows that nonlinear discrimination problems are not good tests of hippocampal function, and suggests that tasks involving rapid, incidental conjunctive learning are better. We implement this framework in a computational neural network model, and show that it can account for a wide range of data in animal learning, thus validating our theoretical ideas, and providing a number of insights and predictions about these learning phenomena.