Deep dyslexia is an acquired reading disorder marked by the occurrence of semantic errors (e.g., reading RIVER as "ocean"). In addition, patients exhibit a number of other symptoms, including visual and morphological effects in their errors, a part-of-speech effect, and an advantage for concrete over abstract words. Deep dyslexia poses a distinct challenge for cognitive neuropsychology because there is little understanding of why such a variety of symptoms should co-occur in virtually all known patients. Hinton and Shallice (1991) replicated the co-occurrence of visual and semantic errors by lesioning a recurrent connectionist network trained to map from orthography to semantics. While the success of their simulations is encouraging, there is little understanding of what underlying principles are responsible for them. In this paper we evaluate and, where possible, improve on the most important design decisions made by Hinton and Shallice, relating to the task, the network architecture, the training procedure, and the testing procedure. We identify four properties of networks that underly their ability to reproduce the deep dyslexic symptom-complex: distributed orthographic and semantic representations, gradient descent learning, attractors for word meanings, and greater richness of concrete vs. abstract semantics. The first three of these are general connectionist principles and the last is based on earlier theorizing. Taken together, the results demonstrate the usefulness of a connectionist approach to understanding deep dyslexia in particular, and the viability of connectionist neuropsychology in general.

this article, in conjunction with those developed previously (Plaut et al., 1996; Seidenberg & McClelland, 1989), illustrate how connectionist computational principles---distributed representation, structure-sensitive learning, and interactivity---can provide insight into central empirical phenomena in normal and impaired lexical processing. Moreover, they make it clear that distinctions in the function of the lexical system---as manifest in the behaviour of experimental subjects--- need not re#ect corresponding distinctions in the structure of the system. Thus, networks exhibit word-frequency effects and word/nonword discrimination without word representations, and spelling --sound consistency effects without separate mechanisms for regular and exception items. In this way, gaining insight into the structure and function of the cognitive system by observing its normal and impaired behaviour ---the central goal of cognitive psychology and neuropsycho logy---may depend critically on developing theories and explicit simulations in the context of a speci#c computational framework that relates structure to function

An interactive 2-step theory of lexical retrieval was applied to the picture-naming error patterns of aphasic and nonaphasic speakers. The theory uses spreading activation in a lexical network to accomplish the mapping between the conceptual representation of an object and the phonological form of the word naming the object. A model developed from the theory was parameterized to fit normal error patterns. It was then "lesioned " by globally altering its connection weight, decay rates, or both to provide fits to the error patterns of 21 fluent aphasic patients. These fits were then used to derive predictions about the influence of syntactic categories on patient errors, the effect of phonology on semantic errors, error patterns after recovery, and patient performance on a single-word repetition task. The predictions were confirmed. It is argued that simple quantitative alterations to a normal processing model can explain much of the variety among patient patterns in naming. Difficulty in word retrieval is the most pervasive symptom of language breakdown in aphasia. As with other symptoms of brain damage, word retrieval is subject to graceful degradation (Marr, 1982; Rumelhart & McClelland, 1986): Unsuccessful attempts at retrieval generally resemble the target, either in

This article presents a sobering view of the discipline of cognitive neuropsychology as practised over the last three or four decades. Our judgement is that, although the study of abnormal cognition resulting from brain injury or disease in previously normal adults has produced a catalogue of fascinating and highly selective deficits, it has yielded relatively little advance in understanding how the brain accomplishes its cognitive business. We question the wisdom of the following three ‘choices ’ in mainstream cognitive neuropsychology: (a) single-case methodology, (b) dissociation between functions as the most important source of evidence, and (c) a central goal of diagramming the functional architecture of cognition rather than specifying how its components work. These choices may all stem from an excessive commitment to strict and fine-grained modularity. Although different brain regions are undoubtedly specialised for different functions, we argue that parallel and interactive processing is a better assumption about cognitive processing. The essential goal of specifying representations and processes can, we claim, be significantly assisted by

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Rehabilitation of the damaged brain can foster reconnection of damaged neural circuits; Hebbian learning mechanisms play an important part in this. The authors propose a triage of post-lesion states, depending on the loss of connectivity in particular circuits. A small loss of connectivity will tend to lead to autonomous recovery, whereas a major loss of connectivity will lead to permanent loss of function; for such individuals, a compensatory approach to recovery is required. The third group have potentially rescuable lesioned circuits, but guided recovery depends on providing precisely targeted bottom-up and top-down inputs, maintaining adequate levels of arousal, and avoiding activation of competitor circuits that may suppress activity in target circuits. Empirical data are implemented in a neural network model, and clinical recommendations for the practice of rehabilitation following brain damage are made. The Clinical Problem Many tens of millions of people worldwide suffer brain damage. Hundreds of thousands of professionals treat this brain damage using a variety of rehabilitation therapies. Yet in spite of this enormous endeavor, there is no agreement as to how these therapies work, though it can be concluded that at least some have been shown to be effective (Antonucci et al., 1995; Biitefisch, Hummelsheim,

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single subject experimental design across participants and behaviors in 4 patients with fluent aphasia. Participants received a semantic feature treatment to improve naming of either typical or atypical items within semantic categories, while

constitute learned, internal representations that mediate between inputs and outputs. In this way, the connectionist approach attempts capture the essential computational properties of the vast ensembles of real neuronal elements found in the brain using simulations of smaller networks of more abstract units. By linking neural computation to behavior, the framework enables developmental, cognitive and neurobiological issues to be addressed within a single, integrated formalism. One very important advantage of connectionist models is that they deal explicitly with learning. Though many of these models have focussed predominantly on simulating aspects of adult, rather than childrens reading, many of the models do explicitly consider the process of learning (e.g., Plaut, McClelland, Seidenberg & Patterson, 1996; Seidenberg & McClelland, 1989). In essence, such models instantiate learning as a process as a slow incremental increase in knowledge, represented by increasingly strong and accu