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 paper describes a novel theoretical framework of how the position of a letter within a string is encoded, the SERIOL model (sequential encoding regulated by inputs to oscillations within letter units). Letter order is represented by a temporal activation pattern across letter units, as is consistent with current theories of information coding based on the precise timing of neural spikes. The framework specifies how this pattern is invoked via an activation gradient that interacts with subthreshold oscillations and how it is decoded via contextual units that activate word units. Using mathematical modeling, this theoretical framework is shown to account for the experimental data from a wide variety of string-processing studies, including hemispheric asymmetries, the optimal viewing position, and positional priming effects

This book chapter examines the role of spatial attention from a computational perspective. It is intended as an overview for cognitive scientists interested in computational modeling of attentional phenomena. Because the function of attention can be understood only in its relation to visual information processing, we model not only the attentional system itself, but also the process of object recognition. We begin by presenting a basic model of object recognition, showing that interference prevents the system from reliably processing multiple, complex stimuli, and then we show how a simple mechanism of attentional selection can reduce this interference. Our first goal is to present a model that is computationally adequate, that is, a model that has the computational power to perform the sort of visual information processing tasks that people do. We then turn to simulations showing that the model can account for diverse experimental data, including: the benefit of attentional precuing, the time course of attention shifts, the effect of spatial uncertainty, the effect of irrelevant stimuli, the relation of object-based and location-based selection, and visual search. We conclude with a discussion of basic questions about computation modeling, including: Why build computational models? What makes a model compelling? When is a model right or wrong? Should one opt for depth or breadth in model coverage?

Networks that learn to make familiar activity patterns into stable attractors have proven useful in accounting for many aspects of normal and impaired cognition. However, their ability to generalize is questionable, particularly in quasiregular tasks that involve both regularities and exceptions, such as word reading. We trained an attractor network to pronounce virtually all of a large corpus of monosyllabic words, including both regular and exception words. When tested on the lists of pronounceable nonwords used in several empirical studies, its accuracy was closely comparable to that of human subjects. The network generalizes because the attractors it developed for regular words are componential---they have substructure that reflects common sublexical correspondences between orthography and phonology. This componentiality is faciliated by the use of orthographic and phonological representations that make explicit the structured relationship between written and spoken words. Furthe...

Connectionist modeling offers a useful computational framework for exploring the nature of normal and impaired cognitive processes. The current work extends the relevance of connectionist modeling in neuropsychology to address issues in cognitive rehabilitation: the degree and speed of recovery through retraining, the extent to which improvement on treated items generalizes to untreated items, and how treated items are selected to maximize this generalization. A network previously used to model impairments in mapping orthography to semantics is retrained after damage. The degree of relearning and generalization varies considerably for different lesion locations, and has interesting implications for understanding the nature and variability of recovery in patients. In a second simulation, retraining on words whose semantics are atypical of their category yields more generalization than retraining on more typical words, suggesting a counterintuitive strategy for selecting items in patient therapy to maximize recovery. In a final simulation, changes in the pattern of errors produced by the network over the course of recovery is used to constrain explanations of the nature of recovery of analogous brain-damaged patients. Taken together, the findings demonstrate that the nature of relearning in damaged connectionist networks can make important contributions to a theory of rehabilitation in patients.

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lligan, 1991, Driver et al. , 1992, Young et al, 1992; Arguin & Bub, 1993, Halligan & Marshall, 1994; Walker, 1995, Humphreys et al. , 1996). Such ndings have often been interpreted (e.g. see Vallar, 1998) as indicating 'allocentric object-centered' neglect, as opposed to neglect within egocentric representations of space A recent study by Pavlovskaya et al. (1997) made strong new claims in this respect. Here we argue that their data, and likewise many previous reports of putatively 'object-centred' neglect, can actually be explained in purely egocentric terms, provided that relative egocentric position matters in addition to absolute egocentric position (Driver et al., 1994, Pouget & Sejnowski, 1997; Driver, 1998). The reason for this is illustrated in Figure 1. Fig 1A represents the popular notion of an egocentric gradient of impairment in neglect following right parietal injury (Kinsbourne, 1987; Mozer & Behrmann, 1990; Driver et al. , 1994; Anderson, 1996; Pouget & Sejno

The basis function theory of spatial representations explains how neurons i n the parietal cortex can perform nonlinear transformations from sensory to motor coordinates. The authors present computer simulations showing that unilateral parietal lesions leading to a neuronal gradient in basis function maps can account for the behavior of patients with hemineglect, including (a) neglect in line cancellation and line bisection experiments; (b) neglect in multiple frames of reference simultaneously; (c) relative neglect, a form of what is sometime called object-centered neglect; and (d) neglect without optic ataxia. Contralateral neglect arises in the model because the lesion produces an imbalance in the salience of stimuli that is modulated by the orientation of the body in space. These results strongly support the basis function theory for spatial representations in humans and provide a computational model of hemineglect at the single-cell level. A unilateral lesion of the parieto-occipital cortex in humans often produces hemineglect (Heilman, Watson, & Valenstein, 1985; Pouget & Driver, 1999; Vallar, 1998), a neurologic syndrome characterized by a conspicuous inability to react or respond to stimuli presented in the hemispace contralateral to the lesion. For example, when asked to

Dividing attention across multiple words occasionally results in misidentifications whereby letters apparently migrate between words. Previous studies have found that letter migrations preserve withinword letter position, which has been interpreted as support for position-specific letter coding. To investigate this issue, the authors used word pairs like STEP and SOAP, in which a letter in 1 word could migrate to an adjacent letter in another word to form an illusory word (STOP). Three experiments show that both same-position and adjacent-position letter migrations can occur, as well as migrations that cross 2 letter positions. These results argue against position-specific letter coding schemes used in many computational models of reading, and they provide support for coding schemes based on relative rather than absolute letter position. A key issue that must be addressed in any theory of visual word recognition is how to code for letter position: Without coding of position, it is not possible to distinguish anagrams like CAT and ACT. Although relatively little empirical work has been directed at assessing the relative merits of different letter coding schemes, the choice of coding scheme plays a central role in the performance of