A connectionist approach to word reading, based on the principles of distributed representation, graded learning of statistical structure, and interactivity in processing, has led to the development of explicit computational models which account for a wide range of data on normal skilled reading and on patterns of reading impairment due to brain damage. There have, however, been recent empirical challenges to these models, and the approach in general, relating to the influence of orthographic length on the naming latencies of both (...) normal and dyslexic readers. The current work presents a simulation which generates sequential phonological output in response to written input, and which can refixate the input when encountering difficulty. The normal model reads both words and nonwords accurately, and exhibits an effect of orthographic length and a frequency‐by‐consistency interaction in its naming latencies. When subject to peripheral damage, the model exhibits an increased length effect that interacts with word frequency, characteristic of letter‐by‐letter reading in pure alexia. Although the model is far from a fully adequate account of all the relevant phenomena, it suggests how connectionist models may be extended to provide deeper insight into sequential processes in reading. (shrink)

Page's proposal to stipulate representations in which individual units correspond to meaningful entities is too unconstrained to support effective theorizing. An approach combining general computational principles with domain-specific assumptions, in which learning is used to discover representations that are effective in solving tasks, provides more insight into why cognitive and neural systems are organized the way they are.

We (Patterson & Plaut, 2009) argued that cognitive neuropsychology has had a limited impact on cognitive science due to a nearly exclusive reliance on (a) single‐case studies, (b) dissociations in cognitive performance, and (c) shallow, box‐and‐arrow theorizing, and we advocated adopting a case‐series methodology, considering associations as well as dissociations, and employing explicit computational modeling in studying “how the brain does its cognitive business.” In reply, Coltheart (2010) claims that our concern is misplaced because cognitive neuropsychology is concerned only (...) with studying the mind, in terms of its “functional architecture,” without regard to how this is implemented in the brain. In this response, we do not dispute his characterization of cognitive neuropsychology as it has typically been practiced over the last 40 years, but we suggest that our understanding of brain structure and function has advanced to the point where studying the mind without regard to the brain is unwise and perpetuates the field’s isolation. (shrink)

The search for a universal theory of reading is misguided. Instead, theories should articulate general principles of neural computation that interact with language-specific learning environments to explain the full diversity of observed reading-related phenomena across the world's languages.

This article presents a sobering view of the discipline of cognitive neuropsychology as practiced over the last three or four decades. Our judgment 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 specialized 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 computational modeling which, by its very nature, requires such specification. (shrink)

Rumelhart and McClelland's chapter about learning the past tense created a degree of controversy extraordinary even in the adversarial culture of modern science. It also stimulated a vast amount of research that advanced the understanding of the past tense, inflectional morphology in English and other languages, the nature of linguistic representations, relations between language and other phenomena such as reading and object recognition, the properties of artificial neural networks, and other topics. We examine the impact of the Rumelhart and McClelland (...) model with the benefit of 25 years of hindsight. It is not clear who “won” the debate. It is clear, however, that the core ideas that the model instantiated have been assimilated into many areas in the study of language, changing the focus of research from abstract characterizations of linguistic competence to an emphasis on the role of the statistical structure of language in acquisition and processing. (shrink)

We share with Anderson & Lebiere (A&L) (and with Newell before them) the goal of developing a domain-general framework for modeling cognition, and we take seriously the issue of evaluation criteria. We advocate a more focused approach than the one reflected in Newell's criteria, based on analysis of failures as well as successes of models brought into close contact with experimental data. A&L attribute the shortcomings of our parallel-distributed processing framework to a failure to acknowledge a symbolic level of thought. (...) Our framework does acknowledge a symbolic level, contrary to their claim. What we deny is that the symbolic level is the level at which the principles of cognitive processing should be formulated. Models cast at a symbolic level are sometimes useful as high-level approximations of the underlying mechanisms of thought. The adequacy of this approximation will continue to increase as symbolic modelers continue to incorporate principles of parallel distributed processing. (shrink)

Connectionist models offer concretemechanisms for cognitive processes. When these modelsmimic the performance of human subjects theycan offer insights into the computationswhich might underlie human cognition. We illustratethis with the performance of a recurrentconnectionist network which produces the meaningof words in response to their spellingpattern. It mimics a paradoxical pattern oferrors produced by people trying to read degradedwords. The reason why the network produces thesurprising error pattern lies in the nature ofthe attractors which it develops as it learns tomap spelling patterns (...) to semantics. The keyrole of attractor structure in the successfulsimulation suggests that the normal adult semanticreading route may involve attractor dynamics, andthus the paradoxical error pattern isexplained. (shrink)

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed thesequence encoderis used to learn nearly 75,000 wordform representations through exposure to strings of stress‐marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional slot‐based (...) codes. Then, two large‐scale simulations are reported that learned to represent lexicons of either phonological or orthographic wordforms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well‐formed pseudowords as opposed to ill‐formed (scrambled) pseudowords, and by accounting for variance in well‐formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing. (shrink)

Four pairs of connectionist simulations are presented in which quasi‐regular mappings are computed using localist and distributed representations. In each simulation, a control parameter termed input gain was modulated over the only level of representation that mapped inputs to outputs. Input gain caused both localist and distributed models to shift between regularity‐based and item‐based modes of processing. Performance on irregular items was selectively impaired in the regularity‐based modes, whereas performance on novel items was selectively impaired in the item‐based modes. Thus, (...) the models exhibited double dissociations without separable processing components. These results are discussed in the context of analogous dissociations found in language domains such as word reading and inflectional morphology. (shrink)

Sibley, Kello, Plaut, and Elman (2008) proposed the sequence encoder as a model that learns fixed‐width distributed representations of variable‐length sequences. In doing so, the sequence encoder overcomes problems that have restricted models of word reading and recognition to processing only monosyllabic words. Bowers and Davis (2009) recently claimed that the sequence encoder does not actually overcome the relevant problems, and hence it is not a useful component of large‐scale word‐reading models. In this reply, it is noted that the (...) sequence encoder has facilitated the creation of large‐scale word‐reading models. The reasons for this success are explained and stand as counterarguments to claims made by Bowers and Davis. (shrink)

In 1864 - two years before the publication of The Communist Manifesto and 21 years before the publication of Das Kapital - Karl Marx published an essay titled Peuchet on Suicide. The essay was originally presented as a translation of excerpts from the memoirs of Jacques Peuchet, a leading French police administrator, economist and statistician. Plaut and Anderson reveal that Marx's Peuchet on Suicide is not a straightforward translation, but is an edited version in which Marx adds passages of (...) his own, altering the emphasis of the text from a moral and psychological focus to a profoundly social one. Thus, the essay very strongly reflects Marx's own position on this controversial subject. Sociologist Kevin Anderson provides an extensive introduction situating the essay in the context of Marx's work, especially that on gender; Plaut's essay focuses on the psychological aspects of the work, in particular contrasting Marx's thoughts on suicide with those of Freud and Durkheim. (shrink)

In his assessment of Miliband, Poulantzas objects that instead of attacking orthodox theory by presenting a different “problematic,” or set of theoretical constructs, Miliband opposes concepts with facts, thereby ““placing himself on their own terrain.” The warning is appropriate and is one that I intend to heed in considering Poulantzas's work. I will therefore not present an alternative set of data, nor examine the internal consistency of his categories, although this would represent a legitimate approach. Instead, I intend to examine (...) certain of Poulantzas's key concepts by considering their origins in Althusser and before him in the work of Bachelard. (shrink)

This collection of eight essays plus Introduction challenges many of society's most fundamental ideas about the world and ourselves with reasonable, original alternatives. Written from an unapologetically Jewish viewpoint, these essays will be of interest to any intellectually inquisitive person. Is economic gain a universal drive? Is it reasonable to say that the world is the result of chance occurrences? Does humanism understate the value of humanity? These questions and others are considered.