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On language and connectionism: Analysis of a parallel distributed processing model of language acquisition (1988)

by S Pinker, A Prince
Venue:Cognition
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Optimality Theory: Constraint interaction in Generative Grammar

by Alan Prince, Paul Smolensky , 2002
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Abstract - Cited by 2220 (43 self) - Add to MetaCart
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...rg 1991; Jain 1991; Miikkulainen & Dyer 1991; Touretzky & Wheeler 1991; Goldsmith 1992; Wheeler & Touretzky 1993 is a small sample of this now vast literature; critiques include Lachter & Bever 1988, =-=Pinker & Prince 1988-=-). Despite their great variety, almost all of these connectionist approaches to language fall fairly near one or the other of two poles, which can be characterized as follows: (304) Eliminativist conn...

A distributed, developmental model of word recognition and naming

by Mark S. Seidenberg, James L. McClelland - PSYCHOLOGICAL REVIEW , 1989
"... A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonological units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the back-propagatio ..."
Abstract - Cited by 675 (48 self) - Add to MetaCart
A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonological units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the back-propagation learning algorithm. The model simulates many aspects of human performance, including (a) differences between words in terms of processing difficulty, (b) pronunciation of novel items, (c) differences between readers in terms of word recognition skill, (d) transitions from beginning to skilled reading, and (e) differences in performance on lexieal decision and naming tasks. The model's behavior early in the learning phase corresponds to that of children acquiring word recognition skills. Training with a smaller number of hidden units produces output characteristic of many dys-lexic readers. Naming is simulated without pronunciation rules, and lexical decisions are simulated without accessing word-level representations. The performance of the model is largely determined by three factors: the nature of the input, a significant fragment of written English; the learning rule, which encodes the implicit structure of the orthography in the weights on connections; and the architecture of the system, which influences the scope of what can be learned. The recognition and pronunciation of words is one of the cen-
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...et of phoneme triples _mA, mAk, Ak__. 2 Note that we do not claim that this scheme in its present form is fully sufficient for representing all of the letter or phoneme sequences that form words (see =-=Pinker & Prince, 1988-=-). However, we are presently applying the model only to monosyllables, and the representation is sufficient for these (see general discussion). Extensions of the representation scheme can be envisione...

Understanding Normal and Impaired Word Reading: Computational Principles in Quasi-Regular Domains

by David C. Plaut , James L. McClelland, Mark S. Seidenberg, Karalyn Patterson - PSYCHOLOGICAL REVIEW , 1996
"... 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 phono ..."
Abstract - Cited by 583 (94 self) - Add to MetaCart
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.

Recursive Distributed Representations

by Jordan B. Pollack - Artificial Intelligence , 1990
"... A long-standing difficulty for connectionist modeling has been how to represent variable-sized recursive data structures, such as trees and lists, in fixed-width patterns. This paper presents a connectionist architecture which automatically develops compact distributed representations for such compo ..."
Abstract - Cited by 409 (9 self) - Add to MetaCart
A long-standing difficulty for connectionist modeling has been how to represent variable-sized recursive data structures, such as trees and lists, in fixed-width patterns. This paper presents a connectionist architecture which automatically develops compact distributed representations for such compositional structures, as well as efficient accessing mechanisms for them. Patterns which stand for the internal nodes of fixed-valence trees are devised through the recursive use of back-propagation on three-layer autoassociative encoder networks. The resulting representations are novel, in that they combine apparently immiscible aspects of features, pointers, and symbol structures. They form a bridge between the data structures necessary for high-level cognitive tasks and the associative, pattern recognition machinery provided by neural networks. 2 J. B. Pollack 1. Introduction One of the major stumbling blocks in the application of Connectionism to higherlevel cognitive tasks, such as Na...
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...ly by an adaptive network. ############### 2 To point out this "Banana Problem" with Rumelhart & McClelland's actual representation, which was phonological rather than orthographic, Pinker a=-=nd Prince [24]-=- discovered words with enough internal duplication in the Oykangand language. 3 Rosenfeld and Touretzky [26] provide a nice analysis of coarse-coded symbol memories. 6 J. B. Pollack (2) The access mec...

Natural language and natural selection

by Steven Pinker, Paul Bloom - Behavioral and Brain Sciences , 1990
"... Pinker, S. & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences 13 ..."
Abstract - Cited by 373 (3 self) - Add to MetaCart
Pinker, S. & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences 13
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...es in response to pressure on one performance would automatically transfer to the other. 5.2.2. Categorical Rules. Many linguistic rules are categorical, all-or-none operations on symbols (see, e.g., =-=Pinker and Prince, 1988-=-, 1989). How could such structures evolve in a gradual sequence? Bates et al. (1989), presumably echoing Gould's "5% of an eye," (1989) write: What protoform can we possibly envision that could have g...

Word Grammar

by Richard Hudson , 1998
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Abstract - Cited by 253 (12 self) - Add to MetaCart
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The induction of dynamical recognizers

by Jordan B. Pollack - Machine Learning , 1991
"... A higher order recurrent neural network architecture learns to recognize and generate languages after being "trained " on categorized exemplars. Studying these networks from the perspective of dynamical systems yields two interesting discoveries: First, a longitudinal examination of the le ..."
Abstract - Cited by 225 (14 self) - Add to MetaCart
A higher order recurrent neural network architecture learns to recognize and generate languages after being "trained " on categorized exemplars. Studying these networks from the perspective of dynamical systems yields two interesting discoveries: First, a longitudinal examination of the learning process illustrates a new form of mechanical inference: Induction by phase transition. A small weight adjustment causes a "bifurcation" in the limit behavior of the network. This phase transition corresponds to the onset of the network’s capacity for generalizing to arbitrary-length strings. Second, a study of the automata resulting from the acquisition of previously published training sets indicates that while the architecture is not guaranteed to find a minimal finite automaton consistent with the given exemplars, which is an NP-Hard problem, the architecture does appear capable of generating non-regular languages by exploiting fractal and chaotic dynamics. I end the paper with a hypothesis relating linguistic generative capacity to the behavioral regimes of non-linear dynamical systems.
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...ectionists can stumble again and again into the trap of making strong claims for their models, easy to attack for not offering an adequate replacement for established theory. (Fodor & Pylyshyn, 1988; =-=Pinker & Prince, 1988-=-). But it is only because of "long-term lack of competition" that descriptive theories involving rules and representations can be defended as explanatory theories. Here is an alternative hypothesis fo...

Rules and Exemplars in Category Learning

by Michael A. Erickson, John K. Kruschke - Journal of Experimental Psychology: General , 1998
"... haracterized by descriptions of each module and how each serves in those tasks for which it is best suited. However, these theories often do not emphasize how modules interact in producing responses and in learning. In this article we will develop a modular theory of categorization that follows fro ..."
Abstract - Cited by 195 (11 self) - Add to MetaCart
haracterized by descriptions of each module and how each serves in those tasks for which it is best suited. However, these theories often do not emphasize how modules interact in producing responses and in learning. In this article we will develop a modular theory of categorization that follows from two distinct accounts of this behavior. The first account is that of rule-based theories of categorization. These theories emerge from a philosophical tradition in which concepts and categorization are described in terms of definitional rules. For example, if a living thing has a wide, flat tail and constructs dams by cutting down trees with its This work was supported by Indiana University Cognitive Science Program Fellowships and by NIMH ResearchTraining Grant PHS-T32-MH19879-03 to Erickson, and in part by NIMH FIRST Award 1-R29-MH51572-01 to Kruschke. This research was reported as a poster at the 1996 Cognitive Science Society Conference in San Diego, CA. We than

Deep Dyslexia: A Case Study of Connectionist Neuropsychology

by David C. Plaut, Tim Shallice , 1993
"... 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 co ..."
Abstract - Cited by 190 (29 self) - Add to MetaCart
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.

Rules vs. analogy in English past tenses: a computational/experimental study

by Adam Albright , Bruce Hayes , 2003
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Abstract - Cited by 178 (19 self) - Add to MetaCart
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...c knowledge is best characterized by rules: • Because they contain variables, rules permit correct outputs to be derived even for unusual input forms that lack neighbors (the central argument made by =-=Pinker and Prince, 1988-=-). • Rules can form very tight systems that avoid overgeneration (*renderèd, *whispert). • Rules limit themselves to structured similarity, and cannot access variegated similarity. Our tentative concl...

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