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Open AccessJournal ArticleDOI

Structure and deterioration of semantic memory: a neuropsychological and computational investigation.

TLDR
The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual representations of objects and their verbal descriptions, to understand the structure of impaired performance in patients with selective and progressive impairments of conceptual knowledge.
Abstract
Wernicke (1900, as cited in G. H. Eggert, 1977) suggested that semantic knowledge arises from the interaction of perceptual representations of objects and words. The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual representations of objects and their verbal descriptions. To test the theory, they trained the model to associate names, verbal descriptions, and visual representations of objects. When its inputs and outputs are constructed to capture aspects of structure apparent in attribute-norming experiments, the model provides an intuitive account of semantic task performance. The authors then used the model to understand the structure of impaired performance in patients with selective and progressive impairments of conceptual knowledge. Data from 4 well-known semantic tasks revealed consistent patterns that find a ready explanation in the model. The relationship between the model and related theories of semantic representation is discussed.

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Journal ArticleDOI

Where do you know what you know? The representation of semantic knowledge in the human brain.

TL;DR: A patient with semantic dementia — a neurodegenerative disease that is characterized by the gradual deterioration of semantic memory — was being driven through the countryside to visit a friend and was able to remind his wife where to turn along the not-recently-travelled route.
Journal ArticleDOI

The brain basis of emotion: A meta-analytic review

TL;DR: A meta-analytic summary of the neuroimaging literature on human emotion finds little evidence that discrete emotion categories can be consistently and specifically localized to distinct brain regions, and finds evidence that is consistent with a psychological constructionist approach to the mind.
Journal ArticleDOI

The Representation of Object Concepts in the Brain

TL;DR: Functional neuroimaging of the human brain indicates that information about salient properties of an object is stored in sensory and motor systems active when that information was acquired, suggesting that object concepts are not explicitly represented, but rather emerge from weighted activity within property-based brain regions.
Journal ArticleDOI

The neural and computational bases of semantic cognition

TL;DR: This Review summarizes key findings and issues arising from a decade of research into the neurocognitive and neurocomputational underpinnings of semantic cognition, leading to a new framework that is term controlled semantic cognition (CSC).
Book

Semantic Cognition: A Parallel Distributed Processing Approach

TL;DR: The authors propose that performance in semantic tasks arises through the propagation of graded signals in a system of interconnected processing units, and show how a simple computational model proposed by Rumelhart exhibits a progressive differentiation of conceptual knowledge, paralleling aspects of cognitive development seen in the work of Frank Keil and Jean Mandler.
References
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Book ChapterDOI

Learning internal representations by error propagation

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book

Learning internal representations by error propagation

TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI

Finding Structure in Time

TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.
Book

Cluster Analysis

TL;DR: This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering.
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