According to the parallel distributed processing approach to semantic memory,

Published online by Cambridge University Press: 05 November 2012

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The Cognitive Neuroscience of Semantic Memory
Semantic memory and language processing: a primer.
Semantic Cognition: A Parallel Distributed Processing Approach
Finding Structure in Time
What is the parallel distributed process of memory?
What is the parallel distributed processing model in memory psychology?
Which is the best description of semantic memory?
How does the parallel distributed processing approach explain learning?

Ken McRaeAffiliation:

University of Western Ontario

Marc JoanisseAffiliation:

University of Western Ontario

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The Cognitive Neuroscience of Semantic Memory

Psychology, Biology
2012

Evidence that conceptual knowledge about concrete objects is acquired through experience with them, distributed across brain regions that are involved in perceiving or acting upon them, and impaired via damage to these brain regions is reviewed.

Semantic memory and language processing: a primer.

Sharon M. AntonucciJamie Reilly
Psychology
Seminars in speech and language
2008

It is argued that treatment specificity demands a comprehensive understanding of the structure of semantic memory and the nature of its compromise, and several neuroanatomically informed theories of semantic organization with respect to the effects of semantic impairment on language processing in aphasia and neurodegenerative disease are reviewed.

SHOWING 1-10 OF 152 REFERENCES

Semantic Cognition: A Parallel Distributed Processing Approach

T. RogersJames L. McClelland
Psychology
2004

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.

Finding Structure in Time

J. Elman
Psychology
Cogn. Sci.
1990

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.

References

Quillian, M. R. in Semantic Information Processing (ed. Minsky, M.) 227–270 (MIT Press, Cambridge, Massachusetts, 1968).
Google Scholar
Warrington, E. K. The selective impairment of semantic memory. Q. J. Exp. Psychol. 27, 635–657 (1975). Classic paper first reporting the syndrome that is now known as semantic dementia.
Article CAS PubMed Google Scholar
Snowden, J. S., Goulding, P. J. & Neary, D. Semantic dementia: a form of circumscribed cerebral atrophy. Behav. Neurol. 2, 167–182 (1989).
Article Google Scholar
Hodges, J. R., Graham, N. & Patterson, K. Charting the progression in semantic dementia: implications for the organisation of semantic memory. Memory 3, 463–495 (1995).
Article CAS PubMed Google Scholar
Bozeat, S., Lambon-Ralph, M. A., Patterson, K. & Hodges, J. R. When objects lose their meanings: what happens to their use? Cogn. Affect. Behav. Neurosci. (in the press).
Rogers, T. T., McClelland, J. L. Patterson, K., Lambon-Ralph, M. A. & Hodges, J. R. A recurrent connectionist model of semantic dementia. Cogn. Neurosci. Soc. Annual Meeting <http://cognet.mit.edu/posters/poster.tcl?publication_id=3664> (1999).
Keil, F. C. Semantic and Conceptual Development: An Ontological Perspective (Harvard Univ. Press, Cambridge, Massachusetts, 1979).
Book Google Scholar
Mandler, J. M. How to build a baby: II. Conceptual primitives. Psychol. Rev. 99, 587–604 (1992).
Article CAS PubMed Google Scholar
Mandler, J. M. Perceptual and conceptual processes in infancy. J. Cogn. Dev. 1, 3–36 (2000). Reviews 10 years of research on progressive differentiation of conceptual knowledge in infancy.
Article Google Scholar
Mandler, J. M., Bauer, P. J. & McDonough, L. Separating the sheep from the goats: differentiating global categories. Cogn. Psychol. 23, 263–298 (1991).
Article Google Scholar
Mandler, J. M. & McDonough, L. Concept formation in infancy. Cogn. Dev. 8, 291–318 (1993).
Article Google Scholar
Pauen, S. The global-to-basic shift in infants' categorical thinking: first evidence from a longitudinal study. Int. J. Behav. Dev. 26, 492–499 (2002).
Article Google Scholar
Rakison, D. in Early Concept and Category Development: Making Sense of the Blooming, Buzzing Confusion (eds Oakes, L. M. & Rakison, D. H.) (Oxford Univ. Press, New York, in the press).
Mareschal, D. Infant object knowledge: current trends and controversies. Trends Cogn. Sci. 4, 408–416 (2000).
Article CAS PubMed Google Scholar
Quinn, P. C. in Blackwell Handbook of Childhood Cognitive Development (ed. Goswami, U.) 84–101 (Blackwell, Oxford, UK, 2002).
Book Google Scholar
Collins, A. M. & Quillian, M. R. Retrieval time from semantic memory. J. Verbal Learn. Verbal Behav. 8, 240–248 (1969).
Article Google Scholar
McCloskey, M. & Glucksberg, S. Decision processes in verifying category membership statements: implications for models of semantic memory. Cogn. Psychol. 11, 1037 (1979).
Article Google Scholar
Murphy, G. L. & Brownell, H. H. Category differentiation in object recognition: typicality constraints on the basic category advantage. J. Exp. Psychol. Learn. Mem. Cogn. 11, 70–84 (1985).
Article CAS PubMed Google Scholar
Rips, L. J., Shoben, E. J. & Smith, E. E. Semantic distance and the verification of semantic relations. J. Verbal Learn. Verbal Behav. 12, 1–20 (1973).
Article Google Scholar
Rosch, E. Cognitive representations of semantic categories. J. Exp. Psychol. Gen. 104, 192–233 (1975).
Article Google Scholar
Rumelhart, D. E. & Ortony, A. in Schooling and the Acquisition of Knowledge (eds Anderson, R. C., Sprio, R. J. & Montague, W. E.) 99–135 (Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1976).
Google Scholar
Rumelhart, D. E. & Abrahamson, A. A. A model of analogical reasoning. Cogn. Psychol. 5, 1–28 (1973).
Article Google Scholar
Anderson, J. R. The Adaptive Character of Thought (Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1990).
Google Scholar
Heit, E. in Rational Models of Cognition (eds Oaksford, M. & Chater, N.) 248–274 (Oxford Univ. Press, Oxford, 1998).
Google Scholar
Heit, E. Properties of inductive reasoning. Psychon. Bull. Rev. 7, 569–592 (2000).
Article CAS PubMed Google Scholar
Medin, D. L. & Shaffer, M. M. Context theory of classification learning. Psychol. Rev. 85, 207–238 (1978).
Article Google Scholar
McClelland, J. L. Retrieving general and specific information from stored knowledge of specifics. Proc. Third Annu. Conf. Cogn. Sci. Soc. 170–172 (1981).
Nosofsky, R. M. Attention, similiarity and the identification-categorization relationship. J. Exp. Psychol. Learn. Mem. Cogn. 115, 39–57 (1986).
CAS Google Scholar
Kruschke, J. K. ALCOVE: an exemplar-based connectionist model of category learning. Psychol. Rev. 99, 22–44 (1992).
Article CAS PubMed Google Scholar
Nosofsky, R. M. & Palmeri, T. J. An exemplar-based random-walk model of speeded classification learning. Psychol. Rev. 104, 266–300 (1997).
Article CAS PubMed Google Scholar
Murphy, G. L. The Big Book of Concepts (MIT Press, Cambridge, Massachusetts, 2002). An up-to-date review of the literature on concepts and semantic cognition.
Book Google Scholar
Rumelhart, D. E., McClelland, J. L. & the PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations (MIT Press, Cambridge, Massachusetts, 1986). Introduces the parallel distributed processing framework. Chapter 6 describes back-propagation, which is briefly presented in reference 35.
Book Google Scholar
Hinton, G. E. in Parallel Models of Associative Memory (eds Hinton, G. E. & Anderson, J. A.) 161–187 (Erlbaum, Hillsdale, New Jersey, 1981).
Google Scholar
Ackley, D. H., Hinton, G. E. & Sejnowski, T. J. A learning algorithm for Boltzmann machines. Cogn. Sci. 9, 147–169 (1985).
Article Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

Article Google Scholar
Williams, R. J. & Zipser, D. A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1, 270–280 (1989).
Article Google Scholar
Hinton, G. E. & McClelland, J. L. in Neural Information Processing Systems (ed. Anderson, D. Z.) 358–366 (American Institute of Physics, New York, 1988).
Google Scholar
Mazzoni, P., Andersen, R. A. & Jordan, M. I. A more biologically plausible learning rule for neural networks. Proc. Natl. Acad. Sci. USA 88, 4433–4437 (1991).
Article CAS PubMed PubMed Central Google Scholar
O'Reilly, R. C. Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Comput. 8, 895–938 (1996).
Article Google Scholar
Devlin, J. T., Gonnerman, L. M., Andersen, E. S. & Seidenberg, M. S. Category specific semantic deficits in focal and widespread brain damage: a computational account. J. Cogn. Neurosci. 10, 77–94 (1998).
Article CAS PubMed Google Scholar
Farah, M. J. & McClelland, J. L. A computational model of semantic memory impairment: modality-specificity and emergent category-specificity. J. Exp. Psychol. Gen. 120, 339–357 (1991).
Article CAS PubMed Google Scholar
Gotts, S. & Plaut, D. C. The impact of synaptic depression following brain damage: a connectionist account of 'access/refractory' and 'degraded-store' semantic impairments. Cogn. Affect. Behav. Neurosci. 2, 187–213 (2002).
Article PubMed Google Scholar
Hinton, G. E. in Parallel Distributed Processing: Implications for Psychology and Neurobiology (ed. Morris, R. G. M.) 46–61 (Clarendon, Oxford, 1989).
Google Scholar
Hinton, G. E. & Shallice, T. Lesioning a connectionist network: investigations of acquired dyslexia. Technical Report No. CRG-TR-89–3 (University of Toronto, Department of Computer Science, Toronto, Ontario, Canada, 1989).
Lambon Ralph, M. A., McClelland, J. L., Patterson, K., Galton, C. J. & Hodges, J. R. No right to speak? The relationship between object naming and semantic impairment: neuropsychological evidence and a computational model. J. Cogn. Neurosci. 13, 341–356 (2001).
Article CAS PubMed Google Scholar
McRae, K., de Sa, V. R. & Seidenberg, M. S. On the nature and scope of featural representations of word meaning. J. Exp. Psychol. Gen. 126, 99–130 (1997).
Article CAS PubMed Google Scholar
Plaut, D. C. Graded modality-specific specialization in semantics: a computational account of optic aphasia. Cogn. Neuropsychol. 19, 603–639 (2002).
Article PubMed Google Scholar
Plaut, D. C. & Booth, J. R. Individual and developmental differences in semantic priming: empirical and computational support for a single-mechanism account of lexical processing. Psychol. Rev. 107, 786–823 (2000).
Article CAS PubMed Google Scholar
Plaut, D. C. & Shallice, T. Deep dyslexia: a case study of connectionist neuropsychology. Cogn. Neuropsychol. 10, 377–500 (1993).
Article Google Scholar
Tyler, L. K., Moss, H. E., Durrant-Peatfield, M. R. & Levy, J. P. Conceptual structure and the structure of concepts: a distributed account of category-specific deficits. Brain Lang. 75, 195–231 (2000).
Article CAS PubMed Google Scholar
Tyler, L. K. & Moss, H. E. Towards a distributed account of conceptual knowledge. Trends Cogn. Sci. 5, 244–252 (2001).
Article PubMed Google Scholar
Mareschal, D., French, R. M. & Quinn, P. C. A connectionist account of asymmetric category learning in early infancy. Dev. Psychol. 36, 635–645 (2000).
Article PubMed Google Scholar
Mareschal, D., Plunkett, K. & Harris, P. A computational and neuropsychological account of object-oriented behaviours in infancy. Dev. Sci. 2, 306–317 (1999).
Article Google Scholar
McClelland, J. L. in Parallel Distribured Processing: Implications for Psychology and Neurobiology (Morris, R. G. M.) 8–45 (Oxford Univ. Press, New York, 1989).
Google Scholar
Munakata, Y., McClelland, J. L., Johnson, M. H. & Siegler, R. S. Rethinking infant knowledge: toward an adaptive process accout of successes and failures in object permanence tasks. Psychol. Rev. 104, 686–713 (1997).
Article CAS PubMed Google Scholar
Munakata, Y. & McClelland, J. L. Connectionist models of development. Dev. Sci. (in the press).
Quinn, P. C. & Johnson, M. H. The emergence of perceptual category representations in young infants: A connectionist analysis. J. Exp. Child Psychol. 66, 236–263 (1997).
Article CAS PubMed Google Scholar
Schyns, P. G. A modular neural network model of concept acquisition. Cogn. Sci. 15, 461–508 (1991).
Article Google Scholar
Shultz, T. R., Mareschal, D. & Schmidt, W. C. Modeling cognitive development of balance scale phenomena. Machine Learn. 16, 57–86 (1994).
Google Scholar
Rumelhart, D. E. in An Introduction to Neural and Electronic Networks (eds Zornetzer, S. F., Davis, J. L. & Lau, C.) 405–420 (Academic, New York, 1990).
Google Scholar
Rumelhart, D. E. & Todd, P. M. in Attention and Performance XIV: Synergies in Experimental Psychology, Artifical Intelligence, and Cognitive Neuroscience (eds Meyer, D. E. & Kornblum, S.) 3–30 (MIT Press, Cambridge, Massachusetts, 1993).
Google Scholar
Flavell, J. The Developmental Psychology of Jean Piaget (Van Nostrand, New York, 1963).
Book Google Scholar
McClelland, J. L., McNaughton, B. L. & O'Reilly, R. C. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419–457 (1995). Elaborates the complementary learning system theory that is described briefly in the current article.
Article PubMed Google Scholar
Quinn, P. C. & Johnson, M. H. Global-before-basic object categorization in connectionist networks and 2-month-old infants. Infancy 1, 31–46 (2000).

Article PubMed Google Scholar
Bloom, P. How Children Learn the Meaning of Words (MIT Press, Cambridge, Massachusetts, 2000).
Book Google Scholar
McCloskey, M. & Cohen, N. J. in The Psychology of Learning and Motivation (ed. Bower, G. H.) 109–165 (Academic, New York, 1989).
Google Scholar
Marr, D. Simple memory: a theory for archicortex. Phil. Trans. R. Soc. Lond. B 262, 23–81 (1971).
Article CAS Google Scholar
Milner, P. A cell assembly theory of hippocampal amnesia. Neuropsychologia 27, 23–30 (1989).
Article CAS PubMed Google Scholar
Rolls, E. T. in An Introduction to Neural and Electronic Networks (eds Zornetzer, S. F., Davis, J. L. & Lau, C.) 73–90 (Academic, San Diego, California, 1990).
Google Scholar
Alvarez, P. & Squire L. R. Memory consolidation and the medial temporal lobe: a simple network model. Proc. Natl Acad. Sci. USA 91, 7041–7045 (1994).
Article CAS PubMed PubMed Central Google Scholar
Scoville, W. B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurosurg. Neurol. Psychiatry 20, 11–21 (1957).
Article CAS Google Scholar
Squire, L. R. Memory and the hippocampus: a synthesis from findings with rats, monkeys and humans. Psychol. Rev. 99, 195–231 (1992). A useful overview of amnesia resulting from lesions to the medial temporal lobes.
Article CAS PubMed Google Scholar
Nadel, L. & Moscovitch, M. Memory consolidation, retrograde amnesia and the hippocampal complex. Curr. Opin. Neurobiol. 7, 217–227 (1997).
Article CAS PubMed Google Scholar
Cipolotti, L. et al. Long-term retrograde amnesia: the crucial role of the hippocampus. Neuropsychologia 39, 151–172 (2001).
Article CAS PubMed Google Scholar
Norman, K. A. & O'Reilly, R. C. Modeling hippocampal and neocortical contributions to recognition memory: a complementary learning systems approach. Psychol. Rev. (in the press).
Hodges, J. R., Patterson, K., Oxbury, S. & Funnell, E. Semantic dementia: progressive fluent aphasia with temporal lobe atrophy. Brain 115, 1783–1806 (1992).
Article PubMed Google Scholar
Garrard, P. & Hodges, J. R. Semantic dementia: clinical, radiological, and pathological perspectives. J. Neurol. 247, 409–422 (2000).
Article CAS PubMed Google Scholar
Rogers, T. T. & McClelland, J. L. Semantic Cognition: A Parallel Distributed Processing Approach (MIT Press, Cambridge, Massachusetts, in the press). A full presentation of the PDP approach.
Mervis, C. B. in Concepts and Conceptual Development: Ecological and Intellectual Factors in Categorization (ed. Neisser, U.) 201–233 (Cambridge Univ. Press, Cambridge, 1987).
Google Scholar
Jolicoeur, P., Gluck, M. & Kosslyn, S. M. Pictures and names: making the connection. Cogn. Psychol. 19, 31–53 (1984).
Google Scholar
Rosch, E., Mervis, C. B., Gray, W., Johnson, D. & Boyes-Braem, P. Basic objects in natural categories. Cogn. Psychol. 8, 382–439 (1976).
Article Google Scholar
Brown, R. A First Language (Harvard Univ. Press, Cambridge, Massachusetts, 1973).
Book Google Scholar
MacWhinney, B. The CHILDES Project: Tools for Analyzing Talk (Lawrence Erlbaum Associates, Mahwah, New Jersey, 2000).
Google Scholar
Carey, S. Conceptual Change in Childhood (MIT Press, Cambridge, Massachusetts, 1985). Seminal studies of conceptual reorganization in children.
Google Scholar
Keil, F. C. in The Epigenesis of Mind: Essays on Biology and Cognition (eds Carey, S. & Gelman, R.) 237–256 (Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1991).
Google Scholar
Murphy, G. L. & Medin, D. L. The role of theories in conceptual coherence. Psychol. Rev. 92, 289–316 (1985).
Article CAS PubMed Google Scholar
Gopnik, A. & Wellman, H. M. in Mapping the Mind: Domain Specificity in Cognition and Culture (eds Hirschfeld, L. A. & Gelmans, S. A.) (Cambridge Univ. Press, Cambridge, 1994). One of the most explicit attempts to articulate the nature of 'theory theory'.
Google Scholar
Wilson, R. A. & Keil, F. C. in Explanation and Cognition (eds Keil, F. C. & Wilson, R. A.) 87–114 (MIT Press, Boston, Massachusetts, 2000).
Google Scholar
Gelman, R. First principles organize attention to and learning about relevant data: Number and the animate/inanimate distinction as examples. Cogn. Sci. 14, 79–106 (1990).
Article Google Scholar
Macario, J. F. Young children's use of color in classification: Foods and canonically colored objects. Cogn. Dev. 6, 17–46 (1991).
Article Google Scholar
Gelman, S. A. & Markman, E. M. Categories and induction in young children. Cognition 23, 183–209 (1986).
Article CAS PubMed Google Scholar
Kuhn, T. The Structure of Scientific Revolutions (Univ. Chicago Press, Chicago, 1962).
Google Scholar
Ahn, W. -K. Why are different features central for natural kinds and artifacts?: The role of causal status in determining feature centrality. Cognition 69, 135–178 (1998).

Article CAS PubMed Google Scholar
Ahn, W., Gelman, S., Amsterlaw, J. A., Hohenstein, J. & Kalish, C. W. Causal status effect in children's categorization. Cognition 76, B35–B43 (2000).
Article CAS PubMed Google Scholar
Gopnik, A. & Sobel, D. M. Detecting blickets: how young children use information about novel causal powers in categorization and induction. Child Dev. 71, 1205–1222 (2000).
Article CAS PubMed Google Scholar
Keil, f. Concepts, Kinds, and Cognitive Development (MIT Press, Cambridge, Massachusetts, 1989). Presents key findings relevant to the role of causal mechanisms in determining what kind of thing an object is.
Google Scholar
Elman, J. L. Finding structure in time. Cogn. Sci. 14, 179–211 (1990).
Article Google Scholar
Cleeremans, A., Servan-Schreiber, D. & McClelland, J. L. Finite state automata and simple recurrent networks. Neural Comput. 1, 372–381 (1989).
Article Google Scholar
Rogers, T. T. & Griffin, R. in Proc. 13th Biennial Int. Conf. Infant Studies (eds Schmuckler, M. A. Trehub, S. E. & Kosarev, M. F.) 625 (International Society for Infant Studies, 2002).
Google Scholar
St. John, M. F. The story Gestalt: a model of knowledge-intensive processes in text comprehension. Cogn. Sci. 16, 271–306 (1992).
Article Google Scholar
Marcus, G. F. The Algebraic Mind (MIT Press, Cambridge, Massachusetts, 2001). Critique of the parallel distributed processing framework, offering an alternative perspective on the basic nature of human cognitive and semantic processing.
Book Google Scholar
Carey, S. & Spelke, E. in Domain-Specific Knowledge and Conceptual Change (ed. Hirschfeld, L. A.) 169–200 (Cambridge Univ. Press, New York, 1994).
Google Scholar
Barsalou, L. W., Simmons, W. K., Barbey, A. & Wilson, C. D. Grounding conceptual knowledge in modality-specific systems. Trends Cogn. Sci. (in the press).
Damasio, A. R. The brain binds entities and events by multiregional activation from convergence zones. Neural Comput. 1, 123–132 (1989).
Article Google Scholar
Martin, A., Haxby, J. V., Lalonde, F. M., Wiggs, C. L. & Ungerleider, L. G. Discrete cortical regions associated with knowledge of color and knowledge of action. Science 270, 102–105 (1995).
Article CAS PubMed Google Scholar
Kellenbach, M. L., Brett, M. & Patterson, K. Actions speak louder than functions: the importance of manipularity and action in tool representation. J. Cogn. Neurosci. 15, 30–46 (2003).
Article PubMed Google Scholar
Kellenbach, M. L., Brett, M. & Patterson, K. Larger, colorful, or noisy? Attribute- and modality-specific activations during retrieval of perceptual attribut knowledge. Cogn. Affect. Behav. Neurosci. 1, 207–221 (2001).
Article CAS PubMed Google Scholar
Martin, A. & Chao, L. L. Semantic memory in the brain: structure and processes. Curr. Opin. Neurobiol. 11, 194–201 (2001). A useful contemporary review of findings on semantic representation in the brain.
Article CAS PubMed Google Scholar
Warrington, E. K. & Shallice, T. Category specific semantic impairments. Brain 107, 829–854 (1984). Seminal report of category-specific deficits in semantic knowledge.
Article PubMed Google Scholar
Rogers, T. T. & Plaut, D. C. in Category Specificity in Mind and Brain (eds Forde, E. & Humphreys, G.) 251–289 (Psychology Press, East Sussex, UK, 2002).
Google Scholar
Gonnerman, L. M., Andersen, E. S., Devlin, J. T., Kempler, D. & Seidenberg, M. S. Double dissociation of semantic categories in Alzheimer's disease. Brain Lang. 57, 254–279 (1997).
Article CAS PubMed Google Scholar
Allport, D. A. in Current Perspectives in Dysphasia (eds Newman, S. K. & Epstein, R.) 207–244 (Churchill Livingstone, Edinburgh, 1985).
Google Scholar
Caramazza, A. & Shelton, J. R. Domain-specific knowledge systems in the brain: the animate-inanimate distinction. J. Cogn. Neurosci. 10, 1–34 (1998).
Article CAS PubMed Google Scholar
Warrington, E. K. & McCarthy, R. Categories of knowledge: further fractionation and an attempted integration. Brain 110, 1273–1296 (1987).
Article PubMed Google Scholar
Jacobs, R. A. & Jordan, M. I. Computational consequences of a bias toward short connections. J. Cogn. Neurosci. 4, 323–336 (1992).
Article CAS PubMed Google Scholar
Kohonen, T. The self-organizing map. Proc. IEEE 78, 1464–1480 (1990).
Article Google Scholar
Snodgrass, J. G. & Vanderwart, M. A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. J. Exp. Psychol. Learn. Mem. Cogn. 6, 174–215 (1980). | PubMed
Article CAS Google Scholar
Gelman, R. & Williams, E. M. in Handbook of Child Psychology, Volume II: Cognition, Perception and Development (eds Kuhn, D. & Siegler, R.) 575–630 (John Wiley and Sons, New York, 1997).
Google Scholar
Plaut, D. C. Double dissociation without modularity: evidence from connectionist neuropsychology. J. Clin. Exp. Neuropsychol. 17, 291–321 (1995).
Article CAS PubMed Google Scholar

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What is the parallel distributed process of memory?

Parallel distributed processing in psychology refers to a theoretical approach to understanding how memory is stored and processed, thus suggesting that images and ideas and concepts are distributed throughout the memorializing system at one time.

What is the parallel distributed processing model in memory psychology?

This system is a form of a computational model that helps us to enlighten ourselves about the complex characteristics of human memory functioning.

Which is the best description of semantic memory?

Semantic memory refers to our general world knowledge that encompasses memory for concepts, facts, and the meanings of words and other symbolic units that constitute formal communication systems such as language or math.

How does the parallel distributed processing approach explain learning?

How does the parallel distributed processing approach explain learning? The strength of the connection between items you are learning about increases with practice. schemas and scripts can reconstruct our memories of events and past perceptions in incorrect ways.