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Short-Term Memory for Serial Order: A Recurrent Neural Network Model

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Title: Short-Term Memory for Serial Order: A Recurrent Neural Network Model
Language: English
Authors: Botvinick, Matthew M., Plaut, David C.
Source: Psychological Review. Apr 2006 113(2):201-233.
Availability: American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Peer Reviewed: Y
Physical Description: PDF
Page Count: 33
Publication Date: 2006
Document Type: Journal Articles
Reports - Research
Descriptors: Models, Short Term Memory, Serial Ordering, Brain Hemisphere Functions, Correlation, Computer Simulation, Recall (Psychology), Prior Learning, Role, Neurology, Scientific Research, Prediction
DOI: 10.1037/0033-295X.113.2.201
ISSN: 0033-295X
Abstract: Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order. (Contains 17 footnotes, 1 table, and 17 figures.)
Abstractor: As Provided
Number of References: 143
Entry Date: 2011
Accession Number: EJ934029
Database: ERIC
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  Data: Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order. (Contains 17 footnotes, 1 table, and 17 figures.)
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