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: | |
| 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 |
| 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.) |
|---|---|
| ISSN: | 0033-295X |
| DOI: | 10.1037/0033-295X.113.2.201 |
