Modeling Working Memory to Identify Computational Correlates of Consciousness
Recent advances in philosophical thinking about consciousness, such as cognitive phenomenology and mereological analysis, provide a framework that facilitates using computational models to explore issues surrounding the nature of consciousness. Here we suggest that, in particular, studying the compu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2019-09-01
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| Series: | Open Philosophy |
| Subjects: | |
| Online Access: | http://www.degruyter.com/view/j/opphil.2019.2.issue-1/opphil-2019-0022/opphil-2019-0022.xml?format=INT |
| _version_ | 1818675278980841472 |
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| author | Reggia James A. Katz Garrett E. Davis Gregory P. |
| author_facet | Reggia James A. Katz Garrett E. Davis Gregory P. |
| author_sort | Reggia James A. |
| collection | DOAJ |
| description | Recent advances in philosophical thinking about consciousness, such as cognitive phenomenology and mereological analysis, provide a framework that facilitates using computational models to explore issues surrounding the nature of consciousness. Here we suggest that, in particular, studying the computational mechanisms of working memory and its cognitive control is highly likely to identify computational correlates of consciousness and thereby lead to a deeper understanding of the nature of consciousness. We describe our recent computational models of human working memory and propose that three computational correlates of consciousness follow from the results of this work: itinerant attractor sequences, top-down gating, and very fast weight changes. Our current investigation is focused on evaluating whether these three correlates are sufficient to create more complex working memory models that encompass compositionality and basic causal inference. We conclude that computational models of working memory are likely to be a fruitful approach to advancing our understanding of consciousness in general and in determining the long-term potential for development of an artificial consciousness specifically. |
| first_indexed | 2024-12-17T08:25:03Z |
| format | Article |
| id | doaj.art-d5ec09242cec4910913f1ee299b94d68 |
| institution | Directory Open Access Journal |
| issn | 2543-8875 |
| language | English |
| last_indexed | 2024-12-17T08:25:03Z |
| publishDate | 2019-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Philosophy |
| spelling | doaj.art-d5ec09242cec4910913f1ee299b94d682022-12-21T21:56:49ZengDe GruyterOpen Philosophy2543-88752019-09-012125226910.1515/opphil-2019-0022opphil-2019-0022Modeling Working Memory to Identify Computational Correlates of ConsciousnessReggia James A.0Katz Garrett E.1Davis Gregory P.2University of Maryland Maryland, United States of AmericaSyracuse UniversitySyracuse, New York, United States of AmericaUniversity of Maryland Maryland, United States of AmericaRecent advances in philosophical thinking about consciousness, such as cognitive phenomenology and mereological analysis, provide a framework that facilitates using computational models to explore issues surrounding the nature of consciousness. Here we suggest that, in particular, studying the computational mechanisms of working memory and its cognitive control is highly likely to identify computational correlates of consciousness and thereby lead to a deeper understanding of the nature of consciousness. We describe our recent computational models of human working memory and propose that three computational correlates of consciousness follow from the results of this work: itinerant attractor sequences, top-down gating, and very fast weight changes. Our current investigation is focused on evaluating whether these three correlates are sufficient to create more complex working memory models that encompass compositionality and basic causal inference. We conclude that computational models of working memory are likely to be a fruitful approach to advancing our understanding of consciousness in general and in determining the long-term potential for development of an artificial consciousness specifically.http://www.degruyter.com/view/j/opphil.2019.2.issue-1/opphil-2019-0022/opphil-2019-0022.xml?format=INTcomputational correlatescomputational explanatory gapcognitive controlworking memorycognitive phenomenologymereologymachine consciousnessartificial consciousnessmind-brain problem |
| spellingShingle | Reggia James A. Katz Garrett E. Davis Gregory P. Modeling Working Memory to Identify Computational Correlates of Consciousness Open Philosophy computational correlates computational explanatory gap cognitive control working memory cognitive phenomenology mereology machine consciousness artificial consciousness mind-brain problem |
| title | Modeling Working Memory to Identify Computational Correlates of Consciousness |
| title_full | Modeling Working Memory to Identify Computational Correlates of Consciousness |
| title_fullStr | Modeling Working Memory to Identify Computational Correlates of Consciousness |
| title_full_unstemmed | Modeling Working Memory to Identify Computational Correlates of Consciousness |
| title_short | Modeling Working Memory to Identify Computational Correlates of Consciousness |
| title_sort | modeling working memory to identify computational correlates of consciousness |
| topic | computational correlates computational explanatory gap cognitive control working memory cognitive phenomenology mereology machine consciousness artificial consciousness mind-brain problem |
| url | http://www.degruyter.com/view/j/opphil.2019.2.issue-1/opphil-2019-0022/opphil-2019-0022.xml?format=INT |
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