A mixture of delta-rules approximation to bayesian inference in change-point problems.
Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23935472/pdf/?tool=EBI |
| _version_ | 1818700980469891072 |
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| author | Robert C Wilson Matthew R Nassar Joshua I Gold |
| author_facet | Robert C Wilson Matthew R Nassar Joshua I Gold |
| author_sort | Robert C Wilson |
| collection | DOAJ |
| description | Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world. |
| first_indexed | 2024-12-17T15:13:34Z |
| format | Article |
| id | doaj.art-319536d444224beeac36e201a42ed281 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-17T15:13:34Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-319536d444224beeac36e201a42ed2812022-12-21T21:43:35ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-0197e100315010.1371/journal.pcbi.1003150A mixture of delta-rules approximation to bayesian inference in change-point problems.Robert C WilsonMatthew R NassarJoshua I GoldError-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23935472/pdf/?tool=EBI |
| spellingShingle | Robert C Wilson Matthew R Nassar Joshua I Gold A mixture of delta-rules approximation to bayesian inference in change-point problems. PLoS Computational Biology |
| title | A mixture of delta-rules approximation to bayesian inference in change-point problems. |
| title_full | A mixture of delta-rules approximation to bayesian inference in change-point problems. |
| title_fullStr | A mixture of delta-rules approximation to bayesian inference in change-point problems. |
| title_full_unstemmed | A mixture of delta-rules approximation to bayesian inference in change-point problems. |
| title_short | A mixture of delta-rules approximation to bayesian inference in change-point problems. |
| title_sort | mixture of delta rules approximation to bayesian inference in change point problems |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23935472/pdf/?tool=EBI |
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