Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.
Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the eff...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011950&type=printable |
| _version_ | 1797224071244546048 |
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| author | Jaron T Colas John P O'Doherty Scott T Grafton |
| author_facet | Jaron T Colas John P O'Doherty Scott T Grafton |
| author_sort | Jaron T Colas |
| collection | DOAJ |
| description | Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior. |
| first_indexed | 2024-04-24T13:47:17Z |
| format | Article |
| id | doaj.art-8ce450de972b418b95c29d5c56a5889f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-24T13:47:17Z |
| publishDate | 2024-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-8ce450de972b418b95c29d5c56a5889f2024-04-04T05:32:47ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-03-01203e101195010.1371/journal.pcbi.1011950Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.Jaron T ColasJohn P O'DohertyScott T GraftonActive reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011950&type=printable |
| spellingShingle | Jaron T Colas John P O'Doherty Scott T Grafton Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. PLoS Computational Biology |
| title | Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. |
| title_full | Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. |
| title_fullStr | Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. |
| title_full_unstemmed | Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. |
| title_short | Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. |
| title_sort | active reinforcement learning versus action bias and hysteresis control with a mixture of experts and nonexperts |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011950&type=printable |
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