Shenoy, Pradeep ; Yu, Angela J ; Rao, Rajesh PN (2010)
A rational decision making framework for inhibitory control.
Twenty-fourth Conference on Neural Information Processing Systems (NIPS 2010). Vancouver (06.12.2010-11.12.2010)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Intelligent agents are often faced with the need to choose actions with uncertain consequences, and to modify those actions according to ongoing sensory processing and changing task demands. The requisite ability to dynamically modify or cancel planned actions is known as inhibitory control in psychology. We formalize inhibitory control as a rational decision-making problem, and apply to it to the classical stop-signal task. Using Bayesian inference and stochastic control tools, we show that the optimal policy systematically depends on various parameters of the problem, such as the relative costs of different action choices, the noise level of sensory inputs, and the dynamics of changing environmental demands. Our normative model accounts for a range of behavioral data in humans and animals in the stop-signal task, suggesting that the brain implements statistically optimal, dynamically adaptive, and reward-sensitive decision-making in the context of inhibitory control problems.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2010 |
| Autor(en): | Shenoy, Pradeep ; Yu, Angela J ; Rao, Rajesh PN |
| Art des Eintrags: | Bibliographie |
| Titel: | A rational decision making framework for inhibitory control |
| Sprache: | Englisch |
| Publikationsjahr: | 2010 |
| Ort: | Red Hook, New York |
| Verlag: | Curran Associates, Inc. |
| Buchtitel: | Advances in Neural Information Processing Systems 23 (NIPS 2010) |
| Band einer Reihe: | 23 |
| Veranstaltungstitel: | Twenty-fourth Conference on Neural Information Processing Systems (NIPS 2010) |
| Veranstaltungsort: | Vancouver |
| Veranstaltungsdatum: | 06.12.2010-11.12.2010 |
| URL / URN: | https://proceedings.neurips.cc/paper_files/paper/2010/hash/6... |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Intelligent agents are often faced with the need to choose actions with uncertain consequences, and to modify those actions according to ongoing sensory processing and changing task demands. The requisite ability to dynamically modify or cancel planned actions is known as inhibitory control in psychology. We formalize inhibitory control as a rational decision-making problem, and apply to it to the classical stop-signal task. Using Bayesian inference and stochastic control tools, we show that the optimal policy systematically depends on various parameters of the problem, such as the relative costs of different action choices, the noise level of sensory inputs, and the dynamics of changing environmental demands. Our normative model accounts for a range of behavioral data in humans and animals in the stop-signal task, suggesting that the brain implements statistically optimal, dynamically adaptive, and reward-sensitive decision-making in the context of inhibitory control problems. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie |
| Hinterlegungsdatum: | 30 Okt 2023 07:15 |
| Letzte Änderung: | 31 Okt 2023 06:54 |
| PPN: | 51275957X |
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