Frazier, Peter ; Yu, Angela J (2007)
Sequential Hypothesis Testing under Stochastic Deadlines.
Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007). Vancouver (03.12.2007-08.12.2007)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Most models of decision-making in neuroscience assume an infinite horizon, which yields an optimal solution that integrates evidence up to a fixed decision threshold; however, under most experimental as well as naturalistic behavioral settings, the decision has to be made before some finite deadline, which is often experienced as a stochastic quantity, either due to variable external constraints or internal timing uncertainty. In this work, we formulate this problem as sequential hypothesis testing under a stochastic horizon. We use dynamic programming tools to show that, for a large class of deadline distributions, the Bayes-optimal solution requires integrating evidence up to a threshold that declines monotonically over time. We use numerical simulations to illustrate the optimal policy in the special cases of a fixed deadline and one that is drawn from a gamma distribution.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2007 |
| Autor(en): | Frazier, Peter ; Yu, Angela J |
| Art des Eintrags: | Bibliographie |
| Titel: | Sequential Hypothesis Testing under Stochastic Deadlines |
| Sprache: | Englisch |
| Publikationsjahr: | 2007 |
| Ort: | Red Hook, NY |
| Verlag: | Curran Associates, Inc. |
| Buchtitel: | Advances in Neural Information Processing Systems 20 (NIPS 2007) |
| Band einer Reihe: | 20 |
| Veranstaltungstitel: | Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007) |
| Veranstaltungsort: | Vancouver |
| Veranstaltungsdatum: | 03.12.2007-08.12.2007 |
| URL / URN: | https://papers.nips.cc/paper_files/paper/2007/hash/9c82c7143... |
| Kurzbeschreibung (Abstract): | Most models of decision-making in neuroscience assume an infinite horizon, which yields an optimal solution that integrates evidence up to a fixed decision threshold; however, under most experimental as well as naturalistic behavioral settings, the decision has to be made before some finite deadline, which is often experienced as a stochastic quantity, either due to variable external constraints or internal timing uncertainty. In this work, we formulate this problem as sequential hypothesis testing under a stochastic horizon. We use dynamic programming tools to show that, for a large class of deadline distributions, the Bayes-optimal solution requires integrating evidence up to a threshold that declines monotonically over time. We use numerical simulations to illustrate the optimal policy in the special cases of a fixed deadline and one that is drawn from a gamma distribution. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie |
| Hinterlegungsdatum: | 01 Nov 2023 08:45 |
| Letzte Änderung: | 02 Nov 2023 07:30 |
| PPN: | 512804494 |
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