Belousov, B. ; Neumann, G. ; Rothkopf, C. A. ; Peters, J. (2017)
Catching heuristics are optimal control policies.
13th Karniel Computational Motor Control Workshop. Be'er Sheva, Israel (14.03.2017-16.03.2017)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Two seemingly contradictory theories attempt to explain how humans move to intercept an airborne ball. One theory posits that humans predict the ball trajectory to optimally plan future actions; the other claims that, instead of performing such complicated computations, humans employ heuristics to reactively choose appropriate actions based on immediate visual feedback. In this paper, we show that interception strategies appearing to be heuristics can be understood as computational solutions to the optimal control problem faced by a ball-catching agent acting under uncertainty. Modeling catching as a continuous partially observable Markov decision process and employing stochastic optimal control theory, we discover that the four main heuristics described in the literature are optimal solutions if the catcher has sufficient time to continuously visually track the ball. Specifically, by varying model parameters such as noise, time to ground contact, and perceptual latency, we show that different strategies arise under different circumstances. The catcher's policy switches between generating reactive and predictive behavior based on the ratio of system to observation noise and the ratio between reaction time and task duration. Thus, we provide a rational account of human ball-catching behavior and a unifying explanation for seemingly contradictory theories of target interception on the basis of stochastic optimal control. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640554
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Belousov, B. ; Neumann, G. ; Rothkopf, C. A. ; Peters, J. |
| Art des Eintrags: | Bibliographie |
| Titel: | Catching heuristics are optimal control policies |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Veranstaltungstitel: | 13th Karniel Computational Motor Control Workshop |
| Veranstaltungsort: | Be'er Sheva, Israel |
| Veranstaltungsdatum: | 14.03.2017-16.03.2017 |
| Kurzbeschreibung (Abstract): | Two seemingly contradictory theories attempt to explain how humans move to intercept an airborne ball. One theory posits that humans predict the ball trajectory to optimally plan future actions; the other claims that, instead of performing such complicated computations, humans employ heuristics to reactively choose appropriate actions based on immediate visual feedback. In this paper, we show that interception strategies appearing to be heuristics can be understood as computational solutions to the optimal control problem faced by a ball-catching agent acting under uncertainty. Modeling catching as a continuous partially observable Markov decision process and employing stochastic optimal control theory, we discover that the four main heuristics described in the literature are optimal solutions if the catcher has sufficient time to continuously visually track the ball. Specifically, by varying model parameters such as noise, time to ground contact, and perceptual latency, we show that different strategies arise under different circumstances. The catcher's policy switches between generating reactive and predictive behavior based on the ratio of system to observation noise and the ratio between reaction time and task duration. Thus, we provide a rational account of human ball-catching behavior and a unifying explanation for seemingly contradictory theories of target interception on the basis of stochastic optimal control. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640554 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Intelligente Autonome Systeme |
| Hinterlegungsdatum: | 09 Nov 2018 08:16 |
| Letzte Änderung: | 21 Nov 2022 10:55 |
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