TU Darmstadt / ULB / TUbiblio

Catching heuristics are optimal control policies

Belousov, Boris ; Neumann, Gerhard ; Rothkopf, Constantin A. ; Peters, Jan (2022)
Catching heuristics are optimal control policies.
30th Annual Conference on Neural Information Processing Systems (NIPS 2016). Barcelona, Spain (05.-10.12.2016)
doi: 10.26083/tuprints-00020556
Conference or Workshop Item, Secondary publication, Publisher's Version

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 appro- priate actions based on immediate visual feedback. In this paper, we show that interception strategies appearing to be heuristics can be understood as computa- tional 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.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Belousov, Boris ; Neumann, Gerhard ; Rothkopf, Constantin A. ; Peters, Jan
Type of entry: Secondary publication
Title: Catching heuristics are optimal control policies
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: Curran Associates, Inc.
Book Title: Advances in Neural Information Processing Systems 29 : 30th Annual Conference on Neural Information Processing Systems 2016
Collation: 9 Seiten
Event Title: 30th Annual Conference on Neural Information Processing Systems (NIPS 2016)
Event Location: Barcelona, Spain
Event Dates: 05.-10.12.2016
Edition: Volume 3
DOI: 10.26083/tuprints-00020556
URL / URN: https://tuprints.ulb.tu-darmstadt.de/20556
Corresponding Links:
Origin: Secondary publication service
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 appro- priate actions based on immediate visual feedback. In this paper, we show that interception strategies appearing to be heuristics can be understood as computa- tional 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.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-205568
Classification DDC: 000 Generalities, computers, information > 004 Computer science
100 Philosophy and psychology > 150 Psychology
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Intelligent Autonomous Systems
03 Department of Human Sciences
03 Department of Human Sciences > Institute for Psychology
TU-Projects: EC/H2020|640554|SKILLS4ROBOTS
Date Deposited: 18 Nov 2022 14:23
Last Modified: 21 Nov 2022 11:10
PPN:
Corresponding Links:
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details