September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Bayes meets Gibson: Affordance-based control of target interception in the face of uncertainty
Author Affiliations
  • Scott Steinmetz
    Cognitive Science Department, Rensselaer Polytechnic Institute
  • Nathaniel Powell
    Cognitive Science Department, Rensselaer Polytechnic Institute
  • Oliver Layton
    Cognitive Science Department, Rensselaer Polytechnic Institute
  • Brett Fajen
    Cognitive Science Department, Rensselaer Polytechnic Institute
Journal of Vision August 2017, Vol.17, 234. doi:10.1167/17.10.234
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      Scott Steinmetz, Nathaniel Powell, Oliver Layton, Brett Fajen; Bayes meets Gibson: Affordance-based control of target interception in the face of uncertainty. Journal of Vision 2017;17(10):234. doi: 10.1167/17.10.234.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

To be efficient over time in the pursuit of moving targets, humans and other animals must know when to abandon the chase of a target that is moving too fast to catch or for which the costs of pursuing outweigh the benefits of catching. From an affordance-based perspective, this entails perceiving catchability, which is determined by how fast one needs to move to intercept in relation to one's locomotor capabilities. However, affordances are traditionally treated as categorical (i.e., the action is either possible or not) when in fact the presence of variability in both perception and movement ensures that target catchability is a continuous, probabilistic function. We developed a computational framework that treats interception as a dynamic decision making process under uncertainty. In our dynamic Bayesian model, the pursuer continuously updates its belief about catchability based on informational variables, such as relative target distance and time until the target reaches an escape region. These beliefs shift based on the likelihood of detecting each variable at a given value (plus noise) when the target was catchable and when it was uncatchable. At each moment, the model uses its belief about catchability to decide whether to continue to pursue the target or give up. To evaluate the model, we compared its beliefs about target catchability to the behavior of humans in an experiment in which subjects had to decide whether to pursue or abandon the chase of a moving target (Fajen et al., VSS 2016). In a subset of randomly selected trials, the model's beliefs closely matched human behavior – that is, the belief reflected high certainty in catchability when subjects pursued the target and high certainty in uncatchability when subjects gave up. Our framework provides a powerful tool for investigating action-scaled affordances as probabilistic functions of actor-environment variables.

Meeting abstract presented at VSS 2017

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