Speeded Reaching Movements around Invisible Obstacles

Todd E. Hudson, Uta Wolfe, Laurence T. Maloney

Research output: Contribution to journalArticle

Abstract

We analyze the problem of obstacle avoidance from a Bayesian decision-theoretic perspective using an experimental task in which reaches around a virtual obstacle were made toward targets on an upright monitor. Subjects received monetary rewards for touching the target and incurred losses for accidentally touching the intervening obstacle. The locations of target-obstacle pairs within the workspace were varied from trial to trial. We compared human performance to that of a Bayesian ideal movement planner (who chooses motor strategies maximizing expected gain) using the Dominance Test employed in Hudson et al. (2007). The ideal movement planner suffers from the same sources of noise as the human, but selects movement plans that maximize expected gain in the presence of that noise. We find good agreement between the predictions of the model and actual performance in most but not all experimental conditions.

Original languageEnglish (US)
Article numbere1002676
JournalPLoS Computational Biology
Volume8
Issue number9
DOIs
StatePublished - Sep 2012

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Collision avoidance
Noise
Target
Reward
Human Performance
Obstacle Avoidance
Workspace
prediction
monitoring
Monitor
Choose
Maximise
Prediction
testing
Movement
trial
Model

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Ecology
  • Molecular Biology
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Computational Theory and Mathematics

Cite this

Speeded Reaching Movements around Invisible Obstacles. / Hudson, Todd E.; Wolfe, Uta; Maloney, Laurence T.

In: PLoS Computational Biology, Vol. 8, No. 9, e1002676, 09.2012.

Research output: Contribution to journalArticle

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