People are better at maximizing expected gain in a manual aiming task with rapidly changing probabilities than with rapidly changing payoffs

J Neurophysiol. 2014 Mar;111(5):1016-26. doi: 10.1152/jn.00163.2013. Epub 2013 Dec 11.


Previous research has shown that humans can select movements that achieve their goals, while avoiding negative outcomes, by selecting an "optimal movement endpoint." This optimal endpoint is modeled based on the participants' endpoint variability and the payoffs associated with the target and penalty regions within the environment. Although the values associated with our goals vary on a moment-to-moment basis in our daily interactions, the adaptation of endpoint selection to changing payoffs in laboratory-based tasks has been examined by varying contexts between blocks of trials. The present study was designed to determine whether participants adjust endpoints and aim to optimal endpoints and whether performance differs when probability or payoff parameters change from trial to trial. Participants aimed to a target circle that was partially overlapped by a penalty circle. They received 100 points for hitting the target and lost points for hitting the penalty area. The magnitude of the penalty value or the distance between the centers of the circles (related to the probability of target and penalty contact) was changed randomly from trial to trial in separate blocks. Results revealed that participants shifted their endpoint and generally aimed optimally when the distance between the circles was varied but did not optimally shift their endpoints when the penalty value was varied. The results suggest that participants rapidly adapted endpoints when the probabilities associated with the task change, because the spatial parameters are an intrinsic property of the visual stimuli that are tightly linked with the motor system, whereas consistent feedback may be necessary to adjust to value parameters effectively.

Keywords: optimality; reaching; statistical decision theory; visuomotor control.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adaptation, Physiological*
  • Adult
  • Female
  • Humans
  • Male
  • Models, Psychological
  • Movement*
  • Psychomotor Performance*
  • Reward
  • Young Adult