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Visual-reward driven changes of movement during action execution.

Angela Marti-Marca1,2, Gustavo Deco1,3, Ignasi Cos4,5,6

  • 1Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.

Scientific Reports
|September 24, 2020
PubMed
Summary
This summary is machine-generated.

Humans can dynamically adjust their planned movements mid-action, even after starting, especially when rewards increase. This motor control flexibility allows rapid adaptation to changing environments and opportunities.

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Area of Science:

  • Neuroscience
  • Motor Control
  • Decision-Making

Background:

  • Motor decision-making is typically viewed as a sequential process.
  • This sequential model may not apply to dynamic environments requiring rapid choices.
  • The consideration of non-selected motor options post-movement onset is not well understood.

Purpose of the Study:

  • To investigate if and how non-selected motor options are considered after movement initiation.
  • To test the hypothesis that reward changes trigger dynamic reassessment of motor options.

Main Methods:

  • A reaching movement task was designed where participants moved towards a target for reward.
  • Reward was contingent on arrival precision and distribution along the target.
  • On a subset of trials, the reward distribution was altered post-movement onset.

Main Results:

  • Participants frequently altered their chosen movements when reward increased after movement onset.
  • These movement adjustments occurred faster than average reaction times.
  • Movement changes were influenced by both reward alterations and the current motor system state.

Conclusions:

  • Motor decision-making can involve dynamic reassessment of options even after movement initiation.
  • Reward-based feedback plays a crucial role in modulating ongoing motor behavior.
  • The motor system exhibits adaptability, integrating real-time information for flexible action selection.