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Occam's Razor in sensorimotor learning.

Tim Genewein1, Daniel A Braun

  • 1Max Planck Institute for Biological Cybernetics, , Tübingen, Germany, Max Planck Institute for Intelligent Systems, , Tübingen, Germany, Graduate Training Centre of Neuroscience, Tübingen, Germany.

Proceedings. Biological Sciences
|March 28, 2014
PubMed
Summary
This summary is machine-generated.

Sensorimotor control exhibits Occam's Razor, a preference for simpler explanations. Participants favored simpler models when faced with ambiguous sensorimotor tasks, aligning with Bayesian principles.

Keywords:
Bayesian model comparisonGaussian processesOccam's Razorsensorimotor controlstructural learning

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Area of Science:

  • Cognitive Neuroscience
  • Sensorimotor Control
  • Bayesian Inference

Background:

  • Recent studies suggest the sensorimotor system employs probabilistic, Bayesian models for environmental prediction.
  • Bayesian statistics incorporates Occam's Razor, favoring simpler models for equally explanatory data.

Purpose of the Study:

  • To directly investigate the presence and application of Occam's Razor in sensorimotor control.
  • To determine if humans prefer simpler models when inferring hidden environmental properties.

Main Methods:

  • A sensorimotor task required participants to draw lines through noisy data points from two models: simple (smooth) and complex (wiggly).
  • Training trials informed participants about the generating model; probe trials presented ambiguous stimuli.
  • Behavioral choices in ambiguous situations were analyzed for model preference.

Main Results:

  • Participants demonstrated a clear preference for the simpler model when ambiguous stimuli could be explained equally well by both.
  • Choice behavior quantitatively matched predictions from Bayesian Occam's Razor.
  • Drawn trajectories aligned with Bayesian predictive distributions, not simple heuristics.

Conclusions:

  • Occam's Razor is a fundamental principle operating within sensorimotor processing.
  • This preference for simplicity is not solely due to physical effort or a general desire for smoothness.
  • The findings support a Bayesian framework for understanding sensorimotor decision-making.