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Summary
This summary is machine-generated.

Human observers quickly learn simple visual distractor distributions, like uniform ones, and can adapt to complex shapes, such as bimodal distributions, with more experience.

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Area of Science:

  • Cognitive psychology
  • Visual perception
  • Machine learning

Background:

  • Observers encode stimulus ensemble statistics (mean, variance).
  • Recent work shows encoding of ensemble shape.
  • Learning dynamics of shape encoding remain unexplored.

Purpose of the Study:

  • Investigate learning dynamics of shape encoding.
  • Examine priors for distribution shape.
  • Demonstrate learning of complex distributions (bimodal).

Main Methods:

  • Used intertrial priming (response time changes) in visual search.
  • Assessed internal models of distractor distributions.
  • Varied distractor distribution shapes (uniform, Gaussian, bimodal).

Main Results:

  • Two repetitions suffice for learning uniform distribution shape.
  • Gaussian and uniform distributions distinguished after two repetitions.
  • Bimodal distributions initially treated as uniform, adapted with repetitions.

Conclusions:

  • Observers lack strong initial priors for distribution shapes.
  • Simple distributions are learned rapidly.
  • Representation adjusts to complex distributions with accumulated experience.