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Visualizing Visual Adaptation
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Algorithms of adaptation in inductive inference.

Jan-Philipp Fränken1, Nikos C Theodoropoulos2, Neil R Bramley1

  • 1Department of Psychology, University of Edinburgh, United Kingdom.

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Summary
This summary is machine-generated.

Human concept inference uses local adaptive search in a mental theory space. Revised guesses are anchored to earlier ones, suggesting a local search mechanism guides hypothesis revision.

Keywords:
Adaptive searchConcept learningLanguage of thoughtMarkov chain Monte CarloProgram induction

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Area of Science:

  • Cognitive Science
  • Psychology
  • Artificial Intelligence

Background:

  • Human concept inference is complex.
  • Understanding how people learn and revise hypotheses is crucial.

Purpose of the Study:

  • To investigate if human concept inference employs local adaptive search.
  • To model hypothesis revision in concept learning tasks.

Main Methods:

  • Participants performed a rule-discovery task in a simulated environment.
  • Evidence gathering (active or observational) preceded hypothesis revision.
  • Computational models were compared against human judgment data.

Main Results:

  • An order effect was observed: revised guesses were anchored to initial ones.
  • A local adaptive search model best explained hypothesis revision patterns.
  • This model outperformed alternative explanations.

Conclusions:

  • Human concept inference appears to utilize local adaptive search.
  • This mechanism operates within a compositional mental theory space.
  • Local search helps manage the complexity of concept learning.