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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Human preferences are biased towards associative information.

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The brain values predictive information, preferring stimuli with stronger learned associations. This preference for predictable patterns enhances environmental stability and understanding of everyday choices.

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Area of Science:

  • Cognitive Neuroscience
  • Psychology
  • Decision Making

Background:

  • The brain utilizes learned statistical regularities and associations to generate predictions for interpreting sensory input.
  • Predictive information is hypothesized to be intrinsically valuable, driving proactive information seeking for environmental stability.

Purpose of the Study:

  • To investigate whether individuals prefer stimuli with greater associative information, all other factors being equal.
  • To test the hypothesis that predictive information is affectively tagged, influencing preferences.

Main Methods:

  • Experiments 1 and 2: Participants were presented with novel, meaningless visual shapes trained to associate with other shapes.
  • Experiment 3: Participants were shown real-world object images.
  • Preference was measured by comparing choices between stimuli with varying levels of associative information.

Main Results:

  • Participants consistently preferred shapes with established associations over those without.
  • A similar preference was observed for real-world objects eliciting stronger associations.
  • These findings indicate a preference for stimuli with higher predictive value.

Conclusions:

  • The study supports the proposal that predictive information is intrinsically valued.
  • This affective tagging of predictive information influences everyday preferences and choices.
  • Findings enhance understanding of how the brain forms preferences based on learned associations.