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A computational model of aesthetic value.

Aenne A Brielmann1, Peter Dayan1

  • 1Department of Computational Neuroscience.

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This study introduces a novel model for aesthetic value, integrating immediate sensory rewards with future reward expectations. It explains how processing fluency and learning shape our appreciation of experiences like music and movies.

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Area of Science:

  • Cognitive Science
  • Psychology
  • Neuroaesthetics

Background:

  • Understanding aesthetic value is crucial for explaining preferences in sensory experiences.
  • Existing theories often focus on immediate rewards, neglecting future expectations.
  • A comprehensive model integrating both is needed to explain aesthetic judgments.

Purpose of the Study:

  • To propose a new model of aesthetic value that unifies immediate sensory rewards and expected future rewards.
  • To explain how processing fluency and learning contribute to aesthetic appreciation.
  • To provide a computational framework for understanding aesthetic judgments.

Main Methods:

  • Developed a probabilistic generative model of environmental stimuli based on observer system states.
  • Defined immediate sensory reward as processing fluency (stimulus likelihood).
  • Quantified change in expected future reward by changes in system state divergence from long-term stimulus expectations.

Main Results:

  • Simulations demonstrated the model's ability to account for empirical data on exposure, complexity, and symmetry.
  • The model successfully integrates processing fluency (immediate reward) and learning theories (future reward).
  • The interplay between immediate processing and learning was shown to drive aesthetic value.

Conclusions:

  • The proposed model offers a unified framework for understanding aesthetic value.
  • It highlights the dual role of immediate processing fluency and learning in shaping aesthetic judgments.
  • This research provides insights into the cognitive mechanisms underlying aesthetic appreciation.