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https://doi.org/10.31234/osf.i...
Article . 2021 . Peer-reviewed
License: CC BY
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Psychological Review
Article . 2022 . Peer-reviewed
Data sources: Crossref
MPG.PuRe
Article . 2022
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A computational model of aesthetic value.

Authors: Aenne Brielmann; Peter Dayan;

A computational model of aesthetic value.

Abstract

People invest precious time and resources on sensory experiences such as watching movies or listening to music. Yet, we still have a poor understanding of how sensory experiences gain aesthetic value. We propose a model of aesthetic value that integrates existing theories with literature on conventional primary and secondary rewards such as food and money. We assume that the states of observers' sensory and cognitive systems adapt to process stimuli effectively in both the present and the future. These system states collectively comprise a probabilistic generative model of stimuli in the environment. Two interlinked components generate value: immediate sensory reward and the change in expected future reward. Immediate sensory reward is taken as the fluency with which a stimulus is processed, quantified by the likelihood of that stimulus given an observer's state. The change in expected future reward is taken as the change in fluency with which likely future stimuli will be processed. It is quantified by the change in the divergence between the observer's system state and the distribution of stimuli that the observer expects to see over the long term.Simulations show that a simple version of the model can account for empirical data on the effects of exposure, complexity, and symmetry on aesthetic value judgments. Taken together, our model melds processing fluency theories (immediate reward) and learning theories (change in expected future reward). Its application offers insight as to how the interplay of immediate processing fluency and learning gives rise to aesthetic value judgments.

Keywords

Judgment, Reward, Esthetics, Auditory Perception, Humans, Learning

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
43
Top 10%
Top 10%
Top 10%
hybrid