Modelling individual aesthetic judgements over time

Philos Trans R Soc Lond B Biol Sci. 2024 Jan 29;379(1895):20220414. doi: 10.1098/rstb.2022.0414. Epub 2023 Dec 18.

Abstract

Listening to music, watching a sunset-many sensory experiences are valuable to us, to a degree that differs significantly between individuals, and within an individual over time. We have theorized (Brielmann & Dayan 2022 Psychol. Rev. 129, 1319-1337 (doi:10.1037/rev0000337))) that these idiosyncratic values derive from the task of using experiences to tune the sensory-cognitive system to current and likely future input. We tested the theory using participants' (n = 59) ratings of a set of dog images (n = 55) created using the NeuralCrossbreed morphing algorithm. A full realization of our model that uses feature representations extracted from image-recognizing deep neural nets (e.g. VGG-16) is able to capture liking judgements on a trial-by-trial basis (median r = 0.65), outperforming predictions based on population averages (median r = 0.01). Furthermore, the model's learning component allows it to explain image sequence dependent rating changes, capturing on average 17% more variance in the ratings for the true trial order than for simulated random trial orders. This validation of our theory is the first step towards a comprehensive treatment of individual differences in evaluation. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Keywords: aesthetics; individual differences; temporal dynamics; value.

MeSH terms

  • Animals
  • Dogs
  • Emotions
  • Esthetics
  • Humans
  • Judgment*
  • Learning
  • Music* / psychology