Modelling individual aesthetic judgements over time.

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'.