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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, Psychological Review, 2022) that these idiosyncratic values derive from the task of using experiences to tune the sensory 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 Neural Crossbreed 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 judgments 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.