The perceptual primacy of feeling: Affectless visual machines explain a majority of variance in human visually evoked affect.

ABSTRACT: Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive the world in the absence of active physiology, deliberative thought, or any form of feedback that resembles human affective experience offer tools to demystify the relationship between seeing and feeling, and to assess how much of visually evoked affective experiences may be a straightforward function of representation learning over natural image statistics. In this work, we deploy a diverse sample of 180 state-of-the-art deep neural network models trained only on canonical computer vision tasks to predict human ratings of arousal, valence, and beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets. Import

SUBMITTER: Conwell C 

PROVIDER: S-EPMC11789064 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature