Exploring the role of micro-valence in the phenomenal space: insights from similarity judgments and deep learning models

Abstract Is valence an intrinsic dimension of conscious experience, as different authors have suggested? If so, all conscious experiences, and hence all conscious perceptions, should be valenced, even if only minimally so, and similarity judgments should be at least partly driven by one’s affective dispositions. Leveraging the concept of micro-valence, we explore the extent to which valence judgments correlate with similarity judgments and with the different stages of processing in deep neural networks (DNNs). One hundred forty-nine participants provided both similarity and valence judgments for 120 images of everyday objects, using an odd-one-out task (Study 1), a spatial arrangement task (Study 2), and the Birthday task, which asks people to choose an object they would like to keep (or give away) as their birthday gift. We also extracted activations from the layers of DNNs trained to classify objects in response to the same images. Representation similarity analysis and multidimensional scaling analyses highlight the role of micro-valence in the similarity space, suggesting that valence permeates similarity judgments. DNN analyses show that this valence-similarity relationship is not entirely mediated by stimulus perceptual features and suggest that low-level visual features play a role in the computation of valence. HighlightsWe observed evidence for the existence of micro-valence, the valence of a priori neutral objects.We observed a correlation between micro-valence and similarity distances. Moreover, this correlation was not entirely mediated by stimulus perceptual features captured by deep neural network (DNN).Micro-valence, and thus valence, appears to be involved in how humans organize their phenomenal space.Finally, we observed a correlation between micro-valence distances and the distances in activation of the very first DNN layer.