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Direct perception of affective valence from vision.

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Feelings may be decoded directly from visual environment statistics. A machine learning model showed that objective image properties, not just meaning, contribute to subjective valence perception.

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Area of Science:

  • Cognitive Neuroscience
  • Computer Vision
  • Psychology

Background:

  • Subjective feelings (valence) are traditionally linked to conceptual and bodily states.
  • The role of objective visual environmental statistics in shaping emotional valence is less understood.

Purpose of the Study:

  • To investigate if the valence of feelings can be decoded directly from objective ecological statistics of the visual environment.
  • To explore the contribution of visual valence (VV) to subjective emotional experience.

Main Methods:

  • Trained a machine learning model (VV model) on low-level image statistics from emotionally charged photographs.
  • Tested the VV model's predictive accuracy on human valence ratings and abstract paintings.
  • Examined human observers' valence experience with and without conceptual image analysis.
  • Investigated the neural basis of VV in visual brain regions using deep generative networks.

Main Results:

  • The VV model accurately predicted human valence ratings and generalized robustly to abstract art.
  • Limiting conceptual analysis in humans increased the influence of VV on valence experience, aligning with machine perception.
  • Neural activity in lower to mid-level visual areas correlated with VV.
  • Synthesized images in generative networks reflected positive versus negative VV.

Conclusions:

  • Subjective valence experience has at least two distinct modes: one indirect (meaning-based) and one direct (ecological statistics-based).
  • Ecological statistics in the visual environment can be directly perceived as an objective property contributing to subjective valence.
  • This suggests a direct perception pathway for emotional valence embedded in visual input.