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Area of Science:

  • Psychology
  • Neuroscience
  • Computer Science

Background:

  • Affective picture sets are crucial for emotion research but face limitations in diversity, quality, and accessibility.
  • Artificial intelligence (AI) offers a potential solution for generating novel affective stimuli, but its efficacy in eliciting reliable emotional responses is unproven.

Purpose of the Study:

  • To compare emotional and physiological responses to AI-generated pictures versus original, standardized affective pictures.
  • To assess the reliability and validity of AI-generated images as stimuli in emotion research.

Main Methods:

  • Two study iterations involved 109 participants viewing standardized pictures (from IAPS and other sources) and matched AI-generated counterparts.
  • Emotional responses were measured using pupil diameter, electroencephalogram (EEG), and self-reported ratings of hedonic valence and emotional arousal.

Main Results:

  • Both original and AI-generated emotional pictures elicited stronger responses (ratings and EEG) than neutral pictures, with smaller effect sizes for AI images.
  • Correlations between matched AI and original pictures were strong for ratings and EEG measures.
  • Pupil data showed content effects in the second iteration but not the first.

Conclusions:

  • AI-generated picture sets show potential for eliciting affective and physiological responses, serving as a viable alternative to traditional stimuli.
  • Further research is needed to optimize AI image generation for emotion research and fully understand its capabilities.