Research Specifications

Sensory processing sensitivity (SPS) is a biological trait associated with heightened sensitivity and responsivity to the environment. One important question is how those with the trait perceive their environments, thus giving rise to differential responses and outcomes. In this study, we used an artificial intelligence (AI) model—SPS Vision Net—to investigate perceptual differences associated with SPS and to begin to predict sensitivity levels based on a visual perception task. 190 participants (M age = 22.91; 102 (53%) females), completed an online experiment where they rated 100 images from the Open Affective Standardized Image Set (OASIS) on arousal, valence, and visual saliency. They also completed the Highly Sensitive Person (HSP) Scale measure of SPS. Results showed that SPS was positively associated with arousal in response to negative (vs. positive and neutral images), and, namely, sad (vs. happy, neutral, or fear) images. Also, SPS was negatively associated with positive ratings of negative images, specifically those showing frightening images. SPS was unrelated to response times and the number of salient selection blocks made. However, the AI model showed high accuracy (83.31%) in predicting SPS levels (R2 = 0.77). Consistent with theory and research, this study showed that SPS is associated with higher arousal and lower positive ratings in response to the OASIS image rating task. Novel findings showed that a new, accurate AI-backed SPS measurement system, based on a visual selection, was predictive of HSP scores with high accuracy. Finally, the AI model indicates that visual perception differs as a function of SPS.