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SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network

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Abstract

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.

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Data Availability

The data that support the findings of this study are openly available in the SPS Vision Dataset at https://github.com/nima-sa/SPS-Vision.

Notes

  1. https://github.com/nima-sa/SPS-Vision

  2. The total rest time did not contribute to the final accuracy in our model, and thus was discarded.

  3. https://www.python.org/

  4. https://pandas.pydata.org/

  5. https://scipy.org/

  6. https://matplotlib.org/

  7. https://pingouin-stats.org/

  8. https://seaborn.pydata.org/

  9. Note that this figure is representing the error of one iteration of the fivefold cross-validation, not the average error resulted from all iterations. Hence, the accuracy in the figure is not as same as the reported average accuracy.

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Authors and Affiliations

  1. Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz-Azarshahr Road, Tabriz, 5375171379, Iran

    Nima Sadeghzadeh & Nacer Farajzadeh

  2. Artificial Intelligence and Machine Learning Research Laboratory, Azarbaijan Shahid Madani University, Tabriz-Azarshahr Road, Tabriz, 5375171379, Iran

    Nima Sadeghzadeh & Nacer Farajzadeh

  3. Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA

    Novia Dattatri & Bianca P. Acevedo

Authors
  1. Nima Sadeghzadeh
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  2. Nacer Farajzadeh
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  3. Novia Dattatri
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  4. Bianca P. Acevedo
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Correspondence to Nacer Farajzadeh.

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Sadeghzadeh, N., Farajzadeh, N., Dattatri, N. et al. SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network. Cogn Comput 16, 1379–1392 (2024). https://doi.org/10.1007/s12559-023-10216-6

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