Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.04889 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918315113316352 |
|---|---|
| author | Pegler, Dominik Steyrl, David Zhang, Mengfan Karner, Alexander Arato, Jozsef Scharnowski, Frank Melinscak, Filip |
| author_facet | Pegler, Dominik Steyrl, David Zhang, Mengfan Karner, Alexander Arato, Jozsef Scharnowski, Frank Melinscak, Filip |
| contents | Phobias are common and impairing, and exposure therapy, which involves confronting patients with fear-provoking visual stimuli, is the most effective treatment. Scalable computerized exposure therapy requires automated prediction of fear directly from image content to adapt stimulus selection and treatment intensity. Whether such predictions can be made reliably and generalize across individuals and stimuli, however, remains unknown. Here we show that pretrained convolutional and transformer vision models, adapted via transfer learning, accurately predict group-level perceived fear for spider-related images, even when evaluated on new people and new images, achieving a mean absolute error (MAE) below 10 units on the 0-100 fear scale. Visual explanation analyses indicate that predictions are driven by spider-specific regions in the images. Learning-curve analyses show that transformer models are data efficient and approach performance saturation with the available data (~300 images). Prediction errors increase for very low and very high fear levels and within specific categories of images. These results establish transparent, data-driven fear estimation from images, laying the groundwork for adaptive digital mental health tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04889 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SpiderNets: Vision Models Predict Human Fear From Aversive Images Pegler, Dominik Steyrl, David Zhang, Mengfan Karner, Alexander Arato, Jozsef Scharnowski, Frank Melinscak, Filip Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning Phobias are common and impairing, and exposure therapy, which involves confronting patients with fear-provoking visual stimuli, is the most effective treatment. Scalable computerized exposure therapy requires automated prediction of fear directly from image content to adapt stimulus selection and treatment intensity. Whether such predictions can be made reliably and generalize across individuals and stimuli, however, remains unknown. Here we show that pretrained convolutional and transformer vision models, adapted via transfer learning, accurately predict group-level perceived fear for spider-related images, even when evaluated on new people and new images, achieving a mean absolute error (MAE) below 10 units on the 0-100 fear scale. Visual explanation analyses indicate that predictions are driven by spider-specific regions in the images. Learning-curve analyses show that transformer models are data efficient and approach performance saturation with the available data (~300 images). Prediction errors increase for very low and very high fear levels and within specific categories of images. These results establish transparent, data-driven fear estimation from images, laying the groundwork for adaptive digital mental health tools. |
| title | SpiderNets: Vision Models Predict Human Fear From Aversive Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2509.04889 |