Abstract
Advances in computer vision have opened new avenues for clinical applications, particularly in computerized exposure therapy where visual stimuli can be dynamically adjusted based on patient responses. As a critical step toward such adaptive systems, we investigated whether pretrained computer vision models can accurately predict fear levels from spider-related images. We adapted three diverse models using transfer learning to predict human fear ratings (on a 0-100 scale) from a standardized dataset of 313 images. The models were evaluated using cross-validation, achieving an average mean absolute error (MAE) between 10.1 and 11.0. Our learning curve analysis revealed that reducing the dataset size significantly harmed performance, though further increases yielded no substantial gains. Explainability assessments showed the models' predictions were based on spider-related features. A category-wise error analysis further identified visual conditions associated with higher errors (e.g., distant views and artificial/painted spiders). These findings demonstrate the potential of explainable computer vision models in predicting fear ratings, highlighting the importance of both model explainability and a sufficient dataset size for developing effective emotion-aware therapeutic technologies.
Abstract (translated)
计算机视觉的进步为临床应用开辟了新的途径，尤其是在计算机化暴露疗法中，可以根据患者的反应动态调整视觉刺激。为了向此类自适应系统迈进的关键一步，我们研究了预训练的计算机视觉模型是否能够准确地从与蜘蛛相关的图像中预测恐惧水平。我们使用迁移学习方法改编了三种不同的模型，以根据标准化的313张图片的数据集预测人类对恐惧程度（在0到100分制上的评分）。通过交叉验证评估这些模型后发现，它们达到了平均绝对误差（MAE）为10.1至11.0的成绩。学习曲线分析表明，减少数据集大小显著影响了性能，但进一步增加数据量却没有带来实质性的改进。 在可解释性评估中，我们发现模型的预测主要基于与蜘蛛相关的特征，并且通过按类别划分错误分析后，我们还确定了一些视觉条件会导致更高的误差（例如，远距离视图和人工/画出来的蜘蛛）。这些结果证明了具有透明度的计算机视觉模型在预测恐惧评分方面的潜力，并强调了对于开发有效的感知情感的技术而言，模型可解释性和足够大的数据集的重要性。
URL
https://arxiv.org/abs/2509.04889
