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PriorProbe: Recovering Individual-Level Priors for Personalizing Neural Networks in Facial Expression Recognition

arXiv 2602.03882 ↗First posted 3 Feb 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Incorporating individual-level cognitive priors offers an important route to personalizing neural networks, yet accurately eliciting such priors remains challenging: existing methods either fail to uniquely identify them or introduce systematic biases. Here, we introduce PriorProbe, a novel elicitation approach grounded in Markov Chain Monte Carlo with People that recovers fine-grained, individual-specific priors. Focusing on a facial expression recognition task, we apply PriorProbe to individual participants and test whether integrating the recovered priors with a state-of-the-art neural network improves its ability to predict an individual's classification on ambiguous stimuli. The PriorProbe-derived priors yield substantial performance gains, outperforming both the neural network alone and alternative sources of priors, while preserving the network's inference on ground-truth labels. Together, these results demonstrate that PriorProbe provides a general and interpretable framework for personalizing deep neural networks.

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References · 19

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7 more
  1. 2024Representation Learning and Identity Adversarial Training for Facial Behavior Understandingcited by 12
  2. 2020Predicting human decision making in psychological tasks with recurrent neural networkscited by 11
  3. 2024Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choicecited by 10
  4. 2025Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Modelscited by 10
  5. 2024ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognitioncited by 9
  6. 2017Low-Rank RNN Adaptation for Context-Aware Language Modelingcited by 8
  7. 2025Meta-learning ecological priors from large language models explains human learning and decision makingcited by 2

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