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

TL;DR

PriorProbe is a novel method that accurately recovers individual-specific cognitive priors using MCMC with People, enhancing neural network personalization in facial expression recognition and improving prediction accuracy.

Contribution

The paper introduces PriorProbe, a new approach for eliciting fine-grained individual priors that improves personalization of neural networks in facial expression tasks.

Findings

Recovered priors lead to significant performance improvements.

PriorProbe outperforms existing methods and priors.

Personalized priors preserve ground-truth inference.

Abstract

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.