Bayesian Inference of Psychometric Variables From Brain and Behavior in Implicit Association Tests
Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Implicit Association Test (IAT) as the data generation engine, aiming to overcome the limited predictive performance (typically under 0.7 AUC) of the gold-standard D-score method, which relies solely on reaction times. Approach. We propose a sparse hierarchical Bayesian model that leverages multi-modal data to predict experiences related to mental illness symptoms in new participants. The model is a multivariate generalization of the D-score with trainable parameters, engineered for parameter efficiency in the small-cohort regime typical of IAT s...
| Authors | Christian A. Kothe, Sean Mullen, Michael V. Bronstein et al. |
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| Primary category | cs.LG |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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