Bayesian Inference of Psychometric Variables From Brain and Behavior in Implicit Association Tests

Abstract

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 studies. Data from two IAT variants were analyzed: a suicidality-related E-IAT ($n=39$) and a psychosis-related PSY-IAT ($n=34$). Main Results. Our approach overcomes a high inter-individual variability and low within-session effect size in the dataset, reaching AUCs of 0.73 (E-IAT) and 0.76 (PSY-IAT) in the best modality configurations, though corrected 95% confidence intervals are wide ($\pm 0.18$) and results are marginally significant after FDR correction ($q=0.10$). Restricting the E-IAT to MDD participants improves AUC to 0.79 $[0.62, 0.97]$ (significant at $q=0.05$). Performance is on par with the best reference methods (shrinkage LDA and EEGNet) for each task, even when the latter were adapted to the task, while the proposed method was not. Accuracy was substantially above near-chance D-scores (0.50-0.53 AUC) in both tasks, with more consistent cross-task performance than any single reference method. Significance. Our framework shows promise for enhancing IAT-based assessment of experiences related to entrapment and psychosis, and potentially other mental health conditions, though further validation on larger and independent cohorts will be needed to establish clinical utility.

Figures (7)

• Figure 1: ($a$) Block/trial structure used for E-IAT and PSY-IAT task designs. A "congruent" or "incongruent" block represents a block of trials where the concept-attribute pair (i.e., "trapped/me") displayed is in agreement or conflict, respectively, with the subject type (patient / control). ($b$, $c$) Screenshots of PSY-IAT task showing a trial from the two block types. ($b$) depicts an incongruent trial for a person with schizophrenia (a congruent trial for a person without schizophrenia). ($c$) depicts a congruent trial for a person with schizophrenia (incongruent for a person without schizophrenia).
• Figure 2: ($a$) A diagram of the data capture setup showing the positioning of the sensors relative to the subject. ($b$) A photograph of the data collection setup at the University of Minnesota used for the E-IAT task data collection. The same setup was used for the collection of the PSY-IAT task at the Minneapolis VA Medical Center.
• Figure 3: Diagram for the USBL model, shown for a combination of EEG and FAU modalities. Here $T$ is the number of trials in a session and $P$ is the number of participants in the dataset being analyzed. The modality superscripts EEG and FAU have been shortened to E and F, respectively. $\bm{X}^{\mathrm{E}}$ and $\bm{X}^{\mathrm{F}}$ are the preprocessed observations for the current trial and participant (subscripts omitted) and $\bm{W}^{\mathrm{E}}$ and $\bm{W}^{\mathrm{F}}$ are the associated weight matrices. $y$ is the dependent variable for the current participant (same for all trials), $\bm{\alpha}$ is the vector of per-modality weights $\alpha_m$, $\bm{A}$ is the latent EEG contrast matrix, $\bm{\beta}^{\mathrm{F}}$ is the unscaled weight matrix for the FAU modality and $\tau$ and $\bm{\lambda}$ are the global and local (per-channel) horseshoe scales, respectively. $\bm{\Sigma}_{U}$ is the model-derived EEG spatial covariance matrix and $\sigma_i$ is the learned innovation scale for the EEG smoothing (GRW) prior. $\bm{\gamma}$ is the vector of per-region hyper-parameters estimated as part of the model.
• Figure 4: EEG and eye-tracking weights for the best-performing E-IAT model; ($a$) spatial EEG weight maps, ($b$) eye tracking weight time course (note 0.0 is not at the center of the vertical axis).
• Figure 5: Mean ERP at channel F7 (top) by group (left=low entrapment, right=high entrapment) for each condition ("trapped/me" vs "free/me") with the scalp topographies underneath at time $t=500$ msec after stimulus onset.