Brain-Based Framework Predicts Hidden Psychological States at Work

Researchers from Pomona College have developed a brain-based framework that can predict hidden psychological states, such as feeling overwhelmed, burned out, or disengaged, in workplace settings. The framework uses noninvasive neuroimaging to identify key neural activity patterns associated with these states. The study found that functional near-infrared spectroscopy (fNIRS) recordings from 67 executives accurately classified individuals as feeling overwhelmed or in need of a new challenge, with 72.8% and 79.1% accuracy, respectively. This breakthrough has significant implications for improving employee well-being and engagement in the workplace.

Key Takeaways:

• The brain-based framework uses fNIRS to record neural activity and identifies key patterns associated with hidden psychological states, such as feeling overwhelmed or burned out.
• The framework achieved 72.8% accuracy in classifying individuals as feeling overwhelmed and 79.1% accuracy in predicting the need for a new challenge.
• The study used a multitimepoint pattern analysis (MTPA) approach to reduce timeseries dimensionality and identify specific thematic properties of the stimulus that evoked differential neural responses.
• The research suggests that neural measures can unobtrusively identify hidden and persistent psychological states in real-world settings, enabling targeted interventions that can improve well-being and engagement.
• The findings demonstrate the potential of brain-based approaches to improve employee mental health and well-being in the workplace.
• The study identified specific neural regions, including the temporal parietal junction (TPJ) and dorsal medial prefrontal cortex (dmPFC), which were associated with predicted outcomes.
• The research has significant implications for employee mental health and well-being, as well as for organizational functioning and productivity.

Statistics:

• 67 executives participated in the study, with fNIRS recordings used to record neural activity.
• The MTPA approach reduced timeseries dimensionality, enabling accurate classification of individuals as feeling overwhelmed or in need of a new challenge.
• The study achieved 72.8% accuracy in classifying individuals as feeling overwhelmed and 79.1% accuracy in predicting the need for a new challenge.
• Neural predictors were able to unobtrusively identify hidden and persistent psychological states in real-world settings.

Sources:

• Neural predictors of hidden, persistent psychological states at work. Proceedings of the National Academy of Sciences, 2025; 122(42).
• NewsRx. Findings from Pomona College Has Provided New Information about Science (Neural predictors of hidden, persistent psychological states at work). Psychology & Psychiatry Journal. November 1, 2025; p 1216.