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Predictive modeling of optimism bias using gray matter cortical thickness.
Raviteja Kotikalapudi1,2, Dominik A Moser3, Mihai Dricu3
1Institute of Psychology, University of Bern, Fabrikstrasse 8, 3012, Bern, Switzerland. raviteja.kotikalapudi@uk-essen.de.
Scientific Reports
|January 7, 2023
Summary
This study used machine learning to predict optimism bias (OB), finding that brain structure could forecast personal OB but not social OB. This advance aids understanding of positive psychology and well-being.
Area of Science:
- Neuroscience
- Psychology
- Cognitive Science
Background:
- Optimism bias (OB) is a well-documented cognitive tendency where individuals exhibit more favorable future outcome expectancies for themselves (personal OB) and their social groups (social OB) compared to rivals.
- Previous research using MRI has identified neural correlates of OB, but these findings are primarily associative.
- A key question remains whether neuroimaging data can accurately predict individual differences in optimism bias.
Purpose of the Study:
- To investigate the predictive power of neuroimaging data, specifically gray matter cortical thickness, for personal optimism bias (POB) and social optimism bias (SOB).
- To apply machine learning techniques to forecast POB and SOB at the individual level.
Main Methods:
- A validated soccer paradigm was employed to quantify personal (self vs. rival) and social (in-group vs. out-group) optimism biases in participants.
- Machine learning models were trained using gray matter cortical thickness data to predict POB and SOB.
- Voxel-based mass-univariate analyses were used to identify key brain regions associated with the predictive models.
Main Results:
- The predictive model successfully explained 17% of the variance in individual variability for personal optimism bias (POB) (R² = 0.17).
- The model did not achieve significant predictive accuracy for social optimism bias (SOB).
- Key brain regions identified as predictors for POB included the rostral-caudal anterior cingulate cortex, pars orbitalis, and entorhinal cortex, areas previously linked to OB.
Conclusions:
- Gray matter cortical thickness can predict personal optimism bias, suggesting a neurobiological basis for individual differences in self-focused optimism.
- The predictive power for social optimism bias remains elusive with current methods, indicating potential differences in the neural underpinnings of self- versus group-based optimism.
- The development of predictive models is crucial for advancing our understanding of positive psychology, individual well-being, and the neural mechanisms of optimism.
