Model-based understanding of cognitive biases

Abstract

People makes decisions and judgements that is sometimes irrational, and the systematic pattern of deviation from rationality has been studied and described in a myriads of literatures. This phenomenon is referred as ‘cognitive bias’. Various psychiatric disorders are also known to show cognitive bias such as pessimism in depression. Recently developed computational psychiatric approach has not been much applied to systematically understand people and patients’ cognitive bias. In my studies, I aimed to investigate 1) pessimistically biased perception in the panic disorder during risk learning, 2) aberrant structural network of comorbid attention deficit/hyperactivity disorder is associated with addiction severity in internet gaming disorder and 3) drift diffusion model-based understanding of the unconscious affective priming in continuous flash suppression. In the first study, I used a cognitive model to find and quantify the degree of bias in panic disorder patients. The degree of bias was related to symptom severity and this finding may give a target for future cognitive and behavioral therapy. In the second study, I used a both neuroimaging and machine-learning technique to investigate the association between the attention deficit/hyperactivity disorder and internet gaming disorder. I could reveal the neural correlate of association and provide an explanation of how those two disorders are related. Lastly, I investigated affective priming effect using drift diffusion model. Previous studies have reported a prominent priming effect only with negative emotional faces masked by continuous flash suppression, however, I found that facial identity and emotions are mapped onto different parameters. We hope that these results might provide an important clue to deeper understanding of cognitive bias exist in psychiatric disorders as well as ordinary people.