부산대학교 도서관

Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especially crucial for computational psychiatry, in which altered computational processes underlying mental disorders are of interest. For instance, several studies employing model-based fMRI—a method for identifying brain regions correlated with latent variables—have shown that patients with mental disorders (e.g., depression) exhibit diminished neural responses to reward prediction errors (RPEs), which are the differences between experienced and predicted rewards. Such model-based analysis has the drawback that the parameter estimates and inference of latent variables are not necessarily correct—rather, they usually contain some errors. A previous study theoretically and empirically showed that the error in model-fitting does not necessarily cause a serious error in model-based fMRI. However, the study did not deal with certain situations relevant to psychiatry, such as group comparisons between patients and healthy controls. We developed a theoretical framework to explore such situations. We demonstrate that the parameter-misspecification can critically affect the results of group comparison. We demonstrate that even if the RPE response in patients is completely intact, a spurious difference to healthy controls is observable. Such a situation occurs when the ground-truth learning rate differs between groups but a common learning rate is used, as per previous studies. Furthermore, even if the parameters are appropriately fitted to individual participants, spurious group differences in RPE responses are observable when the model lacks a component that differs between groups. These results highlight the importance of appropriate model-fitting and the need for caution when interpreting the results of model-based fMRI. Author summary: Computational modeling has been utilized to infer the latent variables reflecting computational processes underlying behavior. This approach provides a predictor of neural activity by which one can test the physiological or neural measures that reflect computational processes. In this way, individual differences in physiological signals corresponding to latent variables (e.g., reward prediction error) and deficits related to mental disorders have been clarified. However, if the model contains a systematic error, the results may be erroneous. In this study, we suggest that spurious between-group (individual) differences are observable if a model parameter (e.g., learning rate) is misspecified. This bias may account for the inconsistencies in a series of studies that have addressed aberrant brain activity underpinning reward prediction error in patients with psychiatric disorders. [ABSTRACT FROM AUTHOR]