Background:Confusing source code requires deliberate comprehension. Recent psychology studies have characterized individual decision-making differences as model-free (fast and automatic) and model-based (slow and deliberative) decision-making. A framework has been proposed to estimate an individual differences in the degree of model-based control.Aims:This study investigates the correlation of the degree of model-based control and the comprehension performance of confusing source code and compares the correlation of comprehension performance between the degree of model-based control and developer skills reported in previous studies.Method:We conducted an observational study using source code with and without confusing code patterns. We measured the degree of model-based control for each participant.Results:Multiple regression analysis on the results of 91 software engineers showed that the degree of model-based control has a positive correlation with the percentage of correct answers for questions about source code with confusing code patterns with statistical significance.Conclusion:In source code reviews, refactoring, and enhancement development, the appropriate developer assignment criteria using developer attributes differ between source code with and without confusing code patterns.

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.