The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk...

Risk preference changes nonlinearly across development. Although extensive developmental research on the neurotypical (NTP) population has shown that risk preference is highest during adolescence, developmental changes in risk preference in autistic (AUT) people, who tend to prefer predictable behaviors, have not been investigated. Here, we aimed to investigate these changes and underlying computational mechanisms.

We ran a game-like risk-sensitive reinforcement learning task on 75 participants aged 6-30 years (AUT group, n = 31; NTP group, n = 44). Focusing on choices between alternatives with the same objective value but different risks, we calculated the risk preference and stay probability of a risky choice after a rewarding or non-rewarding outcome. Analyses using t-tests and multiple regression analyses were conducted. Using the choice-related data of each participant, we fit four reinforcement learning models and compared the fit of each model to the data. Furthermore, we validated the results of model fitting with multiple methods, model recovery, parameter recovery, and posterior predictive check.

We found a significant difference in nonlinear developmental changes in risk preference between the AUT and NTP groups. The computational modeling approach with reinforcement learning models revealed that individual preferences for surprise modulated such preferences.

These findings indicate that for NTP people, adolescence is a developmental period involving risk preference, possibly due to lower surprise aversion. Conversely, for AUT people, who show opposite developmental change of risk preference, adolescence could be a developmental period involving risk avoidance because of low surprise preference.