Trusting Wisely? Developmental Changes in How Children Learn and Adapt to Partner Trustworthiness

Trusting selectively is a crucial ability in successfully navigating social environments: trusting a trustworthy partner maximizes mutual benefits; withholding trust from an untrustworthy partner minimizes chances of being exploited. Here, we ask how children's trust expectations and experienced trustworthiness inform their own trust decisions. Using a child-friendly repeated Trust Game, we employ a computational modeling approach to examine how children learn about the trustworthiness of others through experience and adjust their own trust behaviors. We tested N = 96 children ages 6-11 years who played 40 trials of the game as trustors with a trustworthy and an untrustworthy trustee. Regression analyses showed that overall, children shared more with trustworthy than untrustworthy partners and older children shared overall more than younger children. Computational models provided a more fine-grained analysis of the learning pattern: a reinforcement learning model that included parameters for individual variation in prior trust expectations and a parameter quantifying the behavioral adjustment over time provided the best fit for the data. Using this computational approach, we could ascertain that children's behavioral adjustments became less responsive over time, suggesting a shift toward more stable patterns of trust once initial impressions were formed. Together, these findings shed light on the development of children's trust interaction with novel partners and highlight the opportunity that computational modeling provides in capturing developmental differences in trust behaviors. SUMMARY: Children aged 6-11 used experience to distinguish trustworthy from untrustworthy partners in a repeated Trust Game, sharing more with the trustworthy than the untrustworthy partner. Older children shared more than younger children, although they were not significantly differentiating between the trustworthy than the untrustworthy partner. Reinforcement learning modeling suggested that children updated trust expectations from experience, with age-related differences in the efficiency of impression updating. These findings highlight both children's capacity in flexibly adjusting their economic trust behaviors and the value of computational modeling in studying economic trust development.