Recent efforts to understand the mechanisms underlying human cooperation have focused on the notion of trust, with research illustrating that both initial impressions and previous interactions impact the amount of trust people place in a partner. Less is known, however, about how these two types of information interact in iterated exchanges. The present study examined how implicit initial trustworthiness information interacts with experienced trustworthiness in a repeated Trust Game. Consistent with our hypotheses, these two factors reliably influence behavior both independently and synergistically, in terms of how much money players were willing to entrust to their partner and also in their post-game subjective ratings of trustworthiness. To further understand this interaction, we used Reinforcement Learning models to test several distinct processing hypotheses. These results suggest that trustworthiness is a belief about probability of reciprocation based initially on implicit judgments, and then dynamically updated based on experiences. This study provides a novel quantitative framework to conceptualize the notion of trustworthiness.
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