The dynamic processes of giving and taking advice (so-called “advice interaction”) is one of the fundamental cornerstones of how information and social influence are spread in society. During advice interaction, advisors (i.e., those giving advice) not only exert unilateral influence over advisees (i.e., those seeking advice), but the response of advisees towards the advice also have a counteractive effect on shaping subsequent advice-giving behavior. While recent studies have started investigating the relationships between the behaviors of advisors and advisees, there is still a lack of comprehensive understanding regarding the mechanical processes that drive these interactions. This review aims to develop an interpersonal influence model that elucidates the dynamic relationship between advisees and advisors, highlighting the prevalent link between their behavioral outcomes and cognitive processes. To this end, we undertake a simultaneous review of two sub-processes inherent in both advice-taking and advice-giving: (1) Decision-making and (2) social cognition. During the advice-taking process, advisees primarily rely on and benefit from advice based on their perception of the advisor’s reliability. Consequently, advisees update their beliefs about the advisor’s reliability through social learning, considering the advisor’s expertise and intentions. In the advice-giving process, advisors offer cautious or overconfident, beneficial or misleading advice depending on their perception of their own influence and the level of respect they receive from advisees. Moreover, by monitoring the feedback (i.e., acceptance or rejection) of their advice from advisees, advisors consistently revise their perception of influence and adjust their attitudes and evaluations of advisees accordingly. Furthermore, we review the computational and neural mechanisms involved in the sub-processes of advice taking and giving, shedding light on the cognitive processes underlying interpersonal advice interactions. Empirical studies indicate that during advice taking, advisees dynamically consider both advice and their own opinions, while monitoring the outcomes of implementing the advice. The rewarding neural system plays a crucial role in guiding advisees towards achieving favorable decision outcomes. Additionally, advisees track advisors’ expertise and intentions following reinforcement-learning and Bayesian-learning rules, activating neural regions associated with Theory of Mind. This enables individuals to decipher social information and make more accurate decisions. On the other hand, advisors provide advice based on their beliefs about their current influence, requiring social inference from advisees’ feedback. Therefore, Theory of Mind and self-monitoring neural mechanisms are significantly activated when advisors give advice. Advisors receive feedback on their advice, which they use to infer the level of their influence, making it a reward learning process that evokes neural representations related to reward processing. In summary, our theoretical model framework proposes a reciprocal influence between advisors and advisees during advice interactions. This influence is manifested through the exchange of advice and the subsequent feedback. These reciprocal influences are internalized and lead to updates in social cognition, specifically in terms of beliefs about the advisor’s reliability and their impact on the advisee. These updated beliefs, in turn, shape future behaviors in giving and taking advice, as well as the overall relationship between the advisee and advisor. Crucial roles in these processes are played by reward processing, Theory of Mind, and self-monitoring. To gain a deeper understanding of how individuals integrate social beliefs and motivations to generate social behavior during advice interactions, future research should explore the use of interdisciplinary approaches such as interpersonal neuroscience and computational modeling.
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