Abstract:

Interpersonal trust is the building block of corporation and resource exchange in the human society. The Trust Game is widely-used paradigm to study interpersonal trust due to its high ecological validity. The current review first introduces the TG and major paradigm variations. The aspect of paradigm variation that we especially highlight is the repeated TG (rTG), which captures the learning, reasoning and strategy updating processes compared to the single-shot TG. We then review current theoretical and empirical progresses in the computational modeling of the rTG. We specifically focus on the reinforcement learning (RL) model and the Bayesian model. The RL model essentially assumes individuals constantly update expected utility values associated with each decision options based on prediction errors, which is the difference between expected utilities and decision outcomes. The individual differences in learning capabilities are reflected in differences in learning rates. The Bayesian model essentially assumes that individuals constantly update prior beliefs about the environment based on partial observations to form posterior beliefs. The individual differences in learning capabilities are reflected in differences in the depth of theory of mind. We then reviewed behavioral studies of computational modeling of rTG. Using the RL model, previous studies have consistently shown reputation information about the trustee can not only influence initial impression about the trustworthiness level of the trustee, but also the rate of dynamic learning of feedback signal. Previous studies have formalized the Bayesian model in different ways and developed different algorithms to derive optimal behavioral solutions. These include (1) the trust level model which depicts the depth of theory of mind and the corresponding Nash-equilibrium solution, (2) the interactional partial observable Marcov Decision Process (IPOMDP) which depicts the depth of theory of mind, planning steps and greediness, and the corresponding partial observable Monte-Carlo algorithm, and (3) the subjective Bayesian model and the corresponding free-energy minimization algorithm for behavior inversion. We then review previous studies which integrate computational modeling with neuro-imaging techniques to uncover neurological bases of trust formation. The underlying logic is to extract key internal processes (e.g. the updating of prediction errors, the parameter estimation of learning rates) which are not directly observable via traditional statistics, and then establish their correlations with BOLD signals. Within the RL model, previous studies have found the updates of prediction errors were associated with the activation of striatum and anterior cingulate cortex, and the incorporation of reputation information in the trust decision process were associated with the functional interaction between the prefrontal cortex and the striatum. Within the Bayesian model, previous studies have found individuals with different depth of theory of mind primarily differed in the activation of striatum and the temporal parietal conjunction area, and that inference about intentions and adjusting behaviors based on feedback involved two independent neural systems. Lastly, we propose several prospective future directions. First, future researchers could apply the existing formalization of the Bayesian model to the rTG and apply the new type of computational modeling technique, the multilevel Gaussian filter model, which integrates both the Bayesian and the reinforcement learning component, to study the dynamic process of trust formation. Second, future researchers could incorporate non-invasive brain stimulation techniques to study the causal relationships between brain activities and behaviors. Third, computational model is an effective tool to study the impairments of trust formation among clinical samples, by comparing model formalization and parameter estimations between the clinical and healthy population. 

Key words: interpersonal trust, trust game, computational modeling, functional magnetic resonance imaging