In social interactions, inferring the interaction partner's hidden mental state is crucial for predicting their actions and optimiz- ing our responses. Effective models for this inference must account for how these mental states evolve due to the interac- tion history and environmental changes. For example, recog- nizing someone's emotional state can help forecast their be- havior. Our study investigates how making these latent states visible influences decision-making in social interactions. Us- ing the repeated trust game paradigm, we show how to use hid- den Markov models (HMM) to formally represent latent state dependent strategies of the players. HMMs fitted to human dyadic play in the trust game are then used to specify adap- tive AI agents that simulate changes in mental dispositions of human players, such as the level of trust in the opponent, dur- ing a repeated interaction. Making these artificial HMM based agents take the role of the investor and interact with real hu- man trustees, we then explore how displaying “emotion” cues to the opponent's latent state affects people's actions. We find that the presence of cues was associated with more cooperative behavior from the human trustees, and that patterns of behav- ior that promote the maintenance of cooperation emerged in the presence of latent state cues and were transferred to set- tings where the cues were subsequently hidden.