Decision-Making in a Social Multi-Armed Bandit Task: Behavior, Electrophysiology and Pupillometry
- Julia Adrian, Cognitive Science, UC San Diego, La Jolla, California, United States
- Siddharth Siddharth, Electrical Engineering, UC San Diego, La Jolla, California, United States
- Zain Baquar, Cognitive Science, UC San Diego, La Jolla, California, United States
- Tzyy-Ping Jung, Bioengineering, UC San Diego, La Jolla, California, United States
- Gedeon Deak, UC San Diego, San Diego, California, United States
AbstractUnderstanding, predicting, and learning from other people’s actions are fundamental human social-cognitive skills. Little is known about how and when we consider other’s actions and outcomes when making our own decisions. We developed a novel task to study social influence in decision-making: the social multi-armed bandit task. This task assesses how people learn policies for optimal choices based on their own outcomes and another player's (observed) outcomes. The majority of participants integrated information gained through observation of their partner similarly as information gained through their own actions. This lead to a suboptimal decision-making strategy. Interestingly, event-related potentials time-locked to stimulus onset qualitatively similar but the amplitudes are attenuated in the solo compared to the dyadic version. This might indicate that arousal and attention after receiving a reward are sustained when a second agent is present but not when playing alone.
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