A central research question in cognitive neuroscience, psychology, and economics is to understand how humans interpret the behavior of others. Despite much research on humans’ Theory of Mind, we still lack a computationally and neurobiologically plausible model. In the ERC project TrackingMinds, we propose to fill this gap by testing the hypothesis that humans utilize their own decision-making system to observe and understand actions by simulating them with their own mind.
The multi-modal and mathematically rigorous approach of TrackingMinds will advance our understanding of human mentalizing abilities profoundly. This improved knowledge can lead to many societal benefits. First, it can help to devise better interactive AI systems that take not only decision outcomes but also process information such as response times or eye movements into account. Second, it can have strong implications for equilibrium predictions in economic theory, because not only which but also how a decision is made can reveal critical information about underlying beliefs, preferences, and knowledge. Third, it can foster new interventions of social mental disorders such as autism and social anxiety, which might be characterized by a limited ability to simulate others’ minds.
The objectives of TrackingMinds are (1) to investigate whether people take process information of decisions such as response times and eye movements into account when inferring others’ preferences, and if so, whether their inferences and predictions of others align with our computational framework. These predictions will also be tested on the neural level, with a focus on connecting decision neuroscience with research on the neural basis of time perception. Furthermore, we seek (2) to test whether humans simulate others’ decisions multiple times to improve their predictions. This research question will be assessed on both the behavioral as well as the neural level. Finally, we seek (3) to devise a multi-agent artificial intelligence (AI) system that takes process information into account to improve human-AI interactions in coordination tasks and cooperative settings.