Summary
A core research question in cognitive neuroscience and many other disciplines is to understand how humans interpret the behavior of others. Despite the plethora of empirical work on human Theory of Mind, we still lack a computationally and neurobiologically plausible model. The goal of the current project is to fill this gap by leveraging our extensive knowledge of decision making, which has been formalized as a process of evidence accumulation. Thus, my central hypothesis is that humans utilize their own evidence-accumulation machinery to track others’ minds and to infer their hidden beliefs and preferences. Importantly, my theory goes far beyond previous accounts (e.g., the mirror neuron system), as it specifies not only how we observe others, but also how we predict and learn from their decisions.
I will subject this hypothesis to a series of studies that test its behavioral, physiological and neurobiological implications. Thereto, I will employ cutting-edge cognitive neuroscience tools, including cognitive modeling, eye tracking, functional magnetic resonance imaging, and electroencephalography (EEG). Moreover, I will use EEG hyperscanning to probe the synchronization of brain signals related to evidence accumulation in interacting people. Finally, I will develop a multi-agent artificial intelligence (AI) system that can infer the hidden beliefs of other agents on the basis of their decision processes. I will then show that this AI system provides superior performance in coordinating its actions with human partners.
The multi-modal and mathematically rigorous approach of TrackingMinds will advance our understanding of human mentalizing abilities profoundly. Thereby, it will stimulate further research and applications. First, it can help to devise better interactive AI systems. Second, it has strong implications for equilibrium predictions in economic theory. Third, it can foster new interventions of social mental disorders such as autism and social anxiety.
I will subject this hypothesis to a series of studies that test its behavioral, physiological and neurobiological implications. Thereto, I will employ cutting-edge cognitive neuroscience tools, including cognitive modeling, eye tracking, functional magnetic resonance imaging, and electroencephalography (EEG). Moreover, I will use EEG hyperscanning to probe the synchronization of brain signals related to evidence accumulation in interacting people. Finally, I will develop a multi-agent artificial intelligence (AI) system that can infer the hidden beliefs of other agents on the basis of their decision processes. I will then show that this AI system provides superior performance in coordinating its actions with human partners.
The multi-modal and mathematically rigorous approach of TrackingMinds will advance our understanding of human mentalizing abilities profoundly. Thereby, it will stimulate further research and applications. First, it can help to devise better interactive AI systems. Second, it has strong implications for equilibrium predictions in economic theory. Third, it can foster new interventions of social mental disorders such as autism and social anxiety.
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| Web resources: | https://cordis.europa.eu/project/id/948545 |
| Start date: | 01-01-2021 |
| End date: | 30-09-2026 |
| Total budget - Public funding: | 1 499 129,03 Euro - 1 499 129,00 Euro |
Cordis data
Original description
A core research question in cognitive neuroscience and many other disciplines is to understand how humans interpret the behavior of others. Despite the plethora of empirical work on human Theory of Mind, we still lack a computationally and neurobiologically plausible model. The goal of the current project is to fill this gap by leveraging our extensive knowledge of decision making, which has been formalized as a process of evidence accumulation. Thus, my central hypothesis is that humans utilize their own evidence-accumulation machinery to track others’ minds and to infer their hidden beliefs and preferences. Importantly, my theory goes far beyond previous accounts (e.g., the mirror neuron system), as it specifies not only how we observe others, but also how we predict and learn from their decisions.I will subject this hypothesis to a series of studies that test its behavioral, physiological and neurobiological implications. Thereto, I will employ cutting-edge cognitive neuroscience tools, including cognitive modeling, eye tracking, functional magnetic resonance imaging, and electroencephalography (EEG). Moreover, I will use EEG hyperscanning to probe the synchronization of brain signals related to evidence accumulation in interacting people. Finally, I will develop a multi-agent artificial intelligence (AI) system that can infer the hidden beliefs of other agents on the basis of their decision processes. I will then show that this AI system provides superior performance in coordinating its actions with human partners.
The multi-modal and mathematically rigorous approach of TrackingMinds will advance our understanding of human mentalizing abilities profoundly. Thereby, it will stimulate further research and applications. First, it can help to devise better interactive AI systems. Second, it has strong implications for equilibrium predictions in economic theory. Third, it can foster new interventions of social mental disorders such as autism and social anxiety.
Status
SIGNEDCall topic
ERC-2020-STGUpdate Date
27-04-2024
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