<div><h3 class="t-margin-top-20"> Investigating the human ability to understand others’ actions</h3><p class="c-project-description__text" data-se="project_description"> Despite the great progress in cognitive neuroscience and related disciplines, there is a lack of a computationally and neurobiologically plausible model of humans’ interpretation of other people’s behaviour. The EU-funded TrackingMinds project will implement a multi-modal and mathematically precise approach to shed light on humans’ mental capacity to track others’ minds and to infer their hidden beliefs and preferences. TrackingMinds will focus on how we observe others as well as how we predict and learn from their decisions. The project also aims to contribute to the development of better interactive artificial intelligence systems, equilibrium predictions in economic theory and even innovative approaches against social impairments in mental disorders.</p><h2>Objective</h2><p class="c-article__text">                                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.<br/><br/>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.<br/><br/>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.                            </p></div>

Tracking the decisions of others with the own mind

Investigating the human ability to understand others' actions Despite the great progress in cognitive neuroscience and related disciplines, there is a lack of a computationally and neurobiologically plausible model of humans' interpretation of other people's behaviour. The EU-funded TrackingMinds project will implement a multi-modal and mathematically precise approach to shed light on humans' mental capacity to track others' minds and to infer their hidden beliefs and preferences. TrackingMinds will focus on how we observe others as well as how we predict and learn from their decisions. The project also aims to contribute to the development of better interactive artificial intelligence systems, equilibrium predictions in economic theory and even innovative approaches against social impairments in mental disorders.Objective 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.

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