A key behavioral characteristic of autistic people are difficulties with mentalization – understanding and reacting to the mental states of others. Despite their social and clinical implications, the specific neuro-cognitive mechanisms that may give rise to these difficulties have remained elusive. In my presentation, I will argue that one reason for this impasse may be limitations of the experimental approaches used to assess metalization. These usually assess static application of fixed mentalization strategies and fail to capture a defining characteristic of real-life social interactions – namely that others’ behavioral strategies and associated mental states are not static but in constant flux over time. I will present a novel behavioral paradigm and computational model that can specifically assess adaptive mentalization: Dynamic estimation of the behavioral sophistication of an interaction partner and flexible selection of an appropriate own behavioral strategy. Using this paradigm and model in fMRI experiments, we have identified a multivariate neural fingerprint that can predict out of sample to what degree people can dynamically adapt their mental models of others to their changing strategies. We have also used this model and neural fingerprint in a preregistered study of autistic individuals, revealing that stronger autistic traits are associated with both a decreased ability for adaptive mentalization and weaker expression of the very same multivariate neural fingerprint. We hope that these results illustrate how we can measure, with both behavioral and neural data, a specific deficit in adaptive mentalization that is linked to autistic traits. This not only advances the theoretical understanding of autism but hopefully may help in improving its assessment and diagnosis.


Prof. Christian Ruff, PhD
Zurich Center for Neuroeconomics (ZNE)

Department of Economics
University of Zurich

Christian Ruff – Neurocomputational Underpinnings of Altered Mentalization in Autism