title: Bayesian models of mentalizing.
abstract: Surprisingly effortless is the human capacity known as "mentalizing", i.e., the ability to explain and predict the behavior of others by attributing to them independent mental states, such as beliefs, desires, emotions or intentions. This capacity is, among other factors, dependent on the correct anticipation of the dynamics of facially expressed emotions based on our beliefs and experience. Important information about the neural processes involved in mentalizing can be derived from dynamic recordings of neural activity such as the EEG. We here exemplify how the so-called Bayesian probabilistic models can help us to model the neural dynamic involved in the perception of clips that evolve from neutral to emotionally laden faces. Contrasting with conventional models, in Bayesian models, probabilities can be used to dynamically update beliefs based on new incoming information. Our results show that a reproducible model of the neural dynamic involved in the appraisal of facial expression can be derived from the grand mean ERP over five subjects. One of the two models used to predict the individual subject dynamic yield correct estimates for four of the five subjects analyzed. These results encourage the future use of Bayesian formalism to build more detailed models able to describe the single trial dynamic.
authors: Electrical Neuroimaging Group, Department of Clinical Neuroscience, Geneva University Hospital, Geneva, Switzerland. Rolando.Grave@hcuge.ch
authors: Rolando
authors: R
authors: Grave de Peralta Menendez
authors: Amal
authors: A
authors: Achaïbou
authors: Pierre
authors: P
authors: Bessière
authors: Patrik
authors: P
authors: Vuilleumier
authors: Sara
authors: S
authors: Gonzalez Andino
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D004569:Electroencephalography; D004644:Emotions / Q000502:physiology; D005260:Female; D006801:Humans; D008297:Male; D008606:Mental Processes / Q000502:physiology*; D017711:Nonlinear Dynamics; D010775:Photic Stimulation; D011930:Reaction Time