Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2008-4-4
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0896-0267
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
278-83
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Bayesian models of mentalizing.
pubmed:affiliation
Electrical Neuroimaging Group, Department of Clinical Neuroscience, Geneva University Hospital, Geneva, Switzerland. Rolando.Grave@hcuge.ch
pubmed:publicationType
Journal Article