“Virus and Epidemic”: Causal Knowledge Activates Prediction Error Circuitry

Knowledge about cause and effect relationships (e.g., virus–epidemic) is essential for predicting changes in the environment and for anticipating the consequences of events and one's own actions. Although there is evidence that predictions and learning from prediction errors are instrumental in...

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Bibliographic Details
Published in:Journal of cognitive neuroscience 2010-10, Vol.22 (10), p.2151-2163
Main Authors: Fenker, Daniela B., Schoenfeld, Mircea A., Waldmann, Michael R., Schuetze, Hartmut, Heinze, Hans-Jochen, Duezel, Emrah
Format: Article
Language:English
Subjects:
Adult
Association Learning - physiology
Brain
Brain - blood supply
Brain - physiology
Brain Mapping
Causality
Cognition & reasoning
Female
Functional Laterality
Humans
Image Processing, Computer-Assisted - methods
Information processing
Magnetic Resonance Imaging - methods
Male
Neural Pathways - blood supply
Neural Pathways - physiology
Neuropsychological Tests
Neurosciences
NMR
Nuclear magnetic resonance
Oxygen - blood
Photic Stimulation - methods
Predictive Value of Tests
Reaction Time - physiology
Semantics
Vocabulary
Young Adult
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Summary:Knowledge about cause and effect relationships (e.g., virus–epidemic) is essential for predicting changes in the environment and for anticipating the consequences of events and one's own actions. Although there is evidence that predictions and learning from prediction errors are instrumental in acquiring causal knowledge, it is unclear whether prediction error circuitry remains involved in the mental representation and evaluation of causal knowledge already stored in semantic memory. In an fMRI study, participants assessed whether pairs of words were causally related (e.g., virus–epidemic) or noncausally associated (e.g., emerald–ring). In a second fMRI study, a task cue prompted the participants to evaluate either the causal or the noncausal associative relationship between pairs of words. Causally related pairs elicited higher activity in OFC, amygdala, striatum, and substantia nigra/ventral tegmental area than noncausally associated pairs. These regions were also more activated by the causal than by the associative task cue. This network overlaps with the mesolimbic and mesocortical dopaminergic network known to code prediction errors, suggesting that prediction error processing might participate in assessments of causality even under conditions when it is not explicitly required to make predictions.
ISSN:0898-929X
1530-8898
DOI:10.1162/jocn.2009.21387