Waldmann, Michael R.

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","International Journal of Comparative Psychology"],["dc.bibliographiccitation.lastpage","18"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Blaisdell, Aaron P."],["dc.contributor.author","Leising, Kenneth J."],["dc.contributor.author","Stahlman, W. David"],["dc.contributor.author","Waldmann, Michael R."],["dc.date.accessioned","2019-07-10T08:13:21Z"],["dc.date.available","2019-07-10T08:13:21Z"],["dc.date.issued","2009"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5870"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61215"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject","Sensory Preconditioning"],["dc.subject.ddc","570"],["dc.title","Rats Distinguish Between Absence of Events and Lack of Information in Sensory Preconditioning"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","119"],["dc.bibliographiccitation.journal","The Open Psychology Journal"],["dc.bibliographiccitation.lastpage","135"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Meder, Björn"],["dc.contributor.author","Gerstenberg, Tobias"],["dc.contributor.author","Hagmayer, York"],["dc.contributor.author","Waldmann, Michael R."],["dc.date.accessioned","2019-07-09T11:53:10Z"],["dc.date.available","2019-07-09T11:53:10Z"],["dc.date.issued","2010"],["dc.description.abstract","Recently, a number of rational theories have been put forward which provide a coherent formal framework for modeling different types of causal inferences, such as prediction, diagnosis, and action planning. A hallmark of these theories is their capacity to simultaneously express probability distributions under observational and interventional scenarios, thereby rendering it possible to derive precise predictions about interventions (“doing”) from passive observations (“seeing”). In Part 1 of the paper we discuss different modeling approaches for formally representing interventions and review the empirical evidence on how humans draw causal inferences based on observations or interventions. We contrast deterministic interventions with imperfect actions yielding unreliable or unknown outcomes. In Part 2, we discuss alternative strategies for making interventional decisions when the causal structure is unknown to the agent. A Bayesian approach of rational causal inference, which aims to infer the structure and its parameters from the available data, provides the benchmark model. This account is contrasted with a heuristic approach which knows categories of causes and effects but neglects further structural information. The results of computer simulations show that despite its computational parsimony the heuristic approach achieves very good performance compared to the Bayesian model."],["dc.identifier.doi","10.2174/1874350101003010119"],["dc.identifier.fs","578454"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6972"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60356"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1874-3501"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","570"],["dc.title","Observing and Intervening: Rational and Heuristic Models of Causal Decision Making"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2151"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Journal of Cognitive Neuroscience"],["dc.bibliographiccitation.lastpage","2163"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Fenker, Daniela B."],["dc.contributor.author","Schoenfeld, Mircea A."],["dc.contributor.author","Waldmann, Michael R."],["dc.contributor.author","Schuetze, Hartmut"],["dc.contributor.author","Heinze, Hans-Jochen"],["dc.contributor.author","Duezel, Emrah"],["dc.date.accessioned","2019-07-10T08:13:50Z"],["dc.date.available","2019-07-10T08:13:50Z"],["dc.date.issued","2010"],["dc.description.abstract","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 oneʼs 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 alsomore 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"],["dc.identifier.fs","578453"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7550"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61353"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Biologie und Psychologie"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","570"],["dc.title","“Virus and Epidemic”: Causal Knowledge Activates Prediction Error Circuitry"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]