Mayrhofer, Ralf

[["dc.bibliographiccitation.firstpage","1880"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Journal of Experimental Psychology: Learning, Memory, and Cognition"],["dc.bibliographiccitation.lastpage","1910"],["dc.bibliographiccitation.volume","44"],["dc.contributor.author","Bramley, Neil R."],["dc.contributor.author","Gerstenberg, Tobias"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Lagnado, David A."],["dc.date.accessioned","2020-12-10T18:09:26Z"],["dc.date.available","2020-12-10T18:09:26Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1037/xlm0000548"],["dc.identifier.eissn","1939-1285"],["dc.identifier.issn","0278-7393"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73655"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Time in causal structure learning."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","54"],["dc.bibliographiccitation.journal","Cognitive Psychology"],["dc.bibliographiccitation.lastpage","84"],["dc.bibliographiccitation.volume","96"],["dc.contributor.author","Meder, Björn"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.date.accessioned","2020-12-10T14:23:11Z"],["dc.date.available","2020-12-10T14:23:11Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1016/j.cogpsych.2017.05.002"],["dc.identifier.issn","0010-0285"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71868"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Diagnostic causal reasoning with verbal information"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","3-4"],["dc.bibliographiccitation.journal","International Journal of Psychology"],["dc.bibliographiccitation.volume","43"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Waldmann, Michael R."],["dc.date.accessioned","2018-11-07T11:14:32Z"],["dc.date.available","2018-11-07T11:14:32Z"],["dc.date.issued","2008"],["dc.identifier.isi","000259264300366"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/54142"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Psychology Press"],["dc.publisher.place","Hove"],["dc.title","Mind reading aliens: Inferring causal structure from covariational data"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","266"],["dc.bibliographiccitation.journal","Cognition"],["dc.bibliographiccitation.lastpage","297"],["dc.bibliographiccitation.volume","179"],["dc.contributor.author","Rothe, Anselm"],["dc.contributor.author","Deverett, Ben"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Kemp, Charles"],["dc.date.accessioned","2020-12-10T14:23:11Z"],["dc.date.available","2020-12-10T14:23:11Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1016/j.cognition.2018.06.003"],["dc.identifier.issn","0010-0277"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71865"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Successful structure learning from observational data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2137"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Cognitive Science"],["dc.bibliographiccitation.lastpage","2150"],["dc.bibliographiccitation.volume","40"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2018-11-07T10:06:18Z"],["dc.date.available","2018-11-07T10:06:18Z"],["dc.date.issued","2016"],["dc.description.abstract","Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism (sufficiency or necessity; Experiment 1). These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally (Experiment 2) and appear to be domain-general (Experiment 3). Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors."],["dc.identifier.doi","10.1111/cogs.12318"],["dc.identifier.isi","000388565700014"],["dc.identifier.pmid","26522238"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39062"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1551-6709"],["dc.relation.issn","0364-0213"],["dc.title","Sufficiency and Necessity Assumptions in Causal Structure Induction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","277"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Psychological Review"],["dc.bibliographiccitation.lastpage","301"],["dc.bibliographiccitation.volume","121"],["dc.contributor.author","Meder, Bjoern"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2018-11-07T09:37:59Z"],["dc.date.available","2018-11-07T09:37:59Z"],["dc.date.issued","2014"],["dc.description.abstract","Our research examines the normative and descriptive adequacy of alternative computational models of diagnostic reasoning from single effects to single causes. Many theories of diagnostic reasoning are based on the normative assumption that inferences from an effect to its cause should reflect solely the empirically observed conditional probability of cause given effect. We argue against this assumption, as it neglects alternative causal structures that may have generated the sample data. Our structure induction model of diagnostic reasoning takes into account the uncertainty regarding the underlying causal structure. A key prediction of the model is that diagnostic judgments should not only reflect the empirical probability of cause given effect but should also depend on the reasoner's beliefs about the existence and strength of the link between cause and effect. We confirmed this prediction in 2 studies and showed that our theory better accounts for human judgments than alternative theories of diagnostic reasoning. Overall, our findings support the view that in diagnostic reasoning people go \"beyond the information given\" and use the available data to make inferences on the (unobserved) causal rather than on the (observed) data level."],["dc.identifier.doi","10.1037/a0035944"],["dc.identifier.isi","000340470300001"],["dc.identifier.pmid","25090421"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32963"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Psychological Assoc"],["dc.relation.issn","1939-1471"],["dc.relation.issn","0033-295X"],["dc.title","Structure Induction in Diagnostic Causal Reasoning"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","485"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Cognition"],["dc.bibliographiccitation.lastpage","490"],["dc.bibliographiccitation.volume","132"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2018-11-07T09:36:17Z"],["dc.date.available","2018-11-07T09:36:17Z"],["dc.date.issued","2014"],["dc.description.abstract","The question how agent and patient roles are assigned to causal participants has largely been neglected in the psychological literature on force dynamics. Inspired by the linguistic theory of Dowty (1991), we propose that agency attributions are based on a prototype concept of human intervention. We predicted that the number of criteria a participant in a causal interaction shares with this prototype determines the strength of agency intuitions. We showed in two experiments using versions of Michotte's (1963) launching scenarios that agency intuitions were moderated by manipulations of the context prior to the launching event. Altering features, such as relative movement, sequence of visibility, and self-propelled motion, tended to increase agency attributions to the participant that is normally viewed as patient in the standard scenario. (C) 2014 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.cognition.2014.05.013"],["dc.identifier.isi","000340013900020"],["dc.identifier.pmid","24955502"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32581"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","1873-7838"],["dc.relation.issn","0010-0277"],["dc.title","Indicators of causal agency in physical interactions: The role of the prior context"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","85"],["dc.bibliographiccitation.journal","The Psychology of Learning and Motivation"],["dc.bibliographiccitation.lastpage","127"],["dc.bibliographiccitation.volume","65"],["dc.contributor.author","Waldmann, M. R."],["dc.contributor.author","Mayrhofer, Ralf"],["dc.date.accessioned","2018-11-07T10:20:03Z"],["dc.date.available","2018-11-07T10:20:03Z"],["dc.date.issued","2016"],["dc.description.abstract","The main goal of this chapter is to defend a new view on causal reasoning, a hybrid representation account. In both psychology and philosophy, different frameworks of causal reasoning compete, each endowed with its distinctive strengths and weaknesses and its preferred domains of application. Three frameworks are presented that either focus on dependencies, dispositions, or processes. Our main claim is that despite the beauty of a parsimonious unitary account, there is little reason to assume that people are restricted to one type of representation of causal scenarios. In contrast to causal pluralism, which postulates the coexistence of different representations in causal reasoning, our aim is to show that competing representations do not only coexist, they can also actively influence each other. In three empirical case studies, we demonstrate how causal dependency, causal dispositional, and causal process representations mutually interact in generating complex representations driving causal inferences."],["dc.identifier.doi","10.1016/bs.plm.2016.04.001"],["dc.identifier.isi","000383370100003"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/41800"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.isbn","978-0-12-805182-5"],["dc.relation.issn","0079-7421"],["dc.title","Hybrid Causal Representations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","65"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Cognitive Science"],["dc.bibliographiccitation.lastpage","95"],["dc.bibliographiccitation.volume","39"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2018-11-07T10:03:40Z"],["dc.date.available","2018-11-07T10:03:40Z"],["dc.date.issued","2015"],["dc.description.abstract","Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented a causal Bayes net model with separate error sources for causes and effects. In several experiments, we tested this new model using the size of Markov violations as the empirical indicator of differential assumptions about the sources of error. As predicted by the model, the size of Markov violations was influenced by the location of the agents and was moderated by the causal structure and the type of causal variables."],["dc.identifier.doi","10.1111/cogs.12132"],["dc.identifier.isi","000348656300003"],["dc.identifier.pmid","24831193"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38526"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1551-6709"],["dc.relation.issn","0364-0213"],["dc.title","Agents and Causes: Dispositional Intuitions As a Guide to Causal Structure"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]