Waldmann, Michael R.

[["dc.bibliographiccitation.firstpage","143"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Cognitive Processing"],["dc.bibliographiccitation.lastpage","158"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Waldmann, M. R."],["dc.contributor.author","Meder, Bjoern"],["dc.contributor.author","von Sydow, Momme"],["dc.contributor.author","Hagmayer, York"],["dc.date.accessioned","2018-11-07T08:43:27Z"],["dc.date.available","2018-11-07T08:43:27Z"],["dc.date.issued","2010"],["dc.description.abstract","The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two interconnected causal relations forming a causal chain (Experiment 1) or a common-cause model (Experiments 2a, b). One of the three events (i.e., the intermediate event of the chain, or the common cause) was presented as a set of uncategorized exemplars. Although participants were not provided with any feedback about category labels, they tended to induce categories in the first phase that maximized the predictability of their causes or effects. In the second causal learning phase, participants had the choice between transferring the newly learned categories from the first phase at the cost of suboptimal predictions, or they could induce a new set of optimally predictive categories for the second causal relation, but at the cost of proliferating different category schemes for the same set of events. It turned out that in all three experiments learners tended to transfer the categories entailed by the first causal relation to the second causal relation."],["dc.identifier.doi","10.1007/s10339-009-0267-x"],["dc.identifier.isi","000277096800005"],["dc.identifier.pmid","19562395"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/4249"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19968"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.publisher.place","Heidelberg"],["dc.relation.issn","1612-4782"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","The tight coupling between category and causal learning"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","842"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Cognitive Science"],["dc.bibliographiccitation.lastpage","873"],["dc.bibliographiccitation.volume","35"],["dc.contributor.author","Hagmayer, York"],["dc.contributor.author","Meder, Bjoern"],["dc.contributor.author","von Sydow, Momme"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2018-11-07T08:54:41Z"],["dc.date.available","2018-11-07T08:54:41Z"],["dc.date.issued","2011"],["dc.description.abstract","The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis."],["dc.identifier.doi","10.1111/j.1551-6709.2011.01179.x"],["dc.identifier.isi","000292511600004"],["dc.identifier.pmid","21609354"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/22727"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","0364-0213"],["dc.title","Category Transfer in Sequential Causal Learning: The Unbroken Mechanism Hypothesis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","3-4"],["dc.bibliographiccitation.journal","International Journal of Psychology"],["dc.bibliographiccitation.volume","43"],["dc.contributor.author","von Sydow, Momme"],["dc.contributor.author","Meder, Bjoern"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2018-11-07T11:14:32Z"],["dc.date.available","2018-11-07T11:14:32Z"],["dc.date.issued","2008"],["dc.format.extent","42"],["dc.identifier.isi","000259264300464"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/54144"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Psychology Press"],["dc.publisher.place","Hove"],["dc.relation.issn","0020-7594"],["dc.title","Transitivity heuristics in causal reasoning"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","249"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Memory & Cognition"],["dc.bibliographiccitation.lastpage","264"],["dc.bibliographiccitation.volume","37"],["dc.contributor.author","Meder, Bjoern"],["dc.contributor.author","Hagmayer, York"],["dc.contributor.author","Waldmann, Michael R."],["dc.date.accessioned","2018-11-07T08:31:14Z"],["dc.date.available","2018-11-07T08:31:14Z"],["dc.date.issued","2009"],["dc.description.abstract","Recent studies have shown that people have the capacity to derive interventional predictions for previously unseen actions from observational knowledge, a finding that challenges associative theories of causal learning and reasoning (e.g., Meder, Hagmayer, & Waldmann, 2008). Although some researchers have claimed that such inferences are based mainly on qualitative reasoning about the structure of a causal system (e.g., Sloman, 2005), we propose that people use both the causal structure and its parameters for their inferences. We here employ an observational trial-by-trial learning paradigm to test this prediction. In Experiment 1, the causal strength of the links within a given causal model was varied, whereas in Experiment 2, base rate information was manipulated while keeping the structure of the model constant. The results show that learners' causal judgments were strongly affected by the observed learning data despite being presented with identical hypotheses about causal structure. The findings show furthermore that participants correctly distinguished between observations and hypothetical interventions. However, they did not adequately differentiate between hypothetical and counterfactual interventions."],["dc.identifier.doi","10.3758/MC.37.3.249"],["dc.identifier.isi","000263942300001"],["dc.identifier.pmid","19246341"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/17076"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Psychonomic Soc Inc"],["dc.publisher.place","Austin"],["dc.relation.conference","49th Annual Meeting of Experimental Psychologists"],["dc.relation.eventlocation","Trier, GERMANY"],["dc.relation.issn","0090-502X"],["dc.title","The role of learning data in causal reasoning about observations and interventions"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["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","75"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Psychonomic Bulletin & Review"],["dc.bibliographiccitation.lastpage","80"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Meder, Bjoern"],["dc.contributor.author","Hagmayer, York"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2021-06-01T10:48:52Z"],["dc.date.available","2021-06-01T10:48:52Z"],["dc.date.issued","2008"],["dc.description.abstract","Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account."],["dc.identifier.doi","10.3758/PBR.15.1.75"],["dc.identifier.isi","000257217600010"],["dc.identifier.pmid","18605483"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/86080"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Psychonomic Soc Inc"],["dc.relation.eissn","1531-5320"],["dc.relation.issn","1069-9384"],["dc.title","Inferring interventional predictions from observational learning data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]