Waldmann, Michael R.

[["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.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","27"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Cognition"],["dc.bibliographiccitation.lastpage","58"],["dc.bibliographiccitation.volume","82"],["dc.contributor.author","Waldmann, Michael R"],["dc.contributor.author","Hagmayer, York"],["dc.date.accessioned","2021-06-01T10:50:05Z"],["dc.date.available","2021-06-01T10:50:05Z"],["dc.date.issued","2001"],["dc.description.abstract","The strength of causal relations typically must be inferred on the basis of statistical relations between observable events. This article focuses on the problem that there are multiple ways of extracting statistical information from a set of events. In causal structures involving a potential cause. an effect and a third related event, the assumed causal role of this third event crucially deter-mines whether it is appropriate to control for this event when making causal assessments between the potential cause and the effect. Three experiments show that prior assumptions about the causal roles of the learning events affect the way contingencies are assessed with otherwise identical learning input. However, prior assumptions about causal roles is only one factor influencing contingency estimation. The experiments also demonstrate that processing effort affects the way statistical information is processed. These findings provide further evidence for the interaction between bottom-up and top-down influences in the acquisition of causal knowledge. They show that, apart from covariation information or knowledge about mechanisms, abstract assumptions about causal structures also may affect the learning process. (C) 2001 Elsevier Science B.V. All rights reserved."],["dc.identifier.doi","10.1016/S0010-0277(01)00141-X"],["dc.identifier.isi","000171988700002"],["dc.identifier.pmid","11672704"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/86521"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","0010-0277"],["dc.title","Estimating causal strength: the role of structural knowledge and processing effort"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1128"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Memory & Cognition"],["dc.bibliographiccitation.lastpage","1137"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Hagmayer, York"],["dc.contributor.author","Waldmann, Michael R."],["dc.date.accessioned","2021-06-01T10:48:51Z"],["dc.date.available","2021-06-01T10:48:51Z"],["dc.date.issued","2002"],["dc.description.abstract","Causal learning typically entails the problem of being confronted with a large number of potentially relevant statistical relations. One type of constraint that may guide the choice of appropriate statistical indicators of causality are assumptions about temporal delays between causes and effects. There have been a few previous studies in which the role of temporal relations in the learning of events that are experienced in real time have been investigated. However, human causal reasoning may also be based on verbally described events, rather than on direct experiences of the events to which the descriptions refer. The aim of this paper is to investigate whether assumptions about the temporal characteristics of the events that are being described also affect causal judgment. Three experiments are presented that demonstrate that different temporal assumptions about causal delays may lead to dramatically different causal judgments, despite identical learning inputs. In particular, the experiments show that temporal assumptions guide the choice of appropriate statistical indicators of causality by structuring the event stream (Experiment 1), by selecting the potential causes among a set of competing candidates (Experiment 2), and by influencing the level of aggregation of events (Experiment 3)."],["dc.identifier.doi","10.3758/BF03194330"],["dc.identifier.isi","000179794100013"],["dc.identifier.pmid","12507377"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/86075"],["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","1532-5946"],["dc.relation.issn","0090-502X"],["dc.title","How temporal assumptions influence causal judgments"],["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"]]

[["dc.bibliographiccitation.firstpage","330"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","The Quarterly Journal of Experimental Psychology"],["dc.bibliographiccitation.lastpage","355"],["dc.bibliographiccitation.volume","60"],["dc.contributor.author","Hagmayer, York"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2018-11-07T11:04:49Z"],["dc.date.available","2018-11-07T11:04:49Z"],["dc.date.issued","2007"],["dc.description.abstract","Estimates of the causal efficacy of an event need to take into account the possible presence and influence of other unobserved causes that might have contributed to the occurrence of the effect. Current theoretical approaches deal differently with this problem. Associative theories assume that at least one unobserved cause is always present. In contrast, causal Bayes net theories (including Power PC theory) hypothesize that unobserved causes may be present or absent. These theories generally assume independence of different causes of the same event, which greatly simplifies modelling learning and inference. In two experiments participants were requested to learn about the causal relation between a single cause and an effect by observing their co-occurrence (Experiment 1) or by actively intervening in the cause (Experiment 2). Participants' assumptions about the presence of an unobserved cause were assessed either after each learning trial or at the end of the learning phase. The results show an interesting dissociation. Whereas there was a tendency to assume interdependence of the causes in the online judgements during learning, the final judgements tended to be more in the direction of an independence assumption. Possible explanations and implications of these findings are discussed."],["dc.identifier.doi","10.1080/17470210601002470"],["dc.identifier.isi","000245106800004"],["dc.identifier.pmid","17366304"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/51925"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Psychology Press"],["dc.publisher.place","Hove"],["dc.relation.conference","26th Annual Meeting of the Cognitive-Science-Society"],["dc.relation.eventlocation","Chicago, IL"],["dc.relation.issn","1747-0218"],["dc.title","Inferences about unobserved causes in human contingency learning"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","27"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Cognitive Psychology"],["dc.bibliographiccitation.lastpage","58"],["dc.bibliographiccitation.volume","53"],["dc.contributor.author","Waldmann, M. R."],["dc.contributor.author","Hagmayer, York"],["dc.date.accessioned","2018-11-07T09:27:31Z"],["dc.date.available","2018-11-07T09:27:31Z"],["dc.date.issued","2006"],["dc.description.abstract","The standard approach guiding research on the relationship between categories and causality views categories as reflecting causal relations in the world. We provide evidence that the opposite direction also holds: categories that have been acquired in previous learning contexts may influence subsequent causal learning. In three experiments we show that identical causal learning input yields different attributions of causal capacity depending on the pre-existing categories to which the learning exemplars are assigned. There is a strong tendency to continue to use old conceptual schemes rather than switch to new ones even when the old categories are not optimal for predicting the new effect, and when they were motivated by goals that differed from the present context of causal discovery. However, we also found that the use of prior categories is dependent on the match between categories and causal effect. Whenever the category labels suggest natural kinds which can be plausibly related to the causal effects, transfer was observed. When the categories were arbitrary, or could not be plausibly related to the causal effect learners abandoned the categories, and used different categories to predict the causal effect. (c) 2006 Elsevier Inc. All rights reserved."],["dc.identifier.doi","10.1016/j.cogpsych.2006.01.001"],["dc.identifier.isi","000238262800002"],["dc.identifier.pmid","16497289"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30557"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Academic Press Inc Elsevier Science"],["dc.publisher.place","San diego"],["dc.relation.conference","21st Annual Conference of the Cognitive-Science-Society"],["dc.relation.eventlocation","VANCOUVER, CANADA"],["dc.relation.issn","0010-0285"],["dc.title","Categories and causality: The neglected direction"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]