Mayrhofer, Ralf

[["dc.bibliographiccitation.firstpage","789"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Psychonomic Bulletin & Review"],["dc.bibliographiccitation.lastpage","796"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Waldmann, M. R."],["dc.date.accessioned","2018-11-07T10:13:01Z"],["dc.date.available","2018-11-07T10:13:01Z"],["dc.date.issued","2016"],["dc.description.abstract","In the Michotte task, a ball (X) moves toward a resting ball (Y). In the moment of contact, X stops und Y starts moving. Previous studies have shown that subjects tend to view X as the causal agent (\"X launches Y\") rather than Y (\"Y stops X\"). Moreover, X tends to be attributed more force than Y (force asymmetry), which contradicts the laws of Newtonian mechanics. Recent theories of force asymmetry try to explain these findings as the result of an asymmetrical identification with either the (stronger) agent or the (weaker) patient of the causal interaction. We directly tested this assumption by manipulating attributions of causal agency while holding the properties of the causal interaction constant across conditions. In contrast to previous accounts, we found that force judgments stayed invariant across conditions in which assignments of causal agency shifted from X to Y and that even those subjects who chose Y as the causal agent gave invariantly higher force ratings to X. These results suggest that causal agency and the perception of force are conceptually independent of each other. Different possible explanations are discussed."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft [Wa 621/22-1, Ma 6545/1-2, SPP 1516]"],["dc.identifier.doi","10.3758/s13423-015-0960-y"],["dc.identifier.isi","000381177400011"],["dc.identifier.pmid","26452375"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12591"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/40352"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1531-5320"],["dc.relation.issn","1069-9384"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Causal agency and the perception of force"],["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","258"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Topics in Cognitive Science"],["dc.bibliographiccitation.lastpage","281"],["dc.bibliographiccitation.volume","14"],["dc.contributor.affiliation","Mayrhofer, Ralf; 3\r\nDepartment of Psychology\r\nUniversity of Göttingen"],["dc.contributor.affiliation","Ruggeri, Azzurra; 2\r\nMPRG iSearch\r\nMax Planck Institute for Human Development"],["dc.contributor.author","Meder, Björn"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Ruggeri, Azzurra"],["dc.date.accessioned","2021-08-12T07:45:26Z"],["dc.date.available","2021-08-12T07:45:26Z"],["dc.date.issued","2021"],["dc.date.updated","2022-06-14T21:11:25Z"],["dc.description.abstract","Abstract Dealing with uncertainty and different degrees of frequency and probability is critical in many everyday activities. However, relevant information does not always come in the form of numerical estimates or direct experiences, but is instead obtained through qualitative, rather vague verbal terms (e.g., “the virus often causes coughing” or “the train is likely to be delayed”). Investigating how people interpret and utilize different natural language expressions of frequency and probability is therefore crucial to understand reasoning and behavior in real‐world situations. While there is considerable work exploring how adults understand everyday uncertainty phrases, very little is known about how children interpret them and how their understanding develops with age. We take a developmental and computational perspective to address this issue and examine how 4‐ to 14‐year‐old children and adults interpret different terms. Each participant provided numerical estimates for 14 expressions, comprising both frequency and probability phrases. In total we obtained 2856 quantitative judgments, including 2240 judgments from children. Our findings demonstrate that adult‐like intuitions about the interpretation of everyday uncertainty terms emerge fairly early in development, with the quantitative estimates of children converging to those of adults from around 9 years on. We also demonstrate how the vagueness of verbal terms can be represented through probability distributions, which provides additional leverage for tracking developmental shifts through cognitive modeling techniques. Taken together, our findings provide key insights into the developmental trajectories underlying the understanding of everyday uncertainty terms, and open up novel methodological pathways to formally model the vagueness of probability and frequency phrases, which are abundant in our everyday life and activities."],["dc.description.abstract","Taking a computational and developmental perspective, the authors trace a developmental trajectory in the understanding of natural language expressions of frequency and probability in 4‐ to 14‐year‐old children and adults. They find that the quantitative estimates of children converge to those of adults from around 9 years on."],["dc.identifier.doi","10.1111/tops.12564"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/88467"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-448"],["dc.relation.eissn","1756-8765"],["dc.relation.issn","1756-8757"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes."],["dc.title","Developmental Trajectories in the Understanding of Everyday Uncertainty Terms"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Cognitive Science"],["dc.bibliographiccitation.volume","44"],["dc.contributor.author","Stephan, Simon"],["dc.contributor.author","Mayrhofer, Ralf"],["dc.contributor.author","Waldmann, Michael R."],["dc.date.accessioned","2021-04-14T08:25:27Z"],["dc.date.available","2021-04-14T08:25:27Z"],["dc.date.issued","2020"],["dc.description.abstract","Abstract Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.identifier.doi","10.1111/cogs.12871"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81635"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1551-6709"],["dc.relation.issn","0364-0213"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited."],["dc.title","Time and Singular Causation—A Computational Model"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]