The effect of temporal information among events on Bayesian causal inference in rats
A temporal relationship between events of potential cause and effect is critical to generate a causal relationship because the cause has to be followed by the effect. The present study investigated the role of temporal relationships between events in causal inference in rats via Pavlovian pairings....
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| Veröffentlicht in: | Frontiers in psychology Jg. 5; S. 1142 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Switzerland
Frontiers Media S.A
08.10.2014
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| Schlagworte: | |
| ISSN: | 1664-1078, 1664-1078 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | A temporal relationship between events of potential cause and effect is critical to generate a causal relationship because the cause has to be followed by the effect. The present study investigated the role of temporal relationships between events in causal inference in rats via Pavlovian pairings. In Experiment 1A, subjects in Group Successive received training trials whereby Event 1 (tone or light) was followed by Events 2 (light or tone) and 3 (sucrose solution), whereas those in Group Simultaneous received simultaneous pairings of Events 1 and 2, and Events 1 and 3. During testing, a lever was inserted into the experimental chamber, where subjects were allowed to press the lever which produced the occurrence of Event 2 without reward. By measuring nose-poke responses during the presentation of Event 2, assumingly based on the prediction of occurrence of sucrose solution, subjects in Group Successive showed a relatively lower response rate than did those in Group Simultaneous. In Experiment 1B, this difference was not observed if subjects received the presentations of Event 2 which was irrelevant to their lever pressing during testing. These results suggest that rats can differentiate their response based on the elemental temporal information even when the integrated temporal map was the same, and implied that rats use temporal information as well as conditional probability based on causal Bayesian network account. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Kenneth Leising, Texas Christian University, USA; W. David Stahlman, University of California, Los Angeles, USA Edited by: Mimi Liljeholm, University of California, Irvine, USA This article was submitted to Cognition, a section of the journal Frontiers in Psychology. |
| ISSN: | 1664-1078 1664-1078 |
| DOI: | 10.3389/fpsyg.2014.01142 |