Causal inference shapes counterfactual plausibility

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

When we reason about what could have been, some possibilities seem plausible, and others far-fetched. According to a recent theory, counterfactual possibilities are plausible if they can be generated by making local, probabilistic adjustments to the causes of what actually happened. We provide evidence that people think about counterfactuals in this way even when they have to infer the causes of what happened. We told participants about the diet of a fictional animal, and then asked them simple counterfactual questions. For example, given that the animal has eaten 1 berry today, how much food could it plausibly have eaten instead? When the amount of food eaten by the animal licensed an inference about a causally upstream variable, participants inferred the state of this variable and used it to guide their counterfactual plausibility judgments. More generally, the distribution over counterfactual values derived from participants' judgments was remarkably similar to the distribution predicted by the model.
Original languageEnglish
Title of host publicationProceedings of the 45th Annual Meeting of the Cognitive Science Society
EditorsMicah Goldwater, Florencia Anggoro, Brett Hayes, Desmond Ong
PublisherThe Cognitive Science Society
Pages80-86
Number of pages7
Volume45
Publication statusPublished - 29 Jul 2023
Event45th Annual Meeting of the Cognitive Science Society: Cognition in Context - Sydney, Australia
Duration: 26 Jul 202329 Jul 2023
https://cognitivesciencesociety.org/cogsci-2023/

Publication series

NameProceedings of the Cognitive Science Society
PublisherThe Cognitive Science Society
ISSN (Electronic)1069-7977

Conference

Conference45th Annual Meeting of the Cognitive Science Society
Abbreviated titleCogSci2023
Country/TerritoryAustralia
CitySydney
Period26/07/2329/07/23
Internet address

Keywords / Materials (for Non-textual outputs)

  • causality
  • counterfactuals
  • computational modeling

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