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  The provenance of modal inference

Khemlani, S., Hinterecker, T., & Johnson-Laird, P. (2017). The provenance of modal inference. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Computational Foundations of Cognition (pp. 663-668). Austin, TX, USA: Cognitive Science Society.

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 Creators:
Khemlani, S, Author
Hinterecker, T1, 2, 3, Author           
Johnson-Laird, PN, Author
Affiliations:
1Project group: Social & Spatial Cognition, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528706              
2Project group: Motion Perception & Simulation, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528705              
3Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

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 Abstract: People reason about possibilities routinely, and reasoners can infer “modal” conclusions, i.e., conclusions that concern what is possible or necessary, from premises that make no mention of modality. For instance, given that Cullen was born in New York or Kentucky, it is intuitive to infer that it’s possible that Cullen was born in New York, and a recent set of studies on modal reasoning bear out these intuitions (Hinterecker, Knauff, Johnson-Laird, 2016). What explains the tendency to make modal inferences? Conventional logic does not apply to modal reasoning, and so logicians invented many alternative systems of modal logic to capture valid modal inferences. But, none of those systems can explain the inference above. We posit a novel theory based on the idea that reasoners build mental models, i.e., iconic simulations of possibilities, when they reason about sentential connectives such as and, if, and or (Johnson-Laird, 2006). The theory posits that reasoners represent a set of conjunctive possibilities to capture the meanings of compound assertions. It is implemented in a new computational process model of sentential reasoning that can draw modal conclusions from non-modal premises. We describe the theory and computational model, and show how its performance matches reasoners’ inferences in two studies by Hinterecker et al. (2016). We conclude by discussing the model-based theory in light of alternative accounts of reasoning.

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 Dates: 2017-07
 Publication Status: Issued
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 Identifiers: BibTex Citekey: KhemlaniHJ2017_2
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Title: 39th Annual Meeting of the Cognitive Science Society (CogSci 2017)
Place of Event: London, UK
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Title: Computational Foundations of Cognition
Source Genre: Proceedings
 Creator(s):
Gunzelmann, G., Editor
Howes, A., Editor
Tenbrink, T., Editor
Davelaar, E., Editor
Affiliations:
-
Publ. Info: Austin, TX, USA : Cognitive Science Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 663 - 668 Identifier: ISBN: 978-0-9911967-6-0