Serveur d'exploration sur les dispositifs haptiques

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Causal reasoning with forces

Identifieur interne : 000A65 ( Main/Exploration ); précédent : 000A64; suivant : 000A66

Causal reasoning with forces

Auteurs : Phillip Wolff [États-Unis] ; Aron K. Barbey [États-Unis]

Source :

RBID : PMC:4301188

Abstract

Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed.


Url:
DOI: 10.3389/fnhum.2015.00001
PubMed: 25653611
PubMed Central: 4301188


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<p>Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the
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, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al.,
<xref rid="B39" ref-type="bibr">2014</xref>
) and the causal model theory (Sloman et al.,
<xref rid="B60" ref-type="bibr">2009</xref>
), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed.</p>
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<name sortKey="Shepard, J" uniqKey="Shepard J">J. Shepard</name>
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<author>
<name sortKey="Wolff, P" uniqKey="Wolff P">P. Wolff</name>
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<author>
<name sortKey="Zettergren, M" uniqKey="Zettergren M">M. Zettergren</name>
</author>
</analytic>
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<biblStruct>
<analytic>
<author>
<name sortKey="Woodward, J" uniqKey="Woodward J">J. Woodward</name>
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<biblStruct>
<analytic>
<author>
<name sortKey="Woodward, J" uniqKey="Woodward J">J. Woodward</name>
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<li>États-Unis</li>
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<name sortKey="Wolff, Phillip" sort="Wolff, Phillip" uniqKey="Wolff P" first="Phillip" last="Wolff">Phillip Wolff</name>
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<name sortKey="Barbey, Aron K" sort="Barbey, Aron K" uniqKey="Barbey A" first="Aron K." last="Barbey">Aron K. Barbey</name>
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