Can conditionals explain explanations? A modus ponens model of B because A

Sebben, Simone; Ullrich, Johannes

doi:10.1016/j.cognition.2021.104812

Cited by 6 publications

(6 citation statements)

References 63 publications

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“…For example, research on conditional reasoning has shown that people’s assumptions about the causal structure (Bonnefond et al, 2014; Byrne, 1989; Byrne et al, 1999; Espino & Byrne, 2020) and their normative expectations about the frequency of events (Oaksford & Chater, 1994, 2003) affect what inferences people draw. Precisely what people infer from conditional statements is still very much under investigation (Barrouillet et al, 2008; Collins et al 2020; Khemlani & Johnson-Laird, 2013; Sebben & Ullrich, 2021; Skovgaard-Olsen et al, 2021). Given the tight relationship between conditionals and causality (e.g., Goldvarg & Johnson-Laird, 2001; Over et al, 2007), we suspect that inferences from conditional statements, just like inferences from explanations, may be illuminated by considering what role these statements play in communication (see also Evans, 2005; Johnson-Laird & Byrne, 2002; Sebben & Ullrich, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Precisely what people infer from conditional statements is still very much under investigation (Barrouillet et al, 2008; Collins et al 2020; Khemlani & Johnson-Laird, 2013; Sebben & Ullrich, 2021; Skovgaard-Olsen et al, 2021). Given the tight relationship between conditionals and causality (e.g., Goldvarg & Johnson-Laird, 2001; Over et al, 2007), we suspect that inferences from conditional statements, just like inferences from explanations, may be illuminated by considering what role these statements play in communication (see also Evans, 2005; Johnson-Laird & Byrne, 2002; Sebben & Ullrich, 2021).…”

Section: Discussionmentioning

confidence: 99%

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Inference from explanation.

Kirfel¹,

Icard²

2022

Journal of Experimental Psychology: General

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What do we communicate with causal explanations? Upon being told, "E because C", a person might learn that C and E both occurred, and perhaps that there is a causal relationship between C and E. In fact, causal explanations systematically disclose much more than this basic information. Here, we offer a communication-theoretic account of explanation that makes specific predictions about the kinds of inferences people draw from others' explanations. We test these predictions in a case study involving the role of norms and causal structure. In Experiment 1, we demonstrate that people infer the normality of a cause from an explanation when they know the underlying causal structure. In Experiment 2, we show that people infer the causal structure from an explanation if they know the normality of the cited cause. We find these patterns both for scenarios that manipulate the statistical and prescriptive normality of events. Finally, we consider how the communicative function of explanations, as highlighted in this series of experiments, may help to elucidate the distinctive roles that normality and causal structure play in causal judgment, paving the way toward a more comprehensive account of causal explanation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Inference from explanation.

Kirfel¹,

Icard²

2022

Journal of Experimental Psychology: General

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“…This approach is also empirically supported bySebben and Ullrich (2021), who show that people tend to evaluate conditionals in this way.…”

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confidence: 66%

Towards a theory of abduction based on conditionals

Pfister

2022

Synthese

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Abduction is considered the most powerful, but also the most controversially discussed type of inference. Based on an analysis of Peirce’s retroduction, Lipton’s Inference to the Best Explanation and other theories, a new theory of abduction is proposed. It considers abduction not as intrinsically explanatory but as intrinsically conditional: for a given fact, abduction allows one to infer a fact that implies it. There are three types of abduction: Selective abduction selects an already known conditional whose consequent is the given fact and infers that its antecedent is true. Conditional-creative abduction creates a new conditional in which the given fact is the consequent and a defined fact is the antecedent that implies the given fact. Propositional-conditional-creative abduction assumes that the given fact is implied by a hitherto undefined fact and thus creates a new conditional with a new proposition as antecedent. The execution of abductive inferences is specified by theory-specific patterns. Each pattern consists of a set of rules for both generating and justifying abductive conclusions and covers the complete inference process. Consequently, abductive inferences can be formalised iff the whole pattern can be formalised. The empirical consistency of the proposed theory is demonstrated by a case study of Semmelweis' research on puerperal fever.

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“…The tight link between counterfactuals and causal judgements puts pressure on theories of causal judgement that do not distinguish between counterfactuals and other types of conditionals (e.g. [64]). For example, the mental model theory analyses causation in terms of temporally ordered sets of possibilities [65,66].…”

Section: Discussionmentioning

confidence: 99%

What would have happened? Counterfactuals, hypotheticals and causal judgements

Gerstenberg

2022

Phil. Trans. R. Soc. B

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How do people make causal judgements? In this paper, I show that counterfactual simulations are necessary for explaining causal judgements about events, and that hypotheticals do not suffice. In two experiments, participants viewed video clips of dynamic interactions between billiard balls. In Experiment 1, participants either made hypothetical judgements about whether ball B would go through the gate if ball A were not present in the scene, or counterfactual judgements about whether ball B would have gone through the gate if ball A had not been present. Because the clips featured a block in front of the gate that sometimes moved and sometimes stayed put, hypothetical and counterfactual judgements came apart. A computational model that evaluates hypotheticals and counterfactuals by running noisy physical simulations accurately captured participants’ judgements. In Experiment 2, participants judged whether ball A caused ball B to go through the gate. The results showed a tight fit between counterfactual and causal judgements, whereas hypotheticals did not predict causal judgements. I discuss the implications of this work for theories of causality, and for studying the development of counterfactual thinking in children. This article is part of the theme issue ‘Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny’.

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Can conditionals explain explanations? A modus ponens model of B because A

Cited by 6 publications

References 63 publications

Inference from explanation.

Inference from explanation.

Towards a theory of abduction based on conditionals

What would have happened? Counterfactuals, hypotheticals and causal judgements

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