Inference from explanation

Kirfel, Lara; Icard, Thomas

doi:10.31234/osf.io/x5mqc

Cited by 11 publications

(18 citation statements)

References 91 publications

(138 reference statements)

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“…As a result, more counterfactual alternatives come to mind in which the outcome would have been absent as a result of a change in the abnormal agent's behaviour, emphasizing the agent's perceived causal strength. Alternative theories have been suggested with reference to pragmatics (Grice, 1989;Hilton & Jaspars, 1987;Kirfel, Icard, & Gerstenberg, 2020), or co-variation (Cheng & Novick, 1991;Harinen, 2017). Prescriptive norms have been claimed to influence causal judgments via normative judgments such as blame or responsibility Sytsma et al, 2012), or by shifting the understanding of the causal question to the domain of accountability .…”

Section: Abnormal Causesmentioning

confidence: 99%

“…Despite the often crucial role of epistemic states in attributions to and inferences from abnormal behaviour, they are rarely controlled for. Studies on the effects of normality on causal judgments have predominantly used descriptive vignettes with human causal agents (but see Gerstenberg & Icard, 2019;Kirfel et al, 2020;Kirfel & Lagnado, 2019). The short verbal description of these causal scenarios often lack in-depth information about what the causal agents think, believe or know.…”

Section: To Know or Not To Know: The Role Of Mental Statesmentioning

confidence: 99%

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Causal judgments about atypical actions are influenced by agents' epistemic states

Kirfel

Lagnado

2021

Cognition

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A prominent finding in causal cognition research is people's tendency to attribute increased causality to atypical actions. If two agents jointly cause an outcome ("conjunctive causation"), but differ in how frequently they have performed the causal action before, people judge the atypically acting agent to have caused the outcome to a greater extent than the normally acting agent. In this paper, we argue that it is the epistemic state of an abnormally acting agent, rather than the abnormality of their action, that is driving people's causal judgments. Given the predictability of the normally acting agent's behaviour, the abnormal agent is in a better position to foresee the consequences of their action. We put this hypothesis to test in four experiments. In Experiment 1, we show that people judge the atypical agent as more causal than the normally acting agent, but also perceive an epistemic advantage of the abnormal agent. In Experiment 2, we find that people do not judge a causal difference if there is no epistemic asymmetry between the agents. In Experiment 3, we replicate these findings for a scenario in which the abnormal agent's epistemic advantage generalises to a novel context. In Experiment 4, we extend these findings to mental states more broadly construed. We develop a Bayesian network model that predicts the degree of mental states based on action normality and epistemic states, and find that people infer mental states like desire and intention to a greater extent from abnormal behaviour. We discuss these results in light of current theories and research on people's preference for atypical causes.

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Section: Abnormal Causesmentioning

confidence: 99%

Section: To Know or Not To Know: The Role Of Mental Statesmentioning

confidence: 99%

Causal judgments about atypical actions are influenced by agents' epistemic states

Kirfel

Lagnado

2021

Cognition

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Section: Abnormal Causesmentioning

confidence: 99%

Section: To Know or Not To Know: The Role Of Mental Statesmentioning

confidence: 99%

Causal judgments about atypical actions are influenced by agents’ epistemic states

Kirfel¹,

Lagnado²

2020

Preprint

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A prominent finding in causal cognition research is people's tendency to attribute increased causality to atypical actions. If two agents jointly cause an outcome ("conjunctive causation’"), but differ in how frequently they have performed the causal action before, people judge the atypically acting agent to have caused the outcome to a greater extent than the normally acting agent. In this paper, we argue that it is the epistemic state of an abnormally acting agent, rather than the abnormality of their action, that is driving people's causal judgments. Given the predictability of the normally acting agent's behaviour, the abnormal agent is in a better position to foresee the consequences of their action. We put this hypothesis to test in four experiments. In Experiment 1, we show that people judge the atypical agent as more causal than the normally acting agent, but also perceive an epistemic advantage of the abnormal agent. In Experiment 2, we find that people do not judge a causal difference if there is no epistemic asymmetry between the agents. In Experiment 3, we replicate these findings for a scenario in which the abnormal agent's epistemic advantage generalises to a novel context. In Experiment 4, we extend these findings to mental states more broadly construed. We develop a Bayesian Network model that predicts the degree of mental states based on action normality and epistemic states, and find that people infer mental states like desire and intentions to a greater extent from abnormal behaviour. We discuss these results in light of current theories and research on people’s preference for atypical causes.

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“…A remarkable aspect of human intelligence is the ability to draw sophisticated inferences that go beyond what can be perceived directly. For example, from observing the current state of the physical world, people can infer what must have happened in the past (Smith & Vul, 2014;Gerstenberg, Siegel, & Tenenbaum, 2018;Kirfel et al, 2020). These inferences about the past include physical events (e.g.…”

Section: Introductionmentioning

confidence: 99%

Who went fishing? Inferences from social evaluations

Davis¹,

Allen²

2021

Preprint

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Humans have a remarkable ability to go beyond the observable. From seeing the current state of our shared kitchen, we can infer what happened and who did it. Prior work has shown how the physical state of the world licenses inferences about the causal history of events, and the agents that participated in these events. Here, we investigate a previously unstudied source of evidence about what happened: social evaluations. In our experiment, we present situations in which a group failed to optimally coordinate their actions. Participants learn how much each agent was blamed for the outcome, and their task is to make inferences about the situation, the agents' actions, as well as the agents' capabilities. We develop a computational model that accurately captures participants' inferences. The model assumes that people blame others by considering what they should have done, and what causal role their action played. By inverting this generative model of blame, people can figure out what happened.

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Inference from explanation

Cited by 11 publications

References 91 publications

Causal judgments about atypical actions are influenced by agents' epistemic states

Causal judgments about atypical actions are influenced by agents' epistemic states

Causal judgments about atypical actions are influenced by agents’ epistemic states

Who went fishing? Inferences from social evaluations

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