Simplicity and complexity preferences in causal explanation: An opponent heuristic account

Johnson, Samuel G. B.; Valenti, J.J.; Keil, Frank C.

doi:10.1016/j.cogpsych.2019.05.004

Cited by 46 publications

(19 citation statements)

References 44 publications

(54 reference statements)

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“…New information may either introduce uncertainty, or demand increased cognitive effort so it can be accommodated within the belief. This is consistent with people's general preference for simpler, flexible explanations over complex ones (Lombrozo, 2007; see also Johnson et al, 2019). If a particular subset of data induces sufficient anxiety, however, then these costs may be outweighed by the anxiety-reducing benefit of explaining that data away with an overfitted explanation.…”

Section: Implausible Than Plausible Conspiracy Beliefssupporting

confidence: 72%

Of tinfoil hats and thinking caps: Reasoning is more strongly related to implausible than plausible conspiracy beliefs

Hattersley

Brown

Michael³

et al. 2022

Cognition

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Section: Implausible Than Plausible Conspiracy Beliefssupporting

confidence: 72%

Of tinfoil hats and thinking caps: Reasoning is more strongly related to implausible than plausible conspiracy beliefs

Hattersley

Brown

Michael³

et al. 2022

Cognition

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“…Contemporary laboratory-based studies reiterate human preference for simple explanations over complex ones [13,36,[43][44][45]. However, experiments in natural settings have revealed that complexity increases explanatory satisfaction [28,46] and that complexity preferences are aligned with the complexity of the event itself [47][48][49].…”

Section: Explanation Complexitymentioning

confidence: 99%

The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study

Woodcock¹,

Mittelstadt²,

Busbridge³

et al. 2021

J Med Internet Res

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Background Artificial intelligence (AI)–driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Explanations can follow many forms, including why-explanations and how-explanations. Social theories suggest that why-explanations are better at communicating knowledge and cultivating trust among laypeople. Objective The aim of this study is to ascertain whether explanations provided by a symptom checker affect explanatory trust among laypeople and whether this trust is impacted by their existing knowledge of disease. Methods A cross-sectional survey of 750 healthy participants was conducted. The participants were shown a video of a chatbot simulation that resulted in the diagnosis of either a migraine or temporal arteritis, chosen for their differing levels of epidemiological prevalence. These diagnoses were accompanied by one of four types of explanations. Each explanation type was selected either because of its current use in symptom checkers or because it was informed by theories of contrastive explanation. Exploratory factor analysis of participants’ responses followed by comparison-of-means tests were used to evaluate group differences in trust. Results Depending on the treatment group, two or three variables were generated, reflecting the prior knowledge and subsequent mental model that the participants held. When varying explanation type by disease, migraine was found to be nonsignificant (P=.65) and temporal arteritis, marginally significant (P=.09). Varying disease by explanation type resulted in statistical significance for input influence (P=.001), social proof (P=.049), and no explanation (P=.006), with counterfactual explanation (P=.053). The results suggest that trust in explanations is significantly affected by the disease being explained. When laypeople have existing knowledge of a disease, explanations have little impact on trust. Where the need for information is greater, different explanation types engender significantly different levels of trust. These results indicate that to be successful, symptom checkers need to tailor explanations to each user’s specific question and discount the diseases that they may also be aware of. Conclusions System builders developing explanations for symptom-checking apps should consider the recipient’s knowledge of a disease and tailor explanations to each user’s specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap.

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“…The reason for the absence of a Simplicity effect is interpreted as follows. Previous studies have claimed that the effect of Simplicity can only be seen in situations where prior probability or likelihood is indeterminate [effect in situations where prior probability is indeterminate: Lombrozo (2007); effect in situations where likelihood is indeterminate: Johnson et al (2014a), Johnson et al (2019)]. In Experiment 1, we explicitly conveyed the Prior probability to each participant.…”

Section: Findings and Novelties Of This Studymentioning

confidence: 99%

“…However, individual humans can hold distinct beliefs about what kind of explanation is most plausible in each domain (Shipley, 1993;Kemp et al, 2010). For instance, it has been suggested that humans believe that physical events have simpler causations than social events (Strickland et al, 2016) and that social explanations may require complex causal inferences (Johnson et al, 2019). Moreover, explanations in the domain of biology, especially evolution, are evaluated according to different criteria than those in other domains (Liquin and Lombrozo, 2018).…”

Section: Domain Effectmentioning

confidence: 99%

How Does Explanatory Virtue Determine Probability Estimation?—Empirical Discussion on Effect of Instruction

2020

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It is important to reveal how humans evaluate an explanation of the recent development of explainable artificial intelligence. So, what makes people feel that one explanation is more likely than another? In the present study, we examine how explanatory virtues affect the process of estimating subjective posterior probability. Through systematically manipulating two virtues, Simplicity—the number of causes used to explain effects—and Scope—the number of effects predicted by causes—in three different conditions, we clarified two points in Experiment 1: (i) that Scope's effect is greater than Simplicity's; and (ii) that these virtues affect the outcome independently. In Experiment 2, we found that instruction about the explanatory structure increased the impact of both virtues' effects but especially that of Simplicity. These results suggest that Scope predominantly affects the estimation of subjective posterior probability, but that, if perspective on the explanatory structure is provided, Simplicity can also affect probability estimation.

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Simplicity and complexity preferences in causal explanation: An opponent heuristic account

Cited by 46 publications

References 44 publications

Of tinfoil hats and thinking caps: Reasoning is more strongly related to implausible than plausible conspiracy beliefs

Of tinfoil hats and thinking caps: Reasoning is more strongly related to implausible than plausible conspiracy beliefs

The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study

How Does Explanatory Virtue Determine Probability Estimation?—Empirical Discussion on Effect of Instruction

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