The structure and function of explanations

doi:10.1016/j.tics.2006.08.004

Trends in Cognitive Sciences

Volume 10, Issue 10, October 2006, Pages 464-470

https://doi.org/10.1016/j.tics.2006.08.004 Get rights and content

Generating and evaluating explanations is spontaneous, ubiquitous and fundamental to our sense of understanding. Recent evidence suggests that in the course of an individual's reasoning, engaging in explanation can have profound effects on the probability assigned to causal claims, on how properties are generalized and on learning. These effects follow from two properties of the structure of explanations: explanations accommodate novel information in the context of prior beliefs, and do so in a way that fosters generalization. The study of explanation thus promises to shed light on core cognitive issues, such as learning, induction and conceptual representation. Moreover, the influence of explanation on learning and inference presents a challenge to theories that neglect the roles of prior knowledge and explanation-based reasoning.

Section snippets

Explanation and cognition

Children, adults and scientists alike confront the world with a common question: why? We wonder why events unfold in particular ways, why objects have specific properties and why people behave as they do. Explanations are more than a human preoccupation – they are central to our sense of understanding, and the currency in which we exchange beliefs. Accordingly, social psychology and philosophy have subfields dedicated to the study of explanation, with social psychology focusing on explanations

The structure of explanations

Although the content of explanations can vary wildly, their structure is more constrained. Theories of explanation from the philosophy of science have generally imposed logical or causal constraints on what constitutes an explanation [4]. For example, one early but influential account characterized explanations as arguments demonstrating how what is being explained (‘the explanandum’) follows deductively from natural laws and empirical conditions [23]. Several contemporary accounts suggest that

The function of explanations

Explanations are accompanied by a sense of understanding 32, 33 and satisfaction [16] but the drive to explain might serve a less proximal function. Social psychologists and philosophers have argued that, in revealing the past, explanations help to predict and control the future 1, 6, 16, 34. The work reviewed below suggests that explanations often support the broader function of guiding reasoning. In particular, engaging in explanation serves as a mechanism through which beliefs are brought to

Explanation versus causal reasoning

Although explanations clearly have a role in inference and learning, one might legitimately wonder whether explanations exert an influence by virtue of something about explanations, or simply by virtue of the causal knowledge they happen to express. There are three reasons to attribute the effects of explanation to explanation per se. First, the structure of explanations is such that some beliefs are privileged at the expense of others. I have suggested that in summoning general patterns and

Concluding remarks

Explanations mediate a great deal of everyday reasoning. In particular, the generation and evaluation of explanations can constrain inferences by appropriately summoning prior beliefs. In evaluating claims, the existence of explanations can constitute evidence, and serve as a basis for eliminating possibilities to assess probability. In generalizing from facts or examples, explanations subsume provided information under a general pattern, thereby highlighting the senses of similarity that

Acknowledgements

The author wishes to acknowledge Elizabeth Allen, Susan Carey, Tom Griffiths, Steven Sloman and Michael Weisberg for helpful comments on earlier drafts.

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Cognitive scientists suggest that inviting people to explain contentious political issues might reduce intergroup toxicity because it exposes people to how poorly they understand the issue. However, whether providing explanations can result in more open-minded political thinking remains unclear. On one hand, inviting people to explain a political issue might make them more impartial and open-minded in their thinking. On the other hand, an invitation to explain a contentious political issue might lead to myside bias—rationalization of one's default position. Here, we address these contrasting predictions in five experiments (N = 1884; three pre-registered), conducted across a variety of contexts: with graduate students interacting with an actor in a laboratory setting, with US residents at the peak of the 2012 and 2016 U.S. presidential elections, with UK residents before the highly polarized 2019 Brexit vote, and with gun-control partisans. Across studies, we found that explaining politically contentious topics resulted in more open-minded thinking, an effect that generalized across coded (Studies 1–4) and self-report (Studies 3–4) measures. We also observed that participants who were made to feel like their explanations were welcomed felt closer to their discussion partner (Studies 3–4), an effect that generalized to all outgroup members with whom they disagreed with about the politically contentious issue (Study 4). We discuss the theoretical implications of these findings, and the potential for explanations to foster open-minded political engagement.
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An invisible-hand explanation explains a situation as the outcome of individual actions, without individuals intending the situation. Invisible-hand explanations have been used for decades to account for all kinds of phenomena, from segregation to traffic norms. But, they have not been studied cognitively and empirically as an intuitive explanation type. We propose and show that US-based adults intuitively prefer invisible-hand over intentional-design explanations. We first validate pairs of explanations as equally likely to cause a social phenomenon (Exp 1). We then show that given that social phenomenon, participants prefer an invisible-hand to an intentional-design explanation, and that the preference for invisible-hand explanations is also negatively linked to conspiratorial beliefs. (Exp 2). We find that when participants are asked to come up with explanations themselves, they are equally fast to come up with invisible-hand or intentional-design explanations (Exp 3), but that both they and other participants prefer the participant-generated invisible hand explanations (Exp 3 and 4). We conclude that US-based adults likely have a prior preference for invisible-hand explanations for social phenomena.
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Humans are capable explainers and lay people tend to share the same explanatory virtues held in high regard by philosophers and scientists. However, a recent line of studies found a striking deviation from normativity in lay people’s explanations, termed the “narrow latent scope bias”. When competing explanations with identical a priori probabilities fit observed evidence equally well – but differ in the number of unobserved pieces of evidence they predict (latent scope) – reasoners seem to prefer explanations that predict fewer unobserved pieces of evidence (narrow latent scope). This tendency has been described as a robust explanatory reasoning bias. The present paper empirically demonstrates across six experiments ( $N = 2200$ ) that this bias is less robust than has been claimed, and influenced by nuanced pragmatic inferences on the side of participants. Pragmatic factors shown to influence the bias are assumptions about how easily an unobserved piece of evidence should have been observed if it was present (“feature diagnosability”), and the formulation of the test question being asked. Across studies, genuine narrow latent scope biases resulting from fallacious reasoning were found only in a fraction of participants. It is also demonstrated that the magnitude of the bias depends on response options: it is stronger if participants are forced to commit an error, but at best weak if they are allowed to give the correct answer.
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Service robot verbalization in service processes with moral implications and its impact on satisfaction
2023, Technological Forecasting and Social Change
Service robots are expected to become increasingly common. As their capabilities become more advanced, it is also expected that they would be involved in tasks for which a human user would want to know why they do what they are doing. One way to accomplish this is to program robots so that they verbalize (i.e., they are thinking aloud) while they are providing service. This ability is likely to be particularly useful for tasks that involve behavioral norms. The present study used an experimental design to manipulate the level of a robot's ability to verbalize motivations for its behavior (low vs. high) while it was asked by a human to carry out a task with moral implications. The results show that robot verbalizing contributed positively to satisfaction with the robot's performance, and that this impact was mediated by understandability, perceived morality and intellectual stimulation.

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Trends in Cognitive Sciences

ReviewThe structure and function of explanations

Section snippets

Explanation and cognition

The structure of explanations

The function of explanations

Explanation versus causal reasoning

Concluding remarks

Acknowledgements

Trends Cogn. Sci.

J. Mem. Lang.

Trends Cogn. Sci.

Cogn. Sci.

Cogn. Dev.

Cogn. Sci.

Cognition

J. Econ. Behav. Organ.

Cognition

Cogn. Sci.

Cognition

Cogn. Sci.

Cogn. Sci.

Learning & Instruction

Cogn. Sci.

Cogn. Sci.

Contemp. Educ. Psychol.

Cognition

Trends Cogn. Sci.

Cognition

The Psychology of Interpersonal Relations

Ordinary personology

How the Mind Explains Behavior: Folk Explanations, Meaning, and Social Interaction

Four Decades of Scientific Explanation

Explanation and Cognition

Explanation and understanding

Annu. Rev. Psychol.

Conceptual Change in Childhood

The role of theories in conceptual coherence

Psychol. Rev.

The Big Book of Concepts

The locus of knowledge effects in concept learning

J. Exp. Psychol. Learn. Mem. Cogn.

Effect of theory-based feature correlations on typicality judgments

Mem. Cognit.

Theorizing is important, and collateral information constrains how well it is done

Explanation as orgasm and the drive for causal knowledge: the function, evolution, and phenomenology of the theory-formation system

Scientific explanation and the sense of understanding

Philos. Sci.

Conversational processes and causal explanation

Psychol. Bull.

Review
The structure and function of explanations