To approach the many challenges involved in the notion of engineering concepts, it is important to have a clear idea of the starting point – the concepts that people use in their everyday lives, in conversations and in expressing beliefs, desires, intentions and so forth. The first Section of this chapter introduces evidence that I have accumulated over the last many years concerning the flexibility, context-dependence, and vagueness of such common concepts. The concept engineer needs to understand the structure of (...) the “raw material” with which she is working. In Section 2, I summarise some results concerning the amount of individual variation that exists within a single relatively homogenous population of individuals, both in what examples are considered most typical of a category, and in what properties are considered most central to a category (Hampton & Passanisi, 2016). The demonstration of a significant degree of reliable variation in responses to these questions, that is stable over time, illustrates the potential for conceptual engineering interventions to affect individuals’ concepts. Concepts are rarely fixed or uniformly understood in the same way. I then discuss the different ways in which concepts track the world, and I speculate about how different conceptual domains are governed by different criteria of validity or acceptability. This reflection will lead to Section 3 in which I summarise some results from recent research (Thorne, Quilty-Dunn, Smortchova, Shea, & Hampton, 2021), investigating for the first time how people view the value of different familiar concepts. Knowing how people appraise concepts will be a key to developing ideas for improving the reliability and validity of the concepts that they commonly use. A final section contains some (largely speculative) discussion about the implications of work in psychology of concepts for the project of conceptual engineering. (shrink)

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to bemoresensitive to mechanism andlesssensitive to covariation – (...) even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition. (shrink)

In this dissertation, I argue that understanding possesses unique epistemic value. I propose and defend a novel account of understanding that I call the management account of understanding, which is the view that an agent A understands a subject matter S just in case A has the ability to extract the relevant information and exploit it with the relevant cognitive capacities to answer questions in S. Since inquiry is the process of raising and answering questions, I argue that without understanding, (...) it would be impossible to engage in successful inquiry. I argue that understanding is indispensable for effective cognition and that it is irreducible to other epistemic categories, such as knowledge, justification, or rationality. Understanding is an irreducible component of epistemic excellence. In arguing for this account, I focus on the nature and requirements of cognition and pay less attention to intuitions about hypothetical cases, in contrast to previous approaches to understanding. I draw upon psychology and cognitive science, especially work on the frame problem, to demonstrate the importance (and difficulty) of finding relevant information and exploiting it in relevant ways to answer questions. One’s cognitive system must be organized in such a way that one has the ability to think the relevant thoughts in the relevant ways to answer the relevant questions. This reveals well-defined directions in which future research on understanding should move. In addition to proposing and defending the management account of understanding, I explore several features of understanding, including some interesting consequences of the management account. I argue that the management account and commonly used methods of measuring degrees of understanding imply that understanding can be based on false beliefs. I argue that the management account (and any account of understanding that implies understanding is an ability) implies that understanding can be acquired by luck. I also argue that understanding is ontologically prior to explanation: a good explanation is essentially an act that produces understanding in the relevant cognitive background. (shrink)

Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: How does the explanatory power of a hypothesis cohere with other cognitive factors? How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: (...) Explanatory Value, Rational Acceptability, and Entailment. Explanatory judgments strongly cohered with judgments of causal relevance and with a sense of understanding. Furthermore, we found that Explanatory Value was sensitive to manipulations of statistical relevance relations between hypothesis and evidence, but not to explicit information about the prior probability of the hypothesis. These results indicate that probabilistic information about statistical relevance is a strong determinant of Explanatory Value. More generally, our study suggests that abductive and probabilistic reasoning are two distinct modes of inference. (shrink)

In metaethics, evolutionary debunking arguments combine empirical and epistemological premises to purportedly show that our moral judgments are unjustified. One objection to these arguments has been to distinguish between those judgments that evolutionary influence might undermine versus those that it does not. This response is powerful but not well understood. In this paper I flesh out the response by drawing upon a familiar distinction in the natural sciences, where it is common to distinguish folk judgments from theoretical judgments. I argue (...) that this in turn illuminates the proper scope of the evolutionary debunking argument, but not in an obvious way: it is a very specific type of undermining argument that targets those theories where theoretical judgments are inferred merely from folk judgments. One upshot of this conclusion is that it reveals a verboten methodology in metaethics. The evolutionary debunking argument is therefore much less powerful than its proponents have supposed, but it nevertheless rules out what is perhaps a common way of attempting to justify moral judgments. (shrink)

Political protests, debates on college campuses, and social media tirades make it seem like everyone is speaking their minds today. Surveys, however, reveal that many people increasingly feel like they're walking on eggshells when communicating in public. Speaking your mind can risk relationships and professional opportunities. It can alienate friends and anger colleagues. Isn't it smarter to just put your head down and keep quiet about controversial topics? In this book, Hrishikesh Joshi offers a novel defense of speaking your mind. (...) He explains that because we are social creatures, we never truly think alone. What we know depends on what our community knows. And by bringing our unique perspectives to bear upon public discourse, we enhance our collective ability to reach the truth on a variety of important matters. Speaking your mind is also important for your own sake. It is essential for developing your own thinking. And it's a core aspect of being intellectually courageous and independent. Joshi argues that such independence is a crucial part of a well-lived life. The book draws from Aristotle, John Stuart Mill, Friedrich Nietzsche, Bertrand Russell, and a range of contemporary thinkers to argue that it's OK to speak your mind. Key Features Shows that we have not just a right but a moral duty to publicly share what we know. Argues that discussing your unique ideas with others is essential for developing as a critical thinker. Explores the value of intellectual honesty and independence in the writings of John Stuart Mill and Friedrich Nietzsche and connects their thinking to contemporary problems. Argues that avoiding cultural blind spots today is important for the fate of future generations. (shrink)

Abstract The typical explanation of an event or process which attracts the label ‘conspiracy theory’ is an explanation that conflicts with the account advanced by the relevant epistemic authorities. I argue that both for the layperson and for the intellectual, it is almost never rational to accept such a conspiracy theory. Knowledge is not merely shallowly social, in the manner recognized by social epistemology, it is also constitutively social: many kinds of knowledge only become accessible thanks to the agent's embedding (...) in an environment that includes other epistemic agents. Moreover, advances in knowledge typically require ongoing immersion in this social environment. But the intellectual who embraces a conspiracy theory risks cutting herself off from this environment, and therefore epistemically disabling herself. Embracing a conspiracy theory therefore places at risk the ability to engage in genuine enquiry, including the enquiry needed properly to evaluate the conspiracy theory. (shrink)

The Architecture of the Mind is itself built on foundations that deserve probing. In this brief commentary I focus on these foundations—Carruthers’ conception of modularity, his arguments for thinking that the mind is massively modular in structure, and his view of human cognitive architecture.

The bullshit receptivity scale—a methodological tool that measures the level of profoundness that participants assign to a series of obscure and new-agey, randomly generated statements—has become increasingly popular since its introduction in 2015. Researchers that deploy this scale often frame their research in terms of Harry Frankfurt’s analysis of bullshit, according to which bullshit is discourse produced without regard for the truth. I argue that framing these studies in Frankfurtian terms is detrimental and has led to some misguided theorizing about (...) the mental processes implicated in bullshit receptivity. I argue that we should, instead, frame these studies in terms of what G.A. Cohen calls unclarifiable bullshit. After reviewing some recent theorizing about the psychology of bullshit receptivity, I show how reframing the bullshit receptivity scale in terms of unclarifiable bullshit generates plausible conjectures about the psychological factors responsible for bullshit receptivity. I draw from the literature on the illusion of explanatory depth and the misplaced meaning effect to develop a novel account of bullshit detection and receptivity. I consider some empirical support for my account and draw attention to ways that it can also be extended to account for results from studies that don’t use the bullshit receptivity scale. (shrink)

In everyday life, people typically observe fragments of causal networks. From this knowledge, people infer how novel combinations of causes they may never have observed together might behave. I report on 4 experiments that address the question of how people intuitively integrate multiple causes to predict a continuously varying effect. Most theories of causal induction in psychology and statistics assume a bias toward linearity and additivity. In contrast, these experiments show that people are sensitive to cues biasing various integration rules. (...) Causes that refer to intensive quantities (e.g., taste) or to preferences (e.g., liking) bias people toward averaging the causal influences, whereas extensive quantities (e.g., strength of a drug) lead to a tendency to add. However, the knowledge underlying these processes is fallible and unstable. Therefore, people are easily influenced by additional task‐related context factors. These additional factors include the way data are presented, the difficulty of the inference task, and transfer from previous tasks. The results of the experiments provide evidence for causal model and related theories, which postulate that domain‐general representations of causal knowledge are influenced by abstract domain knowledge, data‐driven task factors, and processing difficulty. (shrink)

The demarcation of pseudoscience has been one of the most important philosophical tasks since the 1960s. During the 1980s, an atmosphere of defeatism started to spread among philosophers of science, some of them claimed the failure of the demarcation project. I defend that the more auspicious approach to the problem might be through the intellectual character of epistemic agents, i.e., from the point of view of vice epistemology. Unfortunately, common lists of undesirable character features are usually based on a priori (...) reasoning, and therefore might be considered artificial or too vague. When we base our position on contemporary behavioural sciences, we can see that the epistemic character of believers in pseudoscience is for the most part determined by two related factors. Firstly, these epistemic agents show a higher level of cognitive laziness. By this I mean an inability or unwillingness to engage in reflective thinking and a reluctance to account for counterevidence. Secondly, they yield more easily to metacognitive overconfidence. This can be broadly understood as so-called “knowledge illusion”, the inability to recognize one’s own intellectual limits. The deficiency usually stems from a misunderstanding of the division of cognitive labour and of the agent’s role in epistemic society. I find the proposed epistemological approach to pseudoscience crucial. Only if we understand the descriptive aspects of the problem, can we think of normative solutions to it. (shrink)

Philosophers agree that scientific explanations aim to produce understanding, and that good ones succeed in this aim. But few seriously consider what understanding is, or what the cues are when we have it. If it is a psychological state or process, describing its specific nature is the job of psychological theorizing. This article examines the role of understanding in scientific explanation. It warns that the seductive, phenomenological sense of understanding is often, but mistakenly, viewed as a cue of genuine understanding. (...) The article closes with a discussion of several new paths of research that tie the psychology of scientific explanation to cognate notions of learning, testimony, and understanding. (shrink)

Behavioral economists have proposed that people are subject to an IKEA effect, whereby they attach greater value to products they make for themselves, like IKEA furniture, than to otherwise indiscernible goods. Recently, cognitive psychologist Tom Stafford has suggested there may be an epistemic analog to this, a kind of epistemic IKEA effect. In this paper, I use Stafford’s suggestion to defend a certain thesis about epistemic value. Specifically, I argue that there is a distinctive epistemic value in being an active (...) producer of epistemic goods, like true belief, as opposed to just a passive recipient of such goods, and that because of this it can be rationally permissible to sacrifice truth in a certain way for the sake of this other value. In particular, it is rationally permissible for an epistemic agent to prefer a belief set that contains fewer overall truths but more truths obtained through the agent’s own intellectual labor, in something like the way that a practical agent might prefer furniture they have made through their own manual labor to inherently superior furniture made by someone else. In making my case, I draw on Ernest Sosa’s discussion of causation and praxical epistemic values, and Jennifer Lackey’s testimony-based criticism of the credit view of knowledge. After defending my thesis about epistemic value, I further clarify it by connecting it to the focus of Stafford’s discussion, conspiracy theorists. (shrink)

Behavioral economists have proposed that people are subject to an IKEA effect, whereby they attach greater value to products they make for themselves, like IKEA furniture, than to otherwise indiscernible goods. Recently, cognitive psychologist Tom Stafford has suggested there may be an epistemic analog to this, a kind of epistemic IKEA effect. In this paper, I use Stafford’s suggestion to defend a certain thesis about epistemic value. Specifically, I argue that there is a distinctive epistemic value in being an active (...) producer of epistemic goods, like true belief, as opposed to just a passive recipient of such goods, and that because of this it can be rationally permissible to sacrifice truth in a certain way for the sake of this other value. In particular, it is rationally permissible for an epistemic agent to prefer a belief set that contains fewer overall truths but more truths obtained through the agent’s own intellectual labor, in something like the way that a practical agent might prefer furniture they have made through their own manual labor to inherently superior furniture made by someone else. In making my case, I draw on Ernest Sosa’s discussion of causation and praxical epistemic values, and Jennifer Lackey’s testimony-based criticism of the credit view of knowledge. After defending my thesis about epistemic value, I further clarify it by connecting it to the focus of Stafford’s discussion, conspiracy theorists. (shrink)

This paper reports the first empirical investigation of the hypothesis that epistemic appraisals form part of the structure of concepts. To date, studies of concepts have focused on the way concepts encode properties of objects and the way those features are used in categorization and in other cognitive tasks. Philosophical considerations show the importance of also considering how a thinker assesses the epistemic value of beliefs and other cognitive resources and, in particular, concepts. We demonstrate that there are multiple, reliably (...) judged, dimensions of epistemic appraisal of concepts. Four of these dimensions are accounted for by a common underlying factor capturing how well people believe they understand a concept. Further studies show how dimensions of concept appraisal relate to other aspects of concepts. First, they relate directly to the hierarchical organization of concepts, reflecting the increase in specificity from superordinate to basic and subordinate levels. Second, they predict inductive choices in category-based induction. Our results suggest that epistemic appraisals of concepts form a psychologically important yet previously overlooked aspect of the structure of concepts. These findings will be important in understanding why individuals sometimes abandon and replace certain concepts; why social groups do so, for example, during a “scientific revolution”; and how we can facilitate such changes when we engage in deliberate “conceptual engineering” for epistemic, social, and political purposes. (shrink)

ABSTRACT The illusion of explanatory depth, which has been identified by cognitive psychologists, may play a prominent role in encouraging regulatory action. This special type of overconfidence would logically lead regulators to believe that they are aware of the relevant causes and consequences of the activities they might regulate, and of the unintended side effects of the regulatory actions they are contemplating. So, as with other cognitive biases, the illusion of explanatory depth is likely to lead to mistakes. And unlike (...) the biases that have been the focus of so much behavioral economics, the illusion of explanatory depth is uniquely resistant to correction by those who are aware of it as a general problem and rigorously attempt to keep it under control. (shrink)

Definitions may seem marginal to the sociological enterprise but can be very useful; however, they can also lead to serious errors. Examples of both are given in this article. Different types of definitions are presented, and their relevance for sociology is highlighted. A stipulative definition, for example, is very useful in sociology, as opposed to lexical and ostensive definitions. The definition of a concept that is used in a sociological analysis has to be sociological in nature, and the concept cannot (...) have the same meaning as it has in everyday language. Additional items to bear in mind when a definition is put together are discussed, including some tips on how to proceed that come from political science (Sartori, Collier, Goertz). A definition can also be very helpful in the research process itself by indicating which are the important aspects of some phenomenon to focus on (Weber, Blumer). On the negative side, a term that is not properly defined may lead to misunderstandings as well as to a false sense of agreement with other uses of the term. (shrink)

This article focuses on an area of study that may be called folk economics and that is currently not on the social science agenda. Folk economics has as its task to analyze and explain how people view the economy and how it works; what categories they use in doing so; and what effect this has on the economy and society. Existing studies in economics and sociology that are relevant to this type of study are presented and discussed. A theoretical framework (...) for analyzing folk economic issues is suggested, centered on the distinction between episteme and doxa or between scientific knowledge, on the one hand, and everyday knowledge, on the other. This is then applied to an exploratory case study of the role that folk economics played in Trump’s presidential campaign. It is shown that Trump and his voters thought in a parallel way on key economic issues, especially protectionism. (shrink)

The orthodox view of social cognition maintains that mentalizing is an important and pervasive element of our ordinary social interactions. The orthodoxy has come under scrutiny from various sources recently. Critics from the phenomenological tradition argue that phenomenological reflection on our social interactions tells against the orthodox view. Proponents of pluralistic folk psychology argue that our ordinary social interactions extend far beyond mentalizing. Both sorts of critics argue that emphasis in social cognition research ought to be on other elements of (...) our social practices. In this paper, I consider social explanations specifically and argue that social explanations are implicated in many of the social practices highlighted by critics of the orthodox view. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 31-44, January 2022.

Scientific cognition is a hard-won achievement, both from a historical point of view and a developmental point of view. Here, I review seven facets of lay cognition that run counter to, and often impede, scientific cognition: incompatible folk theories, missing ontologies, tolerance for shallow explanations, tolerance for contradictory explanations, privileging explanation over empirical data, privileging testimony over empirical data, and misconceiving the nature of science itself. Most of these facets have been investigated independent of the others, and I propose directions (...) for future research that might integrate them so as to explore potential commonalities among seemingly disparate obstacles to science learning, as well as potential strategies for bridging lay cognition and scientific cognition in the context of science education. (shrink)

In this article we focus on the concept of concept in conceptual change. We argue that (1) theories of higher learning must often employ two different notions of concept that should not be conflated: psychological and scientific concepts. The usages for these two notions are partly distinct and thus straightforward identification between them is unwarranted. Hence, the strong analogy between scientific theory change and individual learning should be approached with caution. In addition, we argue that (2) research in psychology and (...) cognitive science provides a promising theoretical basis for developing explanatory mechanistic models of conceptual change. Moreover, we argue that (3) arguments against deeper integration between the fields of psychology and conceptual change are not convincing, and that recent theoretical developments in the cognitive sciences might prove indispensable in filling in the details in mechanisms of conceptual change. (shrink)

A central question in cognitive research concerns how new properties are generalized to categories. This article introduces a model of how generalizations involve a process of causal inference in which people estimate the likely presence of the new property in individual category exemplars and then the prevalence of the property among all category members. Evidence in favor of this causal‐based generalization (CBG) view included effects of an existing feature’s base rate (Experiment 1), the direction of the causal relations (Experiments 2 (...) and 4), the number of those relations (Experiment 3), and the distribution of features among category members (Experiments 4 and 5). The results provided no support for an alternative view that generalizations are promoted by the centrality of the to‐be‐generalized feature. However, there was evidence that a minority of participants based their judgments on simpler associative reasoning processes. (shrink)

Can understanding be based on false beliefs? I argue that it can. I first argue that the best way to understand the question is that it is whether one can increase one's degree of understanding by adopting an overall less accurate body of beliefs. I identify three sufficient conditions for one body of beliefs to be more accurate than another. Next, I appeal to two widely used methods of comparing degrees of understanding. With these methods, I show that understanding can (...) be gained by acquiring false beliefs. In addition, I show that this implies that understanding is not factive. (shrink)

Consider people’s ordinary concept of belief. This concept seems to pick out a particular psychological state. Indeed, one natural view would be that the concept of belief works much like the concepts one finds in cognitive science – not quite as rigorous or precise, perhaps, but still the same basic type of notion. But now suppose we turn to other concepts that people ordinarily use to understand the mind. Suppose we consider the concept happiness. Or the concept love. How are (...) these concepts to be understood? One obvious hypothesis would be that they are best understood as being more or less like the concept of belief. Maybe these concepts, too, pick out a particular mental state and thereby enable people to predict, explain and understand others’ behavior. We will argue that this hypothesis is mistaken. Instead, we suggest that the different concepts people use to understand the mind are fundamentally different from each other. Some of these concepts do indeed serve simply to pick out a particular mental state, but others allow a role for evaluative judgments. So, for example, our claim will be that when people are wondering whether a given agent is truly ‘happy’ or ‘in love,’ they are not merely trying to figure out whether this agent has a particular sort of mental state. They are also concerned in a central way with evaluating the agent herself. In short, our aim is to point to a striking sort of difference between the different concepts that people use to pick out psychological attitudes. We will be.. (shrink)

Are we entering a major new phase of modern science, one in which our standard, human modes of reasoning and understanding, including heuristics, have decreasing value? The new methods challenge human intelligibility. The digital revolution inspires such claims, but they are not new. During several historical periods, scientific progress has challenged traditional concepts of reasoning and rationality, intelligence and intelligibility, explanation and knowledge. The increasing intelligence of machine learning and networking is a deliberately sought, somewhat alien intelligence. As such, it (...) challenges the traditional, heuristic foresight of expert researchers. Nonetheless, science remains human-centered in important ways—and yet many of our ordinary human epistemic activities are alien to ourselves. This fact has always been the source of “the discovery problem”. It generalizes to the problem of understanding expert scientific practice. Ironically, scientific progress plunges us ever deeper into complexities beyond our grasp. But how is progress possible without traditional realism and the intelligibility realism requires? Pragmatic flexibility offers an answer. (shrink)

Abstract How do people find arguments while engaged in a discussion? Following an analogy with visual search, a mechanism that performs this task is described. It is a metarepresentational device that examines representations in a mostly serial manner until it finds a good enough argument supporting one’s position. It is argued that the mechanism described in dual process theories as ‘system 2’, or analytic reasoning fulfills these requirements. This provides support for the hypothesis that reasoning serves an argumentative function. Content (...) Type Journal Article Pages 1-20 DOI 10.1007/s10503-011-9256-1 Authors Hugo Mercier, Philosophy, Politics and Economics Program, University of Pennsylvania, 313 Cohen Hall, 249 South 36th Street, Philadelphia, PA 19104, USA Journal Argumentation Online ISSN 1572-8374 Print ISSN 0920-427X. (shrink)

Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented (...) a causal Bayes net model with separate error sources for causes and effects. In several experiments, we tested this new model using the size of Markov violations as the empirical indicator of differential assumptions about the sources of error. As predicted by the model, the size of Markov violations was influenced by the location of the agents and was moderated by the causal structure and the type of causal variables. (shrink)

Scientific and ‘intuitive’ or ‘folk’ theories are typically characterized as serving three critical functions: prediction, explanation, and control. While prediction and control have clear instrumental value, the value of explanation is less transparent. This paper reviews an emerging body of research from the cognitive sciences suggesting that the process of seeking, generating, and evaluating explanations in fact contributes to future prediction and control, albeit indirectly by facilitating the discovery and confirmation of instrumentally valuable theories. Theoretical and empirical considerations also suggest (...) why explanations may nonetheless feel intrinsically valuable. The paper concludes by considering some implications of the psychology of explanation for a naturalized philosophy of explanation. (shrink)

In pedagogical contexts and in everyday life, we often come to believe something because it would best explain the data. What is it about the explanatory endeavor that makes it essential to everyday learning and to scientific progress? There are at least two plausible answers. On one view, there is something special about having true explanations. This view is highly intuitive: it’s clear why true explanations might improve one’s epistemic position. However, there is another possibility—it could be that the process (...) of seeking, generating, or evaluating explanations itself puts one in a better epistemic position, even when the outcome of the process is not a true explanation. In other words, it could be that accurate explanations are beneficial, or it could be that high-quality explaining is beneficial, where there is something about the activity of looking for an explanation that improves our epistemic standing. The main goal of this paper is to tease apart these two possibilities, both theoretically and empirically, which we align with “Inference to the Best Explanation” and “Explaining for the Best Inference”, respectively. We also provide some initial support for EBI and identify promising directions for future research. (shrink)

While extremes tend to capture attention, the ordinary is often most of the story. So it may be with the structure-mapping process. The structure-mapping process can account for such pinnacles of thinking as analogy and metaphor, which can lead to overlooking the mundane, incremental use of structure mapping. Consequently, the current discussion shifts focus to the value of close comparisons between literally similar items for the development of knowledge. The intent is to foster greater integration between process and content as (...) well as between individuals and collectives. The payoff is identifying some undue simplifications and some promising new directions. (shrink)

Philosophical tradition and conspiracy theorists converge in suggesting that ordinary people ought to do their own research, rather than accept the word of others. In this paper, I argue that it’s no accident that conspiracy theorists value lay research on expert topics: such research is likely to undermine knowledge, via its effects on truth and justification. Accepting expert testimony is a far more reliable route to truth. Nevertheless, lay research has a range of benefits; in particular, it is likely to (...) lead to greater understanding, even when it does not lead to knowledge. I argue that we can reap most of the genuine benefits of lay research while minimizing the risks by engaging in exploratory, rather than truth-directed, inquiry. To engage in exploratory inquiry is to engage dogmatically, expecting to be unable to confirm the expert view or to disconfirm rivals. (shrink)

There is a robust scientific consensus concerning climate change and evolution. But many people reject these expert views, in favour of beliefs that are strongly at variance with the evidence. It is tempting to try to explain these beliefs by reference to ignorance or irrationality, but those who reject the expert view seem often to be no worse informed or any less rational than the majority of those who accept it. It is also tempting to try to explain these beliefs (...) by reference to epistemic overconfidence. However, this kind of overconfidence is apparently ubiquitous, so by itself it cannot explain the difference between those who accept and those who reject expert views. Instead, I will suggest that the difference is in important part explained by differential patterns of epistemic deference, and these patterns, in turn, are explained by the cues that we use to filter testimony. We rely on cues of benevolence and competence to distinguish reliable from unreliable testifiers, but when debates become deeply politicized, asserting a claim may itself constitute signalling lack of reliability. (shrink)

We are constantly called upon to evaluate the evidential weight of testimony, and to balance its deliverances against our own independent thinking. ‘Intellectual autonomy’ is the virtue that is supposed to be displayed by those who engage in cognition in this domain well. I argue that this is at best a misleading label for the virtue, because virtuous cognition in this domain consists in thinking with others, and intelligently responding to testimony. I argue that the existing label supports an excessively (...) individualistic conception of good thinking, both within and outside philosophy. I propose replacing ‘intellectual autonomy’ with ‘intellectual interdependence’, which properly emphasises the depth of our reliance on one another, without suggesting we ought ever to be epistemically servile. (shrink)

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability (...) of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches. (shrink)

Disease transmission is a fruitful domain in which to examine how scientific and folk theories interrelate, given laypeople’s access to multiple sources of information to explain events of personal significance. The current paper reports an in-depth survey of U.S. adults’ causal reasoning about two viral illnesses: a novel, deadly disease that has massively disrupted everyone’s lives, and a familiar, innocuous disease that has essentially no serious consequences. Participants received a series of closed-ended and open-ended questions probing their reasoning about disease (...) transmission, with a focus on causal mechanisms underlying disease contraction, transmission, treatment, and prevention; non-visible biological processes; and ontological frameworks regarding what kinds of entities viruses are. We also assessed participants’ attitudes, such as their trust in scientific experts and willingness to be vaccinated. Results indicated complexity in people’s reasoning, consistent with the co-existence of multiple explanatory frameworks. An understanding of viral transmission and viral replication existed alongside folk theories, placeholder beliefs, and lack of differentiation between viral and non-viral disease. For example, roughly 40% of participants who explained illness in terms of the transmission of viruses also endorsed a non-viral folk theory, such as exposure to cold weather or special foods as curative. Additionally, participants made use of competing modes of construal when explaining how viruses operate, such as framing the immune system response as cells trying to fight off the virus. Indeed, participants who displayed greater knowledge about viral transmission were significantly more likely to anthropomorphize bodily processes. Although comparisons of COVID-19 and the common cold revealed relatively few differences, the latter, more familiar disease elicited consistently lower levels of accuracy and greater reliance on folk theories. Moreover, for COVID-19 in particular, accuracy positively correlated with attitudes, self-protective behaviors, and willingness to be vaccinated. For both diseases, self-assessed knowledge about the disease negatively predicted accuracy. The results are discussed in relation to challenges for formal models of explanatory reasoning. (shrink)