Tania Lombrozo

Many natural and artificial entities can be predicted and explained both mechanistically, in term of parts and proximate causal processes, as well as functionally, in terms of functions and goals. Do these distinct “stances” or “modes of construal” support fundamentally different kinds of understanding? Based on recent work in epistemology and philosophy of science, as well as empirical evidence from cognitive and developmental psychology, this chapter argues for the “weak differentiation thesis”: the claim that mechanistic and functional understanding are distinct in that they involve importantly different objects. The chapter also considers more tentative arguments for the “strong differentiation thesis”: the claim that mechanistic and functional understanding involve different epistemic relationships between mind and world.

This chapter explains how norms influence mental state ascriptions by examining the relationship between prescriptive norms (moral and conventional) and ascriptions of intentional action. According to Joshua Knobe, there is an imbalance in judgments about whether morally good actions against bad side effects were done “intentionally,” a phenomenon he dubbed as the side-effect effect. It looks at the two contrasting perspectives in response to the side-effect effect, the Intuitive Moralist view and the Biased Scientist view, and proposes a third approach, the “Rational Scientist”. The chapter then presents studies testing the Rational Scientist as a conjecture about the relationship between moral evaluation and the theory of mind.

This chapter introduces “learning by thinking” (LbT) as a form of learning distinct from familiar forms of learning through observation. When learning by thinking, the learner gains genuinely new insight in the absence of novel observations “outside the head.” Scientific thought experiments are canonical examples, but the phenomenon is much more widespread, and includes learning by explaining to oneself, through analogical reasoning, or through mental simulation. The chapter argues that episodes of LbT can be re-expressed as explicit arguments or inferences but are neither psychologically nor epistemically reducible to explicit arguments or inferences, and that this partially explains the novelty of the conclusions reached through LbT. It also introduces a new perspective on the epistemic value of LbT processes as _practices_ with potentially beneficial epistemic consequences, even when the commitments they invoke and the conclusions they immediately deliver are not themselves true.

We used a survey to explore laypeople’s intuitions about the physical or non-physical nature of consciousness. The survey asked the same questions about consciousness and about digestion, a well-understood biological process. Participants rated agreement with statements that probed whether consciousness and digestion are constituted of physical substrates, whether physical substrates are necessary and sufficient for them, and whether they interact with the physical world. Results showed that participants generally attributed physical properties to digestion, while physicalist judgments about consciousness were mixed. Stronger belief in an immortal soul was associated with non-physicalist judgments about consciousness. These findings suggest that both physicalist and non-physicalist attitudes towards consciousness are common. Public intuitions about consciousness are important because of their potential to inform theories of consciousness itself – not because such beliefs can be taken at face value (they cannot), but because beliefs about consciousness constitute part of the phenomenon in need of explanation.

A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal trade-off depends on the decision-theoretic value of information for a given agent in a given context. This theory predicts that, all else being equal, agents prefer causal models that are as compressed as possible. When compression is associated with information loss, however, all else is not equal, and our theory predicts that agents will favor compressed models only when the information they sacrifice is not informative with respect to the agent’s anticipated decisions. We then show, across six studies reported here (N=2,364) and one study reported in the supplemental materials (N=182), that participants’ preferences over causal models are in keeping with the predictions of our theory. Our theory offers a unification of different dimensions of causal evaluation identified within the philosophy of science (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role

People routinely appeal to ‘beliefs’ in explaining behavior; psychologists do so as well (for instance, in explaining belief polarization and learning). Across three studies (N = 1,843, U.S-based adults), we challenge the assumption that ‘belief’ picks out a single construct in people’s theory of mind. Instead, laypeople attribute different kinds of beliefs depending on whether the beliefs play predominantly epistemic roles (such as truth-tracking) or non-epistemic roles (such as social signaling). We demonstrate that epistemic and non-epistemic beliefs are attributed under different circumstances (Study 1) and support different predictions about the believer’s values (Study 2) and behavior (Study 3). This differentiation emerges reliably across three distinct signatures of attributed belief, and even when the believed content and attributed level of certainty about that content are held constant across cases. Our findings call for a more fine-grained characterization of theory of mind and provide indirect support for the hypothesis that human cognition itself features multiple varieties of belief.

How does the act of explaining influence learning? Prior work has studied effects of explaining through a predominantly proximal lens, measuring short-term outcomes or manipulations within lab settings. Here, we ask whether the benefits of explaining extend to academic performance over time. Specifically, does the quality and frequency of student explanations predict students’ later performance on standardized tests of math and English? In Study 1 (N = 127 5th−6th graders), participants completed a causal learning activity during which their explanation quality was evaluated. Controlling for prior test scores, explanation quality directly predicted both math and English standardized test scores the following year. In Study 2 (N = 20,384 10th graders), participants reported aspects of teachers’ explanations and their own. Controlling for prior test scores, students’ own explanations predicted both math and English state standardized test scores, and teacher explanations were linked to test performance through students’ own explanations. Taken together, these findings suggest that benefits of explaining may result in part from the development of a metacognitive explanatory skill that transfers across domains and over time. Implications for cognitive science, pedagogy, and education are discussed.

Consider the following two (hypothetical) generic causal claims: “Living in a neighborhood with many families with children increases purchases of bicycles” and “living in an affluent neighborhood with many families with children increases purchases of bicycles.” These claims not only differ in what they suggest about how bicycle ownership is distributed across different neighborhoods (i.e., “the data”), but also have the potential to communicate something about the speakers’ values: namely, the prominence they accord to affluence in representing and making decisions about the social world. Here, we examine the relationship between the level of granularity with which a cause is described in a generic causal claim (e.g., neighborhood vs. affluent neighborhood) and the value of the information contained in the causal model that generates that claim. We argue that listeners who know any two of the following can make reliable inferences about the third: 1) the level of granularity at which a speaker makes a generic causal claim, 2) the speaker’s values, and 3) the data available to the speaker. We present results of four experiments (N=1,323) in the domain of social categories that provide evidence in keeping with these predictions.

Humans mentally engage with an astonishing range of phenomena. We can be aware of the atoms in our hands and of stars half as large as our solar system. We represent the invention of the Model T in the past and the consequences of global warming in the future. We keep track of the abstract properties of mathematical objects and the spatiotemporal properties of concrete objects. Through all this, one central way of mentally engaging with any phenomenon is by having beliefs about it. Believing is both fundamental and flexible. The present handbook gathers together a wide array of entries from different parts of cognitive science--anthropology, formal epistemology, philosophy of mind, psychology, and more--to address the question of what believing is and to connect that theoretical question to empirical research.

A growing body of research suggests that scientific and religious beliefs are often held and justified in different ways. In three studies with 707 participants, we examine the distinctive profiles of beliefs in these domains. In Study 1, we find that participants report evidence and explanatory considerations (making sense of things) as dominant reasons for beliefs across domains. However, cuing the religious domain elevates endorsement of nonscientific justifications for belief, such as ethical considerations (e.g., believing it encourages people to be good), affiliation (what loved ones believe), and intuition (what feels true in one's heart). Study 2 replicates these differences with specific scientific and religious beliefs held with equal confidence, and documents further domain differences in beliefs’ personal importance, openness to revision, and perceived objectivity. Study 3 replicates these differences, further finding that counter‐consensus beliefs about contentious science topics (such as climate change and vaccination) often have properties resembling religious beliefs, while counter‐religious beliefs about religion (e.g., “There is no God”) have properties that more closely resemble beliefs about science. We suggest that beliefs are held and justified within coherent epistemic frameworks, with individuals using different frameworks in different contexts and domains.

The new field of experimental philosophy has emerged as the methods of psychological science have been brought to bear on traditional philosophical issues. Oxford Studies in Experimental Philosophy will be the place to go to see outstanding new work in the field. It will feature papers by philosophers, papers by psychologists, and papers co-authored by people in both disciplines. The series heralds the emergence of a truly interdisciplinary field in which people from different disciplines are working together to address a shared set of questions.The inaugural volume is roughly structured into four sections. The first three papers focus on recent developments in moral psychology, a topic that has seen lively debate and a great deal of progress over the last decade. The second section highlights three contributions that bring new methods to moral psychology: formal modeling and special populations. The third section brings together four papers that adopt an experimental philosophy approach to novel topics, including intuitive dualism, generics, joint action, and happiness. And the last two papers provide critical and historical context to the development of experimental philosophy.

Explanation has been an important topic of study in philosophy of science, in epistemology, and in other areas of philosophy. In parallel, psychologists have been studying children's and adults’ explanations, including their role in inference and in learning. This entry reviews recent work that begins to bridge the philosophy and psychology of explanation, with sections introducing recent empirical work on explanation by philosophers, formal and functional accounts of explanation, inference to the best explanation, the role of explanation in discovery, and the implications of empirical work on explanation for the “negative program” in experimental philosophy.

Awe has traditionally been considered a religious or spiritual emotion, yet scientists often report that awe motivates them to answer questions about the natural world, and to do so in naturalistic terms. Indeed, awe may be closely related to scientific discovery and theoretical advance. Awe is typically triggered by something vast (either literally or metaphorically) and initiates processes of accommodation, in which existing mental schemas are revised to make sense of the awe‐inspiring stimuli. This process of accommodation is essential for the kind of belief revision that characterizes scientific reasoning and theory change. Across six studies, we find that the tendency to experience awe is positively associated with scientific thinking, and that this association is not shared by other positive emotions. Specifically, we show that the disposition to experience awe predicts a more accurate understanding of how science works, rejection of creationism, and rejection of unwarranted teleological explanations more broadly.

Occam's razor—the idea that all else being equal, we should pick the simpler hypothesis—plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam's razor is that more complex hypotheses tend to be more flexible—they can accommodate a wider range of possible data—and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people's intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In particular, people's judgments are consistent with the two most distinctive characteristics of BOR: They penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be “tuned” to fit the data better than comparatively simpler hypotheses.

Much recent work on explanation in the interventionist tradition emphasizes the explanatory value of stable causal generalizations—i.e., causal generalizations that remain true in a wide range of background circumstances. We argue that two separate explanatory virtues are lumped together under the heading of `stability’. We call these two virtues breadth and guidance respectively. In our view, these two virtues are importantly distinct, but this fact is neglected or at least under-appreciated in the literature on stability. We argue that an adequate theory of explanatory goodness should recognize breadth and guidance as distinct virtues, as breadth and guidance track different ideals of explanation, satisfy different cognitive and pragmatic ends, and play different theoretical roles in helping us understand the explanatory value of mechanisms. Thus keeping track of the distinction between these two forms of stability yields a more accurate and perspicuous picture of the role that stability considerations play in explanation.

We report three experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalization’s actual scope of application. In Experiment 3, we offer evidence that stable causal relationships may be seen as better guides to action. Collectively, these experiments document a previously underappreciated factor that shapes people’s causal reasoning: the stability of the causal relationship.

The notion of belief appears frequently in cognitive science. Yet it has resisted definition of the sort that could clarify inquiry. How then might a cognitive science of belief proceed? Here we propose a form of pluralism about believing. According to this view, there are importantly different ways to "believe" an idea. These distinct psychological kinds occur within a multi-dimensional property space, with different property clusters within that space constituting distinct varieties of believing. We propose that discovering such property clusters is empirically tractable, and that this approach can help sidestep merely verbal disputes about what constitutes “belief.”

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.