Cognitive Science, Volume 46, Issue 5, May 2022.

The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country and inferred the influence of the policies on the economy. The task was challenging and ambiguous, allowing participants to interpret the relations between the policies and the economy in multiple ways. In three studies, we found evidence of motivated reasoning despite financial incentives for accuracy. For (...) example, participants who believed that border security funding should be increased were more likely to conclude that increasing border security funding actually caused a better economy in the task. In Study 2, we hypothesized that having neutral preferences (e.g., preferring neither increased nor decreased spending on border security) would lead to more accurate assessments overall, compared to having a strong initial preference; however, we did not find evidence for such an effect. In Study 3, we tested whether providing participants with possible functional forms of the policies (e.g., the policy takes some time to work or initially has a negative influence but eventually a positive influence) would lead to a smaller influence of motivated reasoning but found little evidence for this effect. This research advances the field of causal learning by studying the role of prior preferences, and in doing so, integrates the fields of causal learning and motivated reasoning using a novel explore‐exploit task. (shrink)

ABSTRACT This article proposes that Dorothy Dinnerstein’s philosophy can help us understand the problem of miseducation that places male-dominated and ‘masculine’ rapacity at the center of so many human endeavors, including capitalist economic exploitation and environmental exploitation. Dinnerstein argues that early childhood experiences of female domination lead to reactive and immature adult preferences for excessive, triumphing, rapacious, male rule. In Dinnerstein’s theory, the solution to this psychologically deep-rooted problem is for men to do half of the childcare work. This article (...) acknowledges and refutes arguments against Dinnerstein’s theory, and expounds its soundness and benefits. Dinnerstein’s thought can help us put a brake on the capitalist rapacity that threatens our very survival while increasing possibilities for human self-actualization. (shrink)

Constructing an intuitive theory from data confronts learners with a “chicken‐and‐egg” problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken‐and‐egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We (...) present 4‐ and 5‐year‐olds with two different simplified magnet‐learning tasks. Children appropriately constrain their beliefs to two hypotheses following ambiguous but informative evidence. Following a critical intervention, they learn the correct theory. In the second study, children infer the correct number of categories given no information about the possible causal laws. Children's hypotheses in these tasks are explained as rational inferences within a Bayesian computational framework. (shrink)

From early in childhood, humans exhibit sophisticated intuitions about how to share knowledge efficiently in simple controlled studies. Yet, untrained adults often fail to teach effectively in real‐world situations. Here, we explored what causes adults to struggle in informal pedagogical exchanges. In Experiment 1, we first showed evidence of this effect, finding that adult participants failed to communicate their knowledge to naïve learners in a simple teaching task, despite reporting high confidence that they taught effectively. Using a computational model of (...) rational teaching, we found that adults assigned to our teaching condition provided highly informative examples but failed to teach effectively because their examples were tailored to learners who were only considering a small set of possible explanations. In Experiment 2, we then found experimental evidence for this possibility, showing that knowledgeable participants systematically misunderstand the beliefs of naïve participants. Specifically, knowledgeable participants assumed naïve agents would primarily consider hypotheses close to the correct one. Finally, in Experiment 3, we aligned learners’ beliefs to knowledgeable agents’ expectations and showed learners the same examples selected by participants assigned to teach in Experiment 1. We found that these same examples were significantly more informative once learners’ hypothesis spaces were constrained to match teachers’ expectations. Our findings show that, in informal settings, adult pedagogical failures result from an inaccurate representation of what naïve learners believe is plausible and not an inability to select informative data in a rational way. (shrink)

Affective shifts have been linked to work attitudes and behaviors recently, but previous researches only focused on affective shift during work, with little attention to affective shifts outside work. Conservation of resources and personality system interaction theories are used to design a 2-week daily dairy study. Participants report how affective shifts outside work affect their subsequent-day task performance, emotional exhaustion, and CWB. As expected, findings indicate that shifts in affect outside work meaningfully impact job performance and work attitudes. That is, (...) when both positive and negative affect upshift outside work, employees perform their tasks better but also experience increased emotional exhaustion. Practical implications and limitations are discussed. (shrink)

A full account of purposive action must appeal not only to propositional attitude states like beliefs, desires, and intentions, but also to motor representations, i.e., non-propositional states that are thought to represent, among other things, action outcomes as well as detailed kinematic features of bodily movements. This raises the puzzle of how it is that these two distinct types of state successfully coordinate. We examine this so-called “Interface Problem”. First, we clarify and expand on the nature and role of motor (...) representations in explaining intentional action. Next, we characterize the respective functions of intentions and motor representations, the differences in representational format and content that these imply, and the interface challenge these differences in turn raise. We then evaluate Butterfill and Sinigaglia’s recent answer to this interface challenge, according to which intentions refer to action outcomes by way of demonstrative deference to motor representations. We present some worries for this proposal, arguing that, among other things, it implicitly presupposes a solution to the problem, and so cannot help to resolve it. Finally, we suggest that we may make some progress on this puzzle by positing a “content-preserving causal process” taking place between intentions and motor representations, and we offer a proposal for how this might work. (shrink)

Can we be responsible for our attention? Can attention be epistemically good or bad? Siegel tackles these under‐explored questions in “Selection Effects”, a pathbreaking chapter of The Rationality of Perception. In this chapter, Siegel develops one of the first philosophical accounts of attention norms. Her account is inferential: patterns of attention are often controlled by inferences and therefore subject to rational epistemic norms that govern any other form of inference. Although Siegel’s account is explanatorily powerful, it cannot capture a core (...) attention norm in cognitive science: one should balance between exploratory and exploitative attention. For central cases of exploratory attention such as mind‐wandering, child‐like, and creative thinking are non‐inferential. Siegel’s view classifies them as “normative freebies” that are not subject to epistemic evaluation. We’re therefore left with a disjunctive conclusion: either Siegel’s inferentialist theory of attention norms is incomplete or cognitive scientists are wrong about the norms that govern attention. (shrink)

Research has probed the consequences of providing people with different types of information regarding why a person possesses a certain characteristic. However, this work has largely examined the consequences of different information subsets (e.g., information focusing on internal versus societal causes). Less work has compared several types of information within the same paradigm. Using the legal system as an example domain, we provided children (N=198 6- to 8-year-olds) with several types of information—including information highlighting internal moral character, internal biological factors, (...) behavioral factors, and societal factors—about why a specific outcome (incarceration) might occur. We examined how such language shaped children’s attitudes. In Study 1, children reported the most positivity toward people who were incarcerated for societal reasons and the least positivity toward people who were incarcerated for their internal moral character; attitudes linked with behavioral information fell between these extremes. Studies 2a-2b suggested that Study 1’s effects could not be fully explained by participants drawing different about individuals in Study 1. Study 3 replicated Study 1’s results and showed that information linking incarceration with internal biological factors led to more positivity than information linking incarceration with internal moral character. Finally, Study 4 suggested that the patterns found in Studies 1 and 3 generalize to non-punitive contexts. Moreover, Study 4 found that the effects in Studies 1 and 3 emerged regardless of whether information was communicated via explanations or descriptions. These results demonstrate that how we express our beliefs about social phenomena shape the realities in which others live. (shrink)

Imaginary worlds are extremely successful. The most popular fictions produced in the last few decades contain such a fictional world. They can be found in all fictional media, from novels (e.g., Lord of The Rings and Harry Potter) to films (e.g., Star Wars and Avatar), video games (e.g., The Legend of Zelda and Final Fantasy), graphic novels (e.g., One Piece and Naruto), and TV series (e.g., Star Trek and Game of Thrones), and they date as far back as ancient literature (...) (e.g., the Cyclops Islands in The Odyssey, 850 BCE). Why such a success? Why so much attention devoted to non-existent worlds? In this paper, we propose that imaginary worlds co-opt our preferences for exploration, which have evolved in humans and nonhuman animals alike, to propel individuals toward new environments and new sources of reward. Humans would find imaginary worlds very attractive for the very same reasons, and under the same circumstances, as they are lured by unfamiliar environments in real life. After reviewing research on exploratory preferences in behavioral ecology, environmental esthetics, neuroscience, and evolutionary and developmental psychology, we focus on the sources of their variability across time and space, which we argue can account for the variability of the cultural preference for imaginary worlds. This hypothesis can, therefore, explain the way imaginary worlds evolved culturally, their shape and content, their recent striking success, and their distribution across time and populations. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)