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)

This article has two goals. First, it synthesizes and critically assesses current scientific knowledge about the cognitive bases of human tool use. Second, it shows how the cognitive traits reviewed help to explain why technological accumulation evolved so markedly in humans, and so modestly in apes.

It is not a particularly hard thing to want or seek explanations. In fact, explanations seem to be a large and natural part of our cognitive lives. Children ask why and how questions very early in development and seem genuinely to want some sort of answer, despite our often being poorly equipped to provide them at the appropriate level of sophistication and detail. We seek and receive explanations in every sphere of our adult lives, whether it be to understand why (...) a friendship has foundered, why a car will not start, or why ice expands when it freezes. Moreover, correctly or incorrectly, most of the time we think we know when we have or have not received a good explanation. There is a sense both that a given, successful explanation satisfies a cognitive need, and that a questionable or dubious explanation does not. There are also compelling intuitions about what make good explanations in terms of their form, that is, a sense of when they are structured correctly. (shrink)

In this paper, I reject that animal reasoning, negation in particular, necessarily involves the representation of absences, as suggested by Bermúdez (2003, 2006, 2007), since this would still work as a logical negation (unavailable for non-linguistic creatures). False belief, pretense, and communication experiments show that non-human animals (at least some primates) have difficulties representing absent entities or properties. I offer an alternative account resorting to the sub-symbolic similarity judgments proposed by Vigo & Allen (2009) and expectations: animal proto-negation takes place (...) through the incompatibility between an expected and the actual representation. Finally, I propose that the expectation paradigm be extrapolated to other experi-ments in cognitive psychology (both with pre-linguistic children and animals) in order to design fair experiments that test other minds considering their true abilities. (shrink)

Communication, when defined as an act intended to affect the psychological state of another individual, demands the use of inference. Either the signaler, the recipient, or both must make leaps of understanding which surpass the semantic information available and draw from pragmatic clues to fully imbue and interpret meaning. While research into human communication and the evolution of language has long been comfortable with mentalistic interpretations of communicative exchanges, including rich attributions of mental state, research into animal communication has balked (...) at theoretical models which describe mentalized cognitive mechanisms. We submit a new theoretical perspective on animal communication: the model of inferential communication. For use when existing proximate models of animal communication are not sufficient to fully explain the complex, flexible, and intentional communication documented in certain species, specifically non-human primates, we present our model as a bridge between shallower, less cognitive descriptions of communicative behavior and the perhaps otherwise inaccessible mentalistic interpretations of communication found in theoretical considerations of human language. Inferential communication is a framework that builds on existing evidence of referentiality, intentionality, and social inference in primates. It allows that they might be capable of applying social inferences to a communicative setting, which could explain some of the cognitive processes that enable the complexity and flexibility of primate communication systems. While historical models of animal communication focus on the means-ends process of behavior and apparent cognitive outcomes, inferential communication invites consideration of the mentalistic processes that must underlie those outcomes. We propose a mentalized approach to questions, investigations, and interpretations of non-human primate communication. We include an overview of both ultimate and proximate models of animal communication, which contextualize the role and utility of our inferential communication model, and provide a detailed breakdown of the possible levels of cognitive complexity which could be investigated using this framework. Finally, we present some possible applications of inferential communication in the field of non-human primate communication and highlight the role it could play in advancing progress toward an increasingly precise understanding of the cognitive capabilities of our closest living relatives. (shrink)

Ape species-specific communication is grounded on the present, possesses some referential qualities and is mostly used to request objects or actions from others. Artificial systems of communication borrowed from humans transform apes' communicative exchanges by freeing them from the present (i.e. displaced reference) although requests still predominate as the main reason for communicating with others. Symbol use appears to enhance apes' relational abilities and their inhibitory control. Despite these substantial changes, it is concluded that even though artificial communication enhances thought (...) and enables its expression more openly, it does not create it or modify the motivation behind communicative exchanges. (shrink)

It is a commonplace that in philosophy, intuitions supply evidence for and against philosophical theories. Recent work in experimental philosophy has brought to bear the intuitions of philosophically naïve subjects in a number of different ways. One line of response to this work has been to claim that philosophers have expertise that privileges their intuitive judgments, and allows them to disregard the judgments of non-experts. This expertise is supposed to be analogous to the expertise of the mathematician or the physicist. (...) This paper critically evaluates this appeal to philosophical expertise. (shrink)

Mody and Carey (2016) investigated children's capacity to reason by the disjunctive syllogism by hiding stickers within two pairs of cups (i.e., there is one sticker in cup A or B, and one in cup C or D) and then showing one cup to be empty. They found that children as young as 3 years of age chose the most likely cup (i.e., not A, therefore choose B; and disregard C and D) and suggested that these children were representing the (...) dependent relationship between A and B by applying the logical operator “or”. However, it is possible that children succeeded using simpler strategies, such as avoiding the empty cup and choosing within the manipulated pair. We devised a new version of the task in which a sticker was visibly removed from one of the four cups so that 2.5- to 5-year-old children (N = 100) would fail if they relied on such strategies. We also included a conceptual replication of Mody and Carey's (2016) original condition. Our results replicated their findings and showed that even younger children, 2.5 years of age, could pass above chance levels. Yet, 2.5-, 3- and 4-year-olds failed the new condition. Only 5-year-old children performed above chance in both conditions and so provided compelling evidence of deductive reasoning from the premise “A or B", where “or” is exclusive. We propose that younger children may instead conceive of the relationship between A and B as inclusive “or” across both versions of the task. (shrink)

Colin Allen prescribes cognitive modeling as “the right kind of theory” to use in comparative animal cognition and predicts that unless researchers shift from using conceptual framework hypotheses (“the wrong kind of theory”) to cognitive models, the field will fail to be sustained or develop further. I argue, on the contrary, that the robust development of the field over the past 35 years actually belies Allen's dire prediction. What is more, there is reason to be concerned that if Allen's prescription (...) were wholeheartedly adopted by researchers, it would undermine one of the principal aims of the field: the discovery of the reasons for which animals act. Although cognitive modeling may be a useful tool to use to verify predictions made by conceptual framework hypotheses, I see no reason to think that comparative animal cognition would be, or needs to be, saved by researchers coming to taking cognitive modeling as the right kind of theory to use in the field. (shrink)

The present paper proposes a definition for the complex polysemic concepts of consciousness and awareness (in humans as well as in other species), and puts forward the idea of a progressive ontological development of consciousness from a state of ‘childhood’ awareness, in order to explain that humans are not only able to manipulate objects, but also their mental representations. The paper builds on the idea of qualia intended as entities posing regular invariant requests to neural processes, trough the permanence of (...) different properties. The concept of semantic differential introduces the properties of metaphorical qualia as an exclusively human ability. Furthermore this paper proposes a classification of qualia, according to the models–with different levels of abstraction–they are implied in, in a taxonomic perspective. This, in turn, becomes a source of categorization of divergent representations, sign systems, and forms of intentionality, relying always on biological criteria. New emerging image-of-the-world-devices are proposed, whose qualia are likely to be only accessible to humans: emotional qualia, where emotion accounts for the invariant and dominant property; and the qualic self where continuity, combined with the oneness of the self, accounts for the invariant and dominant property. The concept of congruence between different domains in a metaphor introduces the possibility of a general evaluation of truth and falsity of all kinds of metaphorical constructs, while the work of Matte Blanco enables us to classify conscious versus unconscious metaphors, both in individuals and in social organizations. (shrink)