There is an important hemispheric distinction in the functional organization of prefrontal cortex (PFC) that has not been fully recognized and explored. Research with split-brain patients provides considerable evidence for a left hemisphere (LH) “interpreter” that abhors indeterminacy and automatically draws inferences to complete patterns (real or imaginary). It is suggested that this “interpreter” function may be a byproduct of the linguistic capabilities of the LH. This same literature initially limited the role of the right hemisphere (RH) to little more (...) than visual organization. Recent reviews have garnered evidence for several different roles for the right PFC in reasoning, problem solving, and decision-making. We here focus on the beneficial but neglected role of indeterminacy in real-world problem solving and argue that the right PFC complements the left PFC “interpreter” by maintaining, and even enhancing indeterminacy. Successful real-world functioning is a delicate balancing act between these two systems. (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)

People are habitual explanation generators. At its most mundane, our propensity to explain allows us to infer that we should not drink milk that smells sour; at the other extreme, it allows us to establish facts (e.g., theorems in mathematical logic) whose truth was not even known prior to the existence of the explanation (proof). What do the cognitive operations underlying the inference that the milk is sour have in common with the proof that, say, the square root of two (...) is irrational? Our ability to generate explanations bears striking similarities to our ability to make analogies. Both reflect a capacity to generate inferences and generalizations that go beyond the featural similarities between a novel problem and familiar problems in terms of which the novel problem may be understood. However, a notable difference between analogy-making and explanation-generation is that the former is a process in which a single source situation is used to reason about a single target, whereas the latter often requires the reasoner to integrate multiple sources of knowledge. This seemingly small difference poses a challenge to the task of marshaling our understanding of analogical reasoning to understanding explanation. We describe a model of explanation, derived from a model of analogy, adapted to permit systematic violations of this one-to-one mapping constraint. Simulation results demonstrate that the resulting model can generate explanations for novel explananda and that, like the explanations generated by human reasoners, these explanations vary in their coherence. (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)

Mental recruitment of previous technological experience in conceptualizations of non-technological phenomena constitutes a specific kind of unintended cognitive effect of human technological activity. This paper discusses particular conceptual takes on a significant epistemic challenge faced by people in the Bronze Age eastern Mediterranean: that of understanding the factors and dynamics governing and allowing the most important celestial body to travel across the sky during the day. Textual sources, iconography and artefactual evidence in combination provide an outline of concrete conceptual solutions (...) to this challenge, which centre oncharioteering, i.e., the employment of light, horse-drawn vehicles for high-speed transport. These sources also inform on the actual, practical role played by this technological genre in the context of the eastern Mediterranean Bronze Age. The paper specifies a connection between the actual, mundane and social role of this form of vehicular technology in Bronze Age society, and the conceptualizations of an astronomical phenomenon to which it is recruited. This provides a specific case demonstrating how aspects of concrete, sensorimotor experience of technological activities, here the dynamics of vehicular transport, may ground associative conceptualization of empirically ill-specified phenomena. This, in turn, provides support for the general observation that the conceptual repertoires of individuals and collectives in particular historical contexts are influenced substantially by the experiential spectra associated with the specific ways of life into which they are born. (shrink)

Humans possess great capacity for behavioral and cultural change, but our ability to manage change is still limited. This article has two major objectives: first, to sketch a basic science of intentional change centered on evolution; second, to provide examples of intentional behavioral and cultural change from the applied behavioral sciences, which are largely unknown to the basic sciences community.All species have evolved mechanisms of phenotypic plasticity that enable them to respond adaptively to their environments. Some mechanisms of phenotypic plasticity (...) count as evolutionary processes in their own right. The human capacity for symbolic thought provides an inheritance system having the same kind of combinatorial diversity as does genetic recombination and antibody formation. Taking these propositions seriously allows an integration of major traditions within the basic behavioral sciences, such as behaviorism, social constructivism, social psychology, cognitive psychology, and evolutionary psychology, which are often isolated and even conceptualized as opposed to one another.The applied behavioral sciences include well-validated examples of successfully managing behavioral and cultural change at scales ranging from individuals to small groups to large populations. However, these examples are largely unknown beyond their disciplinary boundaries, for lack of a unifying theoretical framework. Viewed from an evolutionary perspective, they are examples of managing evolved mechanisms of phenotypic plasticity, including open-ended processes of variation and selection.Once the many branches of the basic and applied behavioral sciences become conceptually unified, we are closer to a science of intentional change than one might think. (shrink)

Explaining the causal origins of what are taken to be uniquely human capacities for understanding the mind in the first years of life is a primary goal of social cognitive development research, which concerns so called “theory of mind” or “mindreading” skills. We review and discuss particular examples of this research in the context of its underlying evolutionary conceptual framework known as the neo-Darwinian modern synthesis. It is increasingly recognized that the modern synthesis is limited in its neglect of developmental (...) issues. A recent convergence of work from diverse sources, including but not limited to evolutionary developmental biology (evo-devo) and developmental systems approaches, demonstrate the need for a developmental expansion of modern evolutionary theory. We attempt to show that not only are nativist explanations of early human social cognition vulnerable to the criticisms of this developmental shift in thinking, but that these criticisms also problematize the dominant and more mainstream theories in early social cognitive development research. We conclude by discussing the importance of developmental analysis in understanding the ontogeny of cognitive capacities in individuals as well as species. (shrink)

Osiurak and Reynaud highlight the critical role of technical-reasoning skills in the emergence of human cumulative technological culture, in contrast to previous accounts foregrounding social-reasoning skills as key to CTC. We question their analysis of the available evidence, yet for other reasons applaud the emphasis on causal understanding as central to the adaptive and collaborative dynamics of CTC.

thought, in general, and – reasoning by analogy, in particular, have been said to reside at the very summit of human cognition. Leech et al. endeavor to comprehend the development of analogous thinking in human beings. Applying Leech et al.'s general approach to the evolution of analogical behavior in animals might also prove to be of considerable value.

The ability to represent same-different relations is an important condition for abstract thought. However, there is mixed evidence for when this ability develops, both ontogenetically and phylogenetically. Apparent success in relational reasoning may be evidence for genuine conceptual understanding or may be the result of low-level, perceptual strategies. We introduce a method to discriminate these possibilities by pitting two conditions that are perceptually matched but conceptually different: in a "fused" condition, same and different objects are joined, creating single objects that (...) have the same perceptual features as the two object pairs in the "relational" condition. However, the "fused" objects do not provide evidence for the relation 'same.' Using this method with human toddlers in a causal relational reasoning task provides evidence for genuine conceptual understanding. This novel technique offers a simple manipulation that may be applied to a variety of existing match-to-sample procedures used to assess same-different reasoning to include in future research with non-human animals across species, as well as human infants. (shrink)

Whether any non-human animal can attribute mental states to others remains the subject of extensive debate. This despite the fact that several species have behaved as if they have a ‘theory of mind’ in various behavioral tasks. In this paper, we review the reasons of skeptics for their doubts: That existing experimental setups cannot distinguish between ‘mind readers’ and ‘behavior readers’, that results that seem to indicate ‘theory of mind’ may come from studies that are insufficiently controlled, and that our (...) own intuitive biases may lead us to interpret behavior more ‘cognitively’ than is necessary. The merits of each claim and suggested solution are weighed. The conclusion is that while it is true that existing setups cannot conclusively demonstrate ‘theory of mind’ in non-human animals, focusing on this fact is unlikely to be productive. Instead, the more interesting question is how sophisticated their social reasoning can be, whether it is about ‘unobservable inner experiences’ or not. Therefore, it is important to address concerns about the setup and interpretation of specific experiments. To alleviate the impact of intuitive biases, various strategies have been proposed in the literature. These include a deeper understanding of associative learning, a better knowledge of the limited ‘theory of mind’ humans actually use, and thinking of animal cognition in an embodied, embedded way; that is, being aware that constraints outside of the brain, and outside of the body, may naturally predispose individuals to produce behavior that looks smart without requiring complex cognition. To enable this kind of thinking, a powerful methodological tool is advocated: Computational modeling, namely agent-based modeling and, particularly, cognitive modeling. By explicitly simulating the rules and representations that underlie animal performance on specific tasks, it becomes much easier to look past one’s own biases and to see what cognitive processes might actually be occurring. (shrink)

In my response to the commentaries from a collection of esteemed researchers, I reassess and eventually find largely intact my claim that human tool use evidences higher social and non-social cognitive ability. Nonetheless, I concede that my examination of individual-level cognitive traits does not offer a full explanation of cumulative culture yet. For that, one needs to incorporate them into population-dynamic models of cultural evolution. I briefly describe my current and future work on this.

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.

Whether any non-human animal can attribute mental states to others remains the subject of extensive debate. This despite the fact that several species have behaved as if they have a ‘theory of mind’ in various behavioral tasks. In this paper, we review the reasons of skeptics for their doubts: That existing experimental setups cannot distinguish between ‘mind readers’ and ‘behavior readers’, that results that seem to indicate ‘theory of mind’ may come from studies that are insufficiently controlled, and that our (...) own intuitive biases may lead us to interpret behavior more ‘cognitively’ than is necessary. The merits of each claim and suggested solution are weighed. The conclusion is that while it is true that existing setups cannot conclusively demonstrate ‘theory of mind’ in non-human animals, focusing on this fact is unlikely to be productive. Instead, the more interesting question is how sophisticated their social reasoning can be, whether it is about ‘unobservable inner experiences’ or not. Therefore, it is important to address concerns about the setup and interpretation of specific experiments. To alleviate the impact of intuitive biases, various strategies have been proposed in the literature. These include a deeper understanding of associative learning, a better knowledge of the limited ‘theory of mind’ humans actually use, and thinking of animal cognition in an embodied, embedded way; that is, being aware that constraints outside of the brain, and outside of the body, may naturally predispose individuals to produce behavior that looks smart without requiring complex cognition. To enable this kind of thinking, a powerful methodological tool is advocated: Computational modeling, namely agent-based modeling and, particularly, cognitive modeling. By explicitly simulating the rules and representations that underlie animal performance on specific tasks, it becomes much easier to look past one’s own biases and to see what cognitive processes might actually be occurring. (shrink)

Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test the viability of the language exposure hypothesis by assessing whether models exposed to large quantities of human language display sensitivity to the implied knowledge states of characters in written passages. In pre‐registered analyses, we present a linguistic version of the False Belief Task (...) to both human participants and a large language model, GPT‐3. Both are sensitive to others' beliefs, but while the language model significantly exceeds chance behavior, it does not perform as well as the humans nor does it explain the full extent of their behavior—despite being exposed to more language than a human would in a lifetime. This suggests that while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible. (shrink)

The development of cognitive capacities depends on environmental conditions, including various forms of scaffolding. As a result, the evolution of cognition depends on the evolution of activities that provide scaffolding for cognitive development. Non-human animals reared and trained in environments heavily scaffolded with human social interaction can acquire non-species-typical knowledge, skills, and capacities. This can potentially shed light on some of the changes that paved the way for the evolution of distinctively human behavioral capacities such as language, advanced social cognition, (...) and elaborate forms of tool craft. In this light, I revisit several widely known—but also widely misunderstood—cases of exceptional animals and argue that each of these cases provides clues about key innovations in our own evolutionary history. (shrink)

We agree with Vaesen that there is evidence for cognitive differences between humans and other primates. However, it is too early to draw firm conclusions about the uniqueness of the cognitive mechanisms underlying human tool use. Tests of causal understanding are in their infancy, as is the study of animals more distantly related to humans.

Humans are not the only animals to deceive, though we might be the only ones that lie. The arms race von Hippel & Trivers (VH&T) propose may have only started during hominin evolution. VH&T offer a powerful theory, and I suggest it can be expanded to explain why there are systematic biases in human foresight.

Studies of primate cognition have conclusively shown that humans and apes share a range of basic cognitive abilities. As a corollary, these same studies have also focussed attention on what makes humans unique, and on when and how specifically human cognitive skills evolved. There is widespread agreement that a major distinguishing feature of the human mind is its capacity for causal reasoning. This paper argues that causal cognition originated with the use made of indirect natural signs by early hominins forced (...) to adapt to variable late Miocene and early Pliocene environments; that early hominins evolved an innate tendency to search for such signs and infer their causes; that causal inference required the existence of incipient working memory; and that causal relationships were stored through being integrated into spatial maps to create increasingly complex causal models of the world. (shrink)

This paper serves as an introduction to the special issue on “Reconciling Nature and Nurture in Behavior and Cognition Research” and sets its agenda to resolve the 'interactionist' dichotomy of nature as the genetic, and stable, factors of development, and nurture as the environmental, and plastic influences. In contrast to this received view it promotes the idea that all traits, no matter how developmentally fixed or universal they seem, contingently develop out of a single-cell state through the interaction of a (...) multitude of developmental resources that defies any easy, dichotomous separation. It goes on to analyze the necessary ingredients for such a radical, epigenetic account of development, heredity and evolution: 1. A detailed understanding of the epigenetic nature of the regulatory mechanisms of gene expression; 2. The systematical questioning of preconceptions of 'explanatory' categories of behavior, such as 'innate' or 'programmed'; 3. Especially in psychological research the integration of the concepts of 'development' and 'learning', and a richer classification of the concept of 'environment' in the production of behavior; 4. A fuller understanding of the nature of inheritance that transcends the restriction to the genetic material as the sole hereditary unit, and the study of the process of developmental niche construction; and last 5. Taking serious the role of ecology in development and evolution. I hope that an accomplishment of the above task will then lead to a 'postgenomic' synthesis of nature and nurture that conceptualizes 'nature' as the natural phenotypic outcome 'nurtured' by the natural developmental process leading to it. (shrink)

What could someone represent that would enable her to track, at least within limits, others' perceptions, knowledge states and beliefs including false beliefs? An obvious possibility is that she might represent these very attitudes as such. It is sometimes tacitly or explicitly assumed that this is the only possible answer. However, we argue that several recent discoveries in developmental, cognitive, and comparative psychology indicate the need for other, less obvious possibilities. Our aim is to meet this need by describing the (...) construction of a minimal theory of mind. Minimal theory of mind is rich enough to explain systematic success on tasks held to be acid tests for theory of mind cognition including many false belief tasks. Yet minimal theory of mind does not require representing propositional attitudes, or any other kind of representation, as such. Minimal theory of mind may be what enables those with limited cognitive resources or little conceptual sophistication, such as infants, chimpanzees, scrub‐jays and human adults under load, to track others' perceptions, knowledge states and beliefs. (shrink)

(2013). Why humans are (sometimes) less rational than other animals: Cognitive complexity and the axioms of rational choice. Thinking & Reasoning: Vol. 19, No. 1, pp. 1-26. doi: 10.1080/13546783.2012.713178.

Debates in animal cognition are frequently polarized between the romantic view that some species have human-like causal understanding and the killjoy view that human causal reasoning is unique. These apparently endless debates are often characterized by conceptual confusions and accusations of straw-men positions. What is needed is an account of causal understanding that enables researchers to investigate both similarities and differences in cognitive abilities in an incremental evolutionary framework. Here we outline the ways in which a three-dimensional model of causal (...) understanding fulfills these criteria. We describe how this approach clarifies what is at stake, illuminates recent experiments on both physical and social cognition, and plots a path for productive future research that avoids the romantic/killjoy dichotomy. (shrink)

In this paper I critically discuss and, in the end, reject Morgan’s Canon, a popular principle in comparative psychology. According to this principle we should always prefer explanations of animal behavior in terms of lower psychological processes over explanations in terms of higher psychological processes, when alternative explanations are possible. The validity of the principle depends on two things, a clear understanding of what it means for psychological processes to be higher or lower relative to each other and a justification (...) of a general preference for explanations that refer to lower psychological abilities. However, I argue that we cannot spell out the idea of a psychological scale in a way that claim is satisfied. I start with the discussion of different interpretations of the notion of a psychological scale. In Sect. 3, I discuss different possible strategies to justify any of those interpretations and argue that all of them fail. Finally, in Sect. 4, I generalize the argument for all possible interpretations of Morgan’s Canon and propose an alternative strategy: We should base our interpretations of animal behavior on more general principles such as evidential support and explanatory power, as followed in other scientific domains. (shrink)

Relational reasoning is a hallmark of human higher cognition and creativity, yet it is notoriously difficult to encourage in abstract tasks, even in adults. Generally, young children initially focus more on objects, but with age become more focused on relations. While prerequisite knowledge and cognitive resource maturation partially explains this pattern, here we propose a new facet important for children's relational reasoning development: a general orientation to relational information, or a relational mindset. We demonstrate that a relational mindset can be (...) elicited, even in 4‐year‐old children, yielding greater than expected spontaneous attention to relations. Children either generated or listened to an experimenter state the relationships between objects in a set of formal analogy problems, and then in a second task, selected object or relational matches according to their preference. Children tended to make object mappings, but those who generated relations on the first task selected relational matches more often on the second task, signaling that relational attention is malleable even in young children. (shrink)

A basic deep neural network (DNN) is trained to exhibit a large set of input–output dispositions. While being a good model of the way humans perform some tasks automatically, without deliberative reasoning, more is needed to approach human‐like artificial intelligence. Analysing recent additions brings to light a distinction between two fundamentally different styles of computation: content‐specific and non‐content‐specific computation (as first defined here). For example, deep episodic RL networks draw on both. So does human conceptual reasoning. Combining the two takes (...) advantage of the complementary costs and benefits of each. It also offers a better model of human cognitive competence. (shrink)

Among monkeys and apes, both the recognition and classification of individuals and the recognition and classification of vocalizations constitute discrete combinatorial systems. One system maps onto the other, suggesting that during human evolution kinship classifications and language shared a common cognitive precursor.

The goal of forming a science of intentional behavior requires a more richly detailed account of symbolic systems than is assumed by the authors. Cultural systems are not simply the equivalent in the ideational domain of culture of the purported Baldwin Effect in the genetic domain. © 2014 Cambridge University Press.

How do human children come up to carve up and think of the world around them in its most general and abstract structure? And to which degree are these general forms of viewing the world shared by other animals, notably by non-human primates? In response to these questions of what could be called comparative metaphysics, this paper discusses new evidence from developmental and comparative research to argue for the following picture: human children and non-human primates share a basic framework of (...) natural ontology: they think about their natural surroundings in essentialist ways in terms of natural kind objects constituted by their essential properties, and in generic terms as governed by general descriptive regularities. In contrast, there is a great divide when it comes to how human children and non-human primates carve up their social environment: only human children then go on to use their essentialist and generic thinking for developing a distinctively social ontology, to conceive of their surrounding in terms of socially constituted objects governed by general prescriptive norms. (shrink)

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential (...) promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

Analogical cognition, extensively researched by Dedre Gentner and her colleagues over the past thirty five years, has been described as the core of human cognition, and it characterizes our use of many words. This research provides significant insight into the nature of word meaning, but it has been ignored by linguists and philosophers of language. I discuss some of the implications of the research for our account of word meaning. In particular, I argue that the research points to, and helps (...) account for, a key explanatory role that linguistic meaning must play. The research also shows how words contribute to thought as opposed to merely being a means of conveying thought. (shrink)

Hoerl and McCormack demonstrate that although animals possess a sophisticated temporal updating system, there is no evidence that they also possess a temporal reasoning system. This important case study is directly related to the broader claim that although animals are manifestly capable of first-order relational reasoning, they lack the capacity for higher-order, role-based relational reasoning. We argue this distinction applies to all domains of cognition.

Hierarchical structures are rapidly and flexibly built up in the domains of human language and music. These domains require a tree-building capacity – “dendrophilia” – to dynamically infer hierarchical structures from sensory input, based on subunits stored in a lexicon. This dynamic process involves a crucial class of abstracta overlooked in the target article.

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...) to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an “assembly language” for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems. (shrink)

In our opinion, the discontinuity between extant human and nonhuman minds is much broader and deeper than most researchers admit. We are happy to report that Evans & Levinson's (E&L's) target article strongly corroborates our unpopular hypothesis, and that the comparative evidence, in turn, bolsters E&L's provocative argument. Both a Universal Grammar and the “mental continuity” between human and nonhuman minds turn out to be modern myths.

We are big fans of propositions. But we are not big fans of the proposed by Mitchell et al. The authors ignore the critical role played by implicit, non-inferential processes in biological cognition, overestimate the work that propositions alone can do, and gloss over substantial differences in how different kinds of animals and different kinds of cognitive processes approximate propositional representations.

We are in vehement agreement with most of Vaesen's key claims. But Vaesen fails to consider or rebut the possibility that there are deep causal dependencies among the various cognitive traits he identifies as uniquely human. We argue that is one such linchpin trait in the evolution of human tool use, social intelligence, language, and culture.

In our opinion, the discontinuity between extant human and nonhuman minds is much broader and deeper than most researchers admit. We are happy to report that Evans & Levinson's (E&L's) target article strongly corroborates our unpopular hypothesis, and that the comparative evidence, in turn, bolsters E&L's provocative argument. Both a Universal Grammar and the “mental continuity” between human and nonhuman minds turn out to be modern myths.

Cognitive Science, Volume 46, Issue 3, March 2022.