It is often assumed that the capability to make inferences requires language. Against this assumption, I claim that inferential abilities do not necessarily require a language. On the contrary, certain cartographic systems could be used to explain some forms of inferences, and they are capable of warranting rational relations between contents they represent. By arguing that certain maps, as well as sentences, are adequate for inferential processes, I do not mean to neglect that there are important differences between maps and (...) sentences. Instead, the purpose of this paper is to highlight interesting distinctions that might affect the way that cartographic thinking works. (shrink)

A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...) syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

Few areas of scientific investigation have spawned more alternative approaches than animal behavior: comparative psychology, ethology, behavioral ecology, sociobiology, behavioral endocrinology, behavioral neuroscience, neuroethology, behavioral genetics, cognitive ethology, developmental psychobiology---the list goes on. Add in the behavioral sciences focused on the human animal, and you can continue the list with ethnography, biological anthropology, political science, sociology, psychology (cognitive, social, developmental, evolutionary, etc.), and even that dismal science, economics. Clearly, no reasonable-length chapter can do justice to such a varied collection. We (...) have opted therefore to focus on three of these subdisciplines and to provide a somewhat historical tour of them, mentioning along the way the philosophical points that are of particular interest to us, but allowing the development of these points to be limited only by the imaginations of our readers. For readers seeking a more-traditional historical survey, see Dewsbury (1984a, b) and Burghardt (1985a). Our chosen brief is to write about comparative psychology, ethology, and cognitive ethology, although other approaches, especially neuroscience, will be mentioned where appropriate. These sciences are philosophically significant because they are enmeshed in ancient philosophical questions about the nature of mind and purposeful action and about the differences between humans and other animals. These sciences are also clustered because of their attention to mechanistic explanations of individual animal behavior as opposed to attempting to capture regularities at a population level, such as the game-theoretic strategic models popular among behavioral ecologists. (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)

Response to spatial and nonspatial change in wild and Wistar rats The purpose of the experiment was to investigate the effects of domestication on exploration in rats. The comparison was made between wild Warsaw-Wild-Captive-Pisula-Stryjek rats and Wistar laboratory rats. The study used a purpose-built maze divided into zones connected with a corridor. Objects were placed in two out of four zones. Their location and shape were subject to experimental manipulation. Transporter used to move rats to the maze provided the opportunity (...) for spontaneous exploration of the experimental arena. Rats were subjected to a series of 10 sessions, followed by a spatial or nonspatial change in the experimental arena, after which another 5 experimental sessions were conducted. The study revealed that wild rats had much higher exploration latency than their laboratory counterparts. At each analyzed stage, WWCPS rats spent much more time in the transporter than Wistar rats. Wistar rats spent much more time during the experiment on object interaction in the experimental arena. In post-manipulation sessions, however, it was wild rats that explored object zones relatively longer than laboratory rats. No differences in the animals' behavior depending on the type of change were observed. Results suggest that wild rats tend to explore much more cautiously than laboratory rats and are more sensitive to changes in their environment. The underlying cause of these differences is likely to be the higher level of stress in wild rats, resulting from threats in their natural habitat. (shrink)

A leading theoretical framework for naturalistic explanation of mind holds that we explain the mind by positing progressively "stupider" capacities ("homunculi") until the mind is "discharged" by means of capacities that are not intelligent at all. The so-called homuncular fallacy involves violating this procedure by positing the same capacities at subpersonal levels. I argue that the homuncular fallacy is not a fallacy, and that modern-day homunculi are idle posits. I propose an alternative view of what naturalism requires that reflects how (...) the cognitive sciences are actually integrating mind and matter. (shrink)

In this paper, I develop an empirically-driven approach to the relationship between conceptual and non-conceptual representations. I begin by clarifying Wilfrid Sellars's distinction between a non-conceptual capacity to picture significant aspects of our world, and a capacity to stabilize semantic content in the form of conceptual representations that signify those aspects of the world that are relevant to our shared practices. I argue that this distinction helps to clarify the reason why cognition must be understood as embodied and situated. Drawing (...) on recent models of attention and valuation, I then argue that the human brain constructs a dynamic model of the world that it has encountered, encoding higher-level regularities in the form of linguistically structured representations. And I conclude by arguing that this approach to cognition provides a set of critical resources for understanding the situated nature of social cognition. (shrink)

The understanding of decision-making systems has come together in recent years to form a unified theory of decision-making in the mammalian brain as arising from multiple, interacting systems (a planning system, a habit system, and a situation-recognition system). This unified decision-making system has multiple potential access points through which it can be driven to make maladaptive choices, particularly choices that entail seeking of certain drugs or behaviors. We identify 10 key vulnerabilities in the system: (1) moving away from homeostasis, (2) (...) changing allostatic set points, (3) euphorigenic signals, (4) overvaluation in the planning system, (5) incorrect search of situation-action-outcome relationships, (6) misclassification of situations, (7) overvaluation in the habit system, (8) a mismatch in the balance of the two decision systems, (9) over-fast discounting processes, and (10) changed learning rates. These vulnerabilities provide a taxonomy of potential problems with decision-making systems. Although each vulnerability can drive an agent to return to the addictive choice, each vulnerability also implies a characteristic symptomology. Different drugs, different behaviors, and different individuals are likely to access different vulnerabilities. This has implications for an individual's susceptibility to addiction and the transition to addiction, for the potential for relapse, and for the potential for treatment. (shrink)

Name der Zeitschrift: Semiotica Jahrgang: 2017 Heft: 215 Seiten: 235-253.

Much of what we know about cognitive mapping comes from observing how biological agents behave in their physical environments, and several of these ideas were implemented on robots, imitating such a process. In this paper a novel approach to cognitive mapping is presented whereby robots are treated as a species of their own and their cognitive mapping is being investigated. Such robots are referred to as Albots. The design of the first Albot, Albot0, is presented. Albot0 computes an imprecise map (...) and employs a novel method to find its way home. Both the map and the return-home algorithm exhibited characteristics commonly found in biological agents. What we have learned from Albot0’s cognitive mapping are discussed. One major lesson is that the spatiality in a cognitive map affords us rich and useful information and this argues against recent suggestions that the notion of a cognitive map is not a useful one. (shrink)

Mercier & Sperber (M&S) argue for their argumentative theory in terms of communicative abilities. Insights can be gained by extending the discussion beyond human reasoning to rodent and robot navigation. The selection of arguments and conclusions that are mutually reinforcing can be cast as a form of abductive reasoning that I argue underlies the construction of cognitive maps in navigation tasks.

An important part of the explanatory role of concepts is that they enable us to combine a wide variety of objects, properties and relations in thought, with contents spanning diverse domains. I discuss an argument that appears to show that paradigmatic non-linguistic representational formats are unsuited to play this role, and thus conceptual representation could not occur in these formats. I show that this argument fails, because it overlooks the possibility of individual concepts being shared between a number of special (...) purpose representational systems. Demonstrating this requires defending the possibility of cross-format redeployment of concepts. (shrink)

While there is ongoing debate about the existence of basic emotions and about their status as natural kinds, these debates usually carry on under the assumption that basic emotions are modular and therefore cannot account for behavioral variability in emotional situations. Moreover, both sides of the debate have assumed that these putative features of basic emotions distinguish them as products of evolution rather than products of culture and experience. I argue that these assumptions are unwarranted, that there is empirical evidence (...) against them, and that evolutionary theory itself should not lead us to expect that behavioral invariability and modularity mark the distinction between evolved emotions and higher cognitive emotions. I further suggest that claims about behavioral invariability and modularity have functioned as defeasible conjectures aimed at helping test basic emotion theory. Finally, I draw out the implications of these claims for debates about the existence of basic emotions in humans. (shrink)

Some philosophers have laid down rather severe strictures on whether there can be thought without language. Wittgenstein asserted that ‘the limits of language…mean the limits of my world’. Davidson has argued that ‘a creature cannot have thoughts unless it is an interpreter of the speech of another’. Dummett has interpreted some pronouncements as meaning that ‘the study of thought is to be sharply distinguished from the study of the psychological processes of thinking and…the only proper method of analysing thought consists (...) in the analysis of language’. And there is also the position that thought has its own language that might exist even prior to or in the absence of natural language. But here I am going to concentrate on what might be possible in the absence of natural language. I do not know what it would mean to consider thinking in the absence of its own intrinsic language, a language of thought, if the two always co-exist. (shrink)

Critics of the computational connectionism of the last decade suggest that it shares undesirable features with earlier empiricist or associationist approaches, and with behaviourist theories of learning. To assess the accuracy of this charge the works of earlier writers are examined for the presence of such features, and brief accounts of those found are given for Herbert Spencer, William James and the learning theorists Thorndike, Pavlov and Hull. The idea that cognition depends on associative connections among large networks of neurons (...) is indeed one with precedents, although the implications of this for psychological issues have been interpreted variously — not all versions of connectionism are alike. (shrink)

Topics in Cognitive Science, Volume 14, Issue 2, Page 344-362, April 2022.

Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal-Proximity Decision-making (GPD), is implemented within the ACT-R cognitive framework. GPD is found to be more efficient than RL in three maze-navigation simulations. GPD advantages over RL seem to grow as task difficulty is increased. (...) An experiment is presented where participants are asked to make choices in the absence of prior reward. GPD captures human performance in this experiment better than RL. (shrink)

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The (...) second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data. (shrink)

Do animals require rich internal representations, such as cognitive maps, to navigate complex environments? Some researchers believe so, as they argue that sensory information is “too poor” to account for animals’ wayfinding abilities. However, this assumption is debatable, as James J. Gibson showed. Gibson proposed that wayfinding involves detecting information about environmental structure over time and used the concepts of “vistas” and “transitions” to explain terrestrial navigation. While these concepts may not apply universally to animal navigation, they highlight the importance (...) of exploiting stable environmental structures for wayfinding. By searching for species-relative environmental structures, we may gain insight into the navigational abilities of different nonhuman animals, while recognizing the unique evolutionary histories and ecological contexts that have shaped these abilities. (shrink)