This article discusses an approach to characterizing movement patterns (paths/trajectories) of individual agents that allows for relating aspects of cognitive conceptualization of movement patterns with formal spatial characterizations. To this end, we adopt a perspective of characterizing movement patterns on the basis of perceptual and conceptual invariants that we term movement choremes (MCs). MCs are formally grounded by behaviorally validating qualitative spatio-temporal calculi. Relating perceptual and cognitive aspects of space and formal theories of spatial information has shown promise to foster (...) understanding of the semantics of movement patterns. Specifically, we discuss our approach in relation to existing qualitative formalisms such as the region connection calculus and the Egenhofer’s intersection formalisms. We show that the MC approach is compatible with these approaches but offers additional opportunities to improve the cognitive adequacy of these formalisms. We summarize this paper using a movement taxonomy that also provides guidance for future research. (shrink)

The empirical literature on phenomenal causality (i.e., the notion that causality can be perceived) is reviewed. In Part I of this two-part series, different potential types of phenomenal causality (launching, triggering, reaction, tool, entraining, traction, braking, enforced disintegration and bursting, coordinated movement, penetration, expulsion) are described. Stimulus variables (temporal gap, spatial gap, spatial overlap, direction, absolute velocity, velocity ratio, trajectory length, radius of action, size, motion type, modality, animacy) and observer variables (attention, eye movements and fixation, prior experience, intelligence, age, (...) culture, psychopathology) that influence phenomenal causality are reviewed. This provides the necessary background for consideration in Part II (Hubbard, in press) of broader questions regarding properties of phenomenal causality, empirical and theoretical connections of phenomenal causality to other perceptual or cognitive phenomena or processes, and potential mechanisms and models of phenomenal causality. (shrink)

The empirical literature on phenomenal causality (the notion that causality can be perceived) is reviewed. Different potential types of phenomenal causality and variables that influence phenomenal causality were considered in Part I (Hubbard 2012b) of this two-part series. In Part II, broader questions regarding properties of phenomenal causality and connections of phenomenal causality to other perceptual or cognitive phenomena (different types of phenomenal causality, effects of spatial and temporal variance, phenomenal causality in infancy, effects of object properties, naïve physics, spatial (...) localization, other illusions, amodal completion, Gestalt principles of perceptual grouping, effects of context, differences between physical and social causality, effects of learning and experience, individual differences, effects of predictability, asymmetry in phenomenal causality, differences between perceived causality and perceived force, phenomenal causality in nonhuman animals) are considered. Potential mechanisms of phenomenal causality (inference from contiguity, a priori understanding, ampliation, perceptual learning, stimulus activity, beliefs regarding kinematics, haptic experience, beliefs regarding impetus, postdiction, innateness, modularity, specific neural structures) are also considered. (shrink)

Many animal species use tools, but human technical engagement is more complex. We argue that there is coevolution between technical engagement and advanced forms of causal cognition in the human lineage. As an analytic tool, we present a classification of different forms of causal thinking. Human causal thinking has become detached from space and time, so that instead of just reacting to perceptual input, our minds can simulate actions and forces and their causal consequences. Our main thesis is that, unlike (...) the situation for other primate species, an increasing emphasis on technical engagement made some hominins capable of reasoning about the forces involved in causal processes. This thesis is supported in three ways: We compare the casual thinking about forces of hominins with that of other primates. We analyze the causal thinking required for Stone Age hunting technologies such as throwing spears, bow hunting and the use of poisoned arrows, arguing that they may serve as examples of the expansion of casual cognition about forces. We present neurophysiological results that indicate the facilitation of advanced causal thinking. (shrink)

This article presents two learning processes in order to explain how children at an early age can transform a complex sensory input to concepts and categories. The first process constructs the perceptual structures that emerge in children’s cognitive development by detecting invariants in the sensory input. The invariant structures involve a reduction in dimensionality of the sensory information. It is argued that this process generates the primary domains of space, objects and actions and that these domains can be represented as (...) conceptual spaces. Once the primary domains have been established, the second process utilizes covariances between different dimensions of the domains in order to identify natural clusters of entities. The clusters are then are used to determine concepts as regions in the spaces. As an application, the processes are used to resolve the so-called ‘complex first paradox’ that emerges from the fact that children, in general, learn nouns earlier than adjectives, even though nouns are semantically more complex than adjectives. (shrink)

The world as we perceive it is structured into objects, actions and places that form parts of events. In this article, my aim is to explain why these categories are cognitively primary. From an empiricist and evolutionary standpoint, it is argued that the reduction of the complexity of sensory signals is based on the brain's capacity to identify various types of invariances that are evolutionarily relevant for the activities of the organism. The first aim of the article is to explain (...) why places, object and actions are primary cognitive categories in our constructions of the external world. It is shown that the invariances that determine these categories have their separate characteristics and that they are, by and large, independent of each other. This separation is supported by what is known about the neural mechanisms. The second aim is to show that the category of events can be analyzed as being constituted of the primary categories. The category of numbers is briefly discussed. Some implications for computational models of the categories are also presented. (shrink)

Reasoning is not just following logical rules, but a large part of human reasoning depends on our expectations about the world. To some extent, non-monotonic logic has been developed to account for the role of expectations. In this article, the focus is on expectations based on actions and their consequences. The analysis is based on a two-vector model of events where an event is represented in terms of two main components – the force of an action that drives the event, (...) and the result of its application. Actions are modelled in terms of the force domain and the results are modelled with the aid of different domains for locations or properties of objects. As a consequence, the assumption that reasoning about causal relations should be made in terms of propositional structures becomes very unnatural. Instead, the reasoning will be based on the geometric and topological properties of causes and effects modelled in conceptual spaces. (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)

In the course of our educational life we are introduced to various subject areas, each with its specific way of representing knowledge. The challenge for the learner is to be able to think in ways that are supported by, and match, the representational format. A fundamental question for the science of learning concerns how this is achieved. In this thesis, it will be argued that by observing individuals collaboratively constructing their own graphic representations in a subject area that is new (...) to them, it is possible to gain insight into this process. In the LUMES Master program, students are introduced to systems thinking and the method of creating Causal Loop Diagrams and interactive computer models for analyzing and solving complex problems. They learn how to use the terminology, symbols and methods of a specific semiotic domain. The research questions guiding the analysis of the empirical data concern how interacting with systems thinking tools affect students in the process of collaboratively analyzing and modeling complex problems, and how cognitive and semiotic resources are used in this process. The study provides a conceptual lens with which to discuss and perceive the complexity of learning to express knowledge with new representational tools: both its visual and spatial formalism and the related terminology. Three theoretical perspectives are applied: the socio-cultural perspective, the social semiotic, and the constructivist perspective. The sociocultural perspective is applied to emphasize the use of tools as a part of human learning. It puts focus on the mediating roles of language and other tools in a social context, which is in many ways connected to the social semiotic perspective. To view systems thinking and System Dynamics as a semiotic domain accentuates the use of specific terminology, symbols and gestures to express understanding. Furthermore, the constructivist perspective is used to put focus on the role of individual cognitive abilities and prior knowledge. Three empirical studies provide detailed analyses of video-recorded collaborative work in System Dynamics education. The case studies show how individual cognitive abilities together with language, gesture and the visual representations are used as integrated resources to express understanding of a problem and its solution. (shrink)