In this paper, I show how semantic factors constrain the understanding of the computational phenomena to be explained so that they help build better mechanistic models. In particular, understanding what cognitive systems may refer to is important in building better models of cognitive processes. For that purpose, a recent study of some phenomena in rats that are capable of ‘entertaining’ future paths (Pfeiffer and Foster 2013) is analyzed. The case shows that the mechanistic account of physical computation may be complemented (...) with semantic considerations, and in many cases, it actually should. (shrink)

This is a work concerned with justification and freedom and the relationship between these. Its summational aim is to defend a transcendental argument for free will – that we could not be epistemically justified in undermining a strong notion of free will, as a strong notion of free will would be required for any such process of undermining to be itself epistemically justified. The book advances two transcendental arguments – for a deontically internalist conception of epistemic justification and the aforementioned (...) argument for a libertarian conception of free will. In defending each of these arguments, the book both defends and relies upon the principle that ‘ought’ implies ‘can’. In articulating the latter transcendental argument – for freedom – heavy reliance is made on the earlier, epistemic, work: especially on the deontological conception of rational justification (on epistemic internalism). (shrink)

According to the traditional view, beliefs and desires are mental representations that play particular functional roles. A belief that P is state which represents P and plays the belief-role, while a desire that P is a state which represents that P and plays the desire-role. In this dissertation I argue that the traditional view has trouble accounting for (a) role that belief and desire play in the causal and rational explanation of behaviour and (b) our knowledge of our own conscious, (...) occurrent beliefs and desires. -/- In its place I argue for Pure Attitude Representationalism (PAR), which holds that beliefs and desires are not to be distinguished from one another by their functional role, but instead entirely by their representational content. According to PAR, desires are distinct from beliefs not because they do different things, but because desires represent things as being rewarding. Throughout the three papers that comprise this dissertation I argue that PAR provides a better explanation of (a) and (b). (shrink)

Passive frame theory attempts to illuminate what consciousnessis, in mechanistic and functional terms; it does not address the “implementation” level of analysis (how neurons instantiate conscious states), an enigma for various disciplines. However, in response to the commentaries, we discuss how our framework provides clues regarding this enigma. In the framework, consciousness is passive albeit essential. Without consciousness, there would not be adaptive skeletomotor action.

As an important view in the epistemology of perception, dogmatism proposes that for any experience, if it has a distinctive kind of phenomenal character, then it thereby provides us with immediate justification for beliefs about the external world. This paper rejects dogmatism by looking into the epistemology of imagining. In particular, this paper first appeals to some empirical studies on perceptual experiences and imaginings to show that it is possible for imaginings to have the distinctive phenomenal character dogmatists have in (...) mind. Then this paper argues that some of these imaginings fail to provide us with immediate justification for beliefs about the external world at least partly due to their inappropriate etiology. Such imaginings constitute counterexamples to dogmatism. (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)

What is the primary function of consciousness in the nervous system? The answer to this question remains enigmatic, not so much because of a lack of relevant data, but because of the lack of a conceptual framework with which to interpret the data. To this end, we have developed Passive Frame Theory, an internally coherent framework that, from an action-based perspective, synthesizes empirically supported hypotheses from diverse fields of investigation. The theory proposes that the primary function of consciousness is well-circumscribed, (...) serving the 'somatic nervous system[. For this system, consciousness serves as a frame that constrains and directs skeletal muscle output, thereby yielding adaptive behavior. The mechanism by which consciousness achieves this is more counterintuitive, passive, and “low level” than the kinds of functions that theorists have previously attributed to consciousness. Passive frame theory begins to illuminate (a) what consciousness contributes to nervous function, (b) how consciousness achieves this function, and (c) the neuroanatomical substrates of conscious processes. Our untraditional, action-based perspective focuses on olfaction instead of on vision and is 'descriptive' (describing the products of nature as they evolved to be) rather than 'normative' (construing processes in terms of how they should function). Passive frame theory begins to isolate the neuroanatomical, cognitive-mechanistic, and representational (e.g., conscious contents) processes associated with consciousness. (shrink)

The purpose of this paper is to present a general mechanistic framework for analyzing causal representational claims, and offer a way to distinguish genuinely representational explanations from those that invoke representations for honorific purposes. It is usually agreed that rats are capable of navigation because they maintain a cognitive map of their environment. Exactly how and why their neural states give rise to mental representations is a matter of an ongoing debate. I will show that anticipatory mechanisms involved in rats’ (...) evaluation of possible routes give rise to satisfaction conditions of contents, and this is why they are representationally relevant for explaining and predicting rats’ behavior. I argue that a naturalistic account of satisfaction conditions of contents answers the most important objections of antirepresentationalists. (shrink)

In various contexts and for various reasons, writers often define cognitive processes and architectures as those involving representational states and structures. Similarly, cognitive theories are also often delineated as those that invoke representations. In this paper, I present several reasons for rejecting this way of demarcating the cognitive. Some of the reasons against defining cognition in representational terms are that doing so needlessly restricts our theorizing, it undermines the empirical status of the representational theory of mind, and it encourages wildly (...) deflationary and explanatorily vacuous conceptions of representation. After criticizing this outlook, I sketch alternative ways we might try to capture what is distinctive about cognition and cognitive theorizing. (shrink)

While neuroscientists often characterize brain activity as representational, many philosophers have construed these accounts as just theorists’ glosses on the mechanism. Moreover, philosophical discussions commonly focus on finished accounts of explanation, not research in progress. I adopt a different perspective, considering how characterizations of neural activity as representational contributes to the development of mechanistic accounts, guiding the investigations neuroscientists pursue as they work from an initial proposal to a more detailed understanding of a mechanism. I develop one illustrative example involving (...) research on the information-processing mechanisms mammals employ in navigating their environments. This research was galvanized by the discovery in the 1970s of place cells in the hippocampus. This discovery prompted research in what the activity of these cells represents and how place representations figure in navigation. It also led to the discovery of a host of other types of neurons—grid cells, head-direction cells, boundary cells—that carry other types of spatial information and interact with place cells in the mechanism underlying spatial navigation. As I will try to make clear, the research is explicitly devoted to identifying representations and determining how they are constructed and used in an information processing mechanism. Construals of neural activity as representations are not mere glosses but are characterizations to which neuroscientists are committed in the development of their explanatory accounts. (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)

Theories of spatial cognition are derived from many sources. Psychologists are concerned with determining the features of the mind which, in combination with external inputs, produce our spatialized experience. A review of philosophical and other approaches has convinced us that the brain must come equipped to impose a three-dimensional Euclidean framework on experience – our analysis suggests that object re-identification may require such a framework. We identify this absolute, nonegocentric, spatial framework with a specific neural system centered in the hippocampus.A (...) consideration of the kinds of behaviours in which such a spatial mapping system would be important is followed by an analysis of the anatomy and physiology of this system, with special emphasis on the place-coded neurons recorded in the hippocampus of freely moving rats. A tentative physiological model for the hippocampal cognitive map is proposed. A review of lesion studies, in tasks as diverse as discrimination learning, avoidance, and extinction, shows that the cognitive map notion can adequately explain much of the data.The model is extended to humans by the assumption that spatial maps are built in one hemisphere, semantic maps in the other. The latter provide a semantic deep structure within which discourse comprehension and production can be achieved. Evidence from the study of amnesic patients, briefly reviewed, is consistent with this extension. (shrink)

A representation is a remnant of previous experience that allows that experience to affect later behavior. This paper develops a metatheoretical view of representation and applies it to issues concerning representation in animals. To describe a representational system one must specify the following: thedomainor range of situations in the represented world to which the system applies; thecontentor set of features encoded and preserved by the system; thecodeor transformational rules relating features of the representation to the corresponding features of the represented (...) world; themedium, or the representation's physical instantiation; and thedynamics, or how the system changes with time. In part because of the behaviorist assumption that the hypothetical, covert changes occurring in an organism during learning correspond to the overt physical changes that are observed, issues of representation in animal behavior have been largely ignored as irrelevant or misleading. However, it can be inferred that representations, acting as models of environmental regularities, operate at many levels of behavioral functioning, both cognitive and noncognitive. Objections to the use of this concept in explanations of animal behavior, based on the claim that it is indeterminate and on behaviorist considerations of parsimony, can be answered. Animal representations may be specialized in terms of tasks and species. Data from tasks involving spatial memory, delayed matching-to-sample, and sequence learning suggest some foundations for a general theory of animal representations. (shrink)

Purely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions have been demonstrated empirically. Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial (...) processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: How can objects be identified from simultaneously attended features in a parallel, distributed representation? How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention. (shrink)