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  1. Perceptual symbol systems.Lawrence W. Barsalou - 1999 - Behavioral and Brain Sciences 22 (4):577-660.
    Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...)
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  • Against Logicist Cognitive Science.Mike Oaksford & Nick Chater - 1991 - Mind and Language 6 (1):1-38.
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  • From simple associations to systematic reasoning: A connectionist representation of rules, variables, and dynamic binding using temporal synchrony.Lokendra Shastri & Venkat Ajjanagadde - 1993 - Behavioral and Brain Sciences 16 (3):417-51.
    Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...)
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  • S eeingand visualizing: I T' S n otwhaty ou T hink.Zenon Pylyshyn - unknown
    6. Seeing With the Mind’s Eye 1: The Puzzle of Mental Imagery .................................................6-1 6.1 What is the puzzle about mental imagery?..............................................................................6-1 6.2 Content, form and substance of representations ......................................................................6-6 6.3 What is responsible for the pattern of results obtained in imagery studies?.................................6-8..
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  • Mental imagery.Nigel J. T. Thomas - 2001 - Stanford Encyclopedia of Philosophy.
    Mental imagery (varieties of which are sometimes colloquially refered to as “visualizing,” “seeing in the mind's eye,” “hearing in the head,” “imagining the feel of,” etc.) is quasi-perceptual experience; it resembles perceptual experience, but occurs in the absence of the appropriate external stimuli. It is also generally understood to bear intentionality (i.e., mental images are always images of something or other), and thereby to function as a form of mental representation. Traditionally, visual mental imagery, the most discussed variety, was thought (...)
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  • Naive physics.Barry Smith & Roberto Casati - 1994 - Philosophical Psychology 7 (2):227 – 247.
    The project of a 'naive physics' has been the subject of attention in recent years above all in the artificial intelligence field, in connection with work on common-sense reasoning, perceptual representation and robotics. The idea of a theory of the common-sense world is however much older than this, having its roots not least in the work of phenomenologists and Gestalt psychologists such as K hler, Husserl, Schapp and Gibson. This paper seeks to show how contemporary naive physicists can profit from (...)
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  • Mental models and the tractability of everyday reasoning.Mike Oaksford - 1993 - Behavioral and Brain Sciences 16 (2):360-361.
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  • A simplicity principle in unsupervised human categorization.Emmanuel M. Pothos & Nick Chater - 2002 - Cognitive Science 26 (3):303-343.
    We address the problem of predicting how people will spontaneously divide into groups a set of novel items. This is a process akin to perceptual organization. We therefore employ the simplicity principle from perceptual organization to propose a simplicity model of unconstrained spontaneous grouping. The simplicity model predicts that people would prefer the categories for a set of novel items that provide the simplest encoding of these items. Classification predictions are derived from the model without information either about the number (...)
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  • Similarity and rules: distinct? exhaustive? empirically distinguishable?Ulrike Hahn & Nick Chater - 1998 - Cognition 65 (2-3):197-230.
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  • The structures of the common-sense world.Barry Smith - 1995 - Acta Philosophica Fennica 58:290–317.
    While contemporary philosophers have devoted vast amounts of attention to the language we use in describing and finding our way about the world of everyday experience, they have, with few exceptions, refused to see this world itself as a fitting object of theoretical concern. In what follows I shall seek to show how the commonsensical world might be treated ontologically as an object of investigation in its own right. At the same time I shall seek to establish how such a (...)
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  • Dual processes, probabilities, and cognitive architecture.Mike Oaksford & Nick Chater - 2012 - Mind and Society 11 (1):15-26.
    It has been argued that dual process theories are not consistent with Oaksford and Chater’s probabilistic approach to human reasoning (Oaksford and Chater in Psychol Rev 101:608–631, 1994 , 2007 ; Oaksford et al. 2000 ), which has been characterised as a “single-level probabilistic treatment[s]” (Evans 2007 ). In this paper, it is argued that this characterisation conflates levels of computational explanation. The probabilistic approach is a computational level theory which is consistent with theories of general cognitive architecture that invoke (...)
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  • The rational analysis of mind and behavior.Nick Chater & Mike Oaksford - 2000 - Synthese 122 (1-2):93-131.
    Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning (...)
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  • The linguistic dead zone of value-aligned agency, natural and artificial.Travis LaCroix - 2024 - Philosophical Studies:1-23.
    The value alignment problem for artificial intelligence (AI) asks how we can ensure that the “values”—i.e., objective functions—of artificial systems are aligned with the values of humanity. In this paper, I argue that linguistic communication is a necessary condition for robust value alignment. I discuss the consequences that the truth of this claim would have for research programmes that attempt to ensure value alignment for AI systems—or, more loftily, those programmes that seek to design robustly beneficial or ethical artificial agents.
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  • Everyday reasoning and logical inference.Jon Barwise - 1993 - Behavioral and Brain Sciences 16 (2):337-338.
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  • The logical content of theories of deduction.Wilfrid Hodges - 1993 - Behavioral and Brain Sciences 16 (2):353-354.
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  • Précis of Deduction.Philip N. Johnson-Laird & Ruth M. J. Byrne - 1993 - Behavioral and Brain Sciences 16 (2):323-333.
    How do people make deductions? The orthodox view in psychology is that they use formal rules of inference like those of a “natural deduction” system.Deductionargues that their logical competence depends, not on formal rules, but on mental models. They construct models of the situation described by the premises, using their linguistic knowledge and their general knowledge. They try to formulate a conclusion based on these models that maintains semantic information, that expresses it parsimoniously, and that makes explicit something not directly (...)
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  • Introduction: Machine learning as philosophy of science.Kevin B. Korb - 2004 - Minds and Machines 14 (4):433-440.
    I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.
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  • Approximation of action theories and its application to conformant planning.Phan Huy Tu, Tran Cao Son, Michael Gelfond & A. Ricardo Morales - 2011 - Artificial Intelligence 175 (1):79-119.
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  • Pouring liquids: A study in commonsense physical reasoning.Ernest Davis - 2008 - Artificial Intelligence 172 (12-13):1540-1578.
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  • Response to Hanks and McDermott: Temporal Evolution of Beliefs and Beliefs about Temporal Evolution.Ronald P. Loui - 1987 - Cognitive Science 11 (3):283-297.
    This paper critically evaluates the celebrated paper of Hanks and McDermott on temporal projection, non-monotonic reasoning, and the frame problem. First I argue against their intuitions, and a fortiori, against their proposed solution. Next, I suggest how the solution they desire could be obtained, were they willing to represent the problem a bit differently.
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  • Distributing structure over time.John E. Hummel & Keith J. Holyoak - 1993 - Behavioral and Brain Sciences 16 (3):464-464.
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  • Mental models or formal rules?Philip N. Johnson-Laird & Ruth M. J. Byrne - 1993 - Behavioral and Brain Sciences 16 (2):368-380.
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  • Mental models cannot exclude mental logic and make little sense without it.Martin D. S. Braine - 1993 - Behavioral and Brain Sciences 16 (2):338-339.
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  • On rules, models and understanding.Jonathan St B. T. Evans - 1993 - Behavioral and Brain Sciences 16 (2):345-346.
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  • Deduction and degrees of belief.David Over - 1993 - Behavioral and Brain Sciences 16 (2):361-362.
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  • Connectionism, classical cognitive science and experimental psychology.Mike Oaksford, Nick Chater & Keith Stenning - 1990 - AI and Society 4 (1):73-90.
    Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model this (...)
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  • La physique naïve: un essai d'ontologie.Barry Smith & Roberto Casati - 1993 - Intellectica 17 (2):173--197.
    The project of a naive physics has been the subject of attention in recent years above all in the artificial intelligence field, in connection with work on common-sense reasoning, perceptual representation and robotics. The idea of a theory of the common-sense world is however much older than this, having its roots not least in the work of phenomenologists and Gestalt psychologists such as Kohler, Husserl, Schapp and Gibson. This paper seeks to show how contemporary naive physicists can profit from a (...)
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  • SAT-based planning in complex domains: Concurrency, constraints and nondeterminism.Claudio Castellini, Enrico Giunchiglia & Armando Tacchella - 2003 - Artificial Intelligence 147 (1-2):85-117.
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  • Deductive reasoning: What are taken to be the premises and how are they interpreted?Samuel Fillenbaum - 1993 - Behavioral and Brain Sciences 16 (2):348-349.
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  • Why cognitive science is not formalized folk psychology.Martin Pickering & Nick Chater - 1995 - Minds and Machines 5 (3):309-337.
    It is often assumed that cognitive science is built upon folk psychology, and that challenges to folk psychology are therefore challenges to cognitive science itself. We argue that, in practice, cognitive science and folk psychology treat entirely non-overlapping domains: cognitive science considers aspects of mental life which do not depend on general knowledge, whereas folk psychology considers aspects of mental life which do depend on general knowledge. We back up our argument on theoretical grounds, and also illustrate the separation between (...)
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  • Designing visual languages for description logics.Brian R. Gaines - 2009 - Journal of Logic, Language and Information 18 (2):217-250.
    Semantic networks were developed in cognitive science and artificial intelligence studies as graphical knowledge representation and inference tools emulating human thought processes. Formal analysis of the representation and inference capabilities of the networks modeled them as subsets of standard first-order logic (FOL), restricted in the operations allowed in order to ensure the tractability that seemed to characterize human reasoning capabilities. The graphical network representations were modeled as providing a visual language for the logic. Sub-sets of FOL targeted on knowledge representation (...)
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  • Artificial Intelligence Inheriting the Historical Crisis in Psychology: An Epistemological and Methodological Investigation of Challenges and Alternatives.Mohamad El Maouch & Zheng Jin - 2022 - Frontiers in Psychology 13:781730.
    By following the arguments developed by Vygotsky and employing the cultural-historical activity theory (CHAT) in addition to dialectical logic, this paper attempts to investigate the interaction between psychology and artificial intelligence (AI) to confront the epistemological and methodological challenges encountered in AI research. The paper proposes that AI is facing an epistemological and methodological crisis inherited from psychology based on dualist ontology. The roots of this crisis lie in the duality between rationalism and objectivism or in the mind-body rupture that (...)
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  • From symbols to neurons: Are we there yet?Garrison W. Cottrell - 1993 - Behavioral and Brain Sciences 16 (3):454-454.
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  • Deduction by children and animals: Does it follow the Johnson-Laird & Byrne model?Hank Davis - 1993 - Behavioral and Brain Sciences 16 (2):344-344.
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  • Mental-model theory and rationality.Pascal Engel - 1993 - Behavioral and Brain Sciences 16 (2):345-345.
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  • On modes of explanation.Rachel Joffe Falmagne - 1993 - Behavioral and Brain Sciences 16 (2):346-347.
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  • Monotonicity in Practical Reasoning.Kenneth G. Ferguson - 2003 - Argumentation 17 (3):335-346.
    Classic deductive logic entails that once a conclusion is sustained by a valid argument, the argument can never be invalidated, no matter how many new premises are added. This derived property of deductive reasoning is known as monotonicity. Monotonicity is thought to conflict with the defeasibility of reasoning in natural language, where the discovery of new information often leads us to reject conclusions that we once accepted. This perceived failure of monotonic reasoning to observe the defeasibility of natural-language arguments has (...)
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  • The argument for mental models is unsound.James H. Fetzer - 1993 - Behavioral and Brain Sciences 16 (2):347-348.
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  • Computational and biological constraints in the psychology of reasoning.Mike Oaksford & Mike Malloch - 1993 - Behavioral and Brain Sciences 16 (3):468-469.
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  • Mental models, more or less.Thad A. Polk - 1993 - Behavioral and Brain Sciences 16 (2):362-363.
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  • Models, rules and expertise.Rosemary J. Stevenson - 1993 - Behavioral and Brain Sciences 16 (2):366-366.
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  • Making a middling mousetrap.Michael R. W. Dawson & Istvan Berkeley - 1993 - Behavioral and Brain Sciences 16 (3):454-455.
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  • A number of questions about a question of number.Alan Garnham - 1993 - Behavioral and Brain Sciences 16 (2):350-351.
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  • Mental models: Rationality, representation and process.D. W. Green - 1993 - Behavioral and Brain Sciences 16 (2):352-353.
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  • Biological Agency: Its Subjective Foundations and a Large-Scale Taxonomy.Adelina Brizio & Maurizio Tirassa - 2016 - Frontiers in Psychology 7.
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  • Time phases, pointers, rules and embedding.John A. Barnden - 1993 - Behavioral and Brain Sciences 16 (3):451-452.
    This paper is a commentary on the target article by Lokendra Shastri & Venkat Ajjanagadde [S&A]: “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony” in same issue of the journal, pp.417–451. -/- It puts S&A's temporal-synchrony binding method in a broader context, comments on notions of pointing and other ways of associating information - in both computers and connectionist systems - and mentions types of reasoning that are a challenge to (...)
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  • Die Struktur der Common-Sense Welt.Barry Smith - 1994 - Logos. Anales Del Seminario de Metafísica [Universidad Complutense de Madrid, España] 1:422-449.
    Die zeitgenössischen Philosophen haben zwar der Sprache, die wir verwenden, um die Welt der alltäglichen Erfahrung zu beschreiben oder um uns in dieser Welt zurechtzufinden, große Aufmerksamkeit geschenkt, sie haben sich jedoch – von einigen Ausnahmen abgesehen – geweigert, diese Welt selbst als passendes Objekt theoretischer Betrachtungen anzusehen. Im folgenden werde ich versuchen zu zeigen, wie es möglich ist, die Common-Sense-Welt als ontologisch eigenständiges Untersuchungsobjekt zu verstehen. Gleichzeitig werde ich mich bemühen, deutlich zu zeigen, wie eine solch eigenständige Behandlung uns (...)
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  • Mental models and tableau logic.Avery D. Andrews - 1993 - Behavioral and Brain Sciences 16 (2):334-334.
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  • Deduction as an example of thinking.Jonathan Baron - 1993 - Behavioral and Brain Sciences 16 (2):336-337.
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  • Explaining Emotions.Paul O'Rorke & Andrew Ortony - 1994 - Cognitive Science 18 (2):283-323.
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