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  1. Models of Disability in Children’s Pretend Play: Measurement of Cognitive Representations and Affective Expression Using the Affect in Play Scale.Stefano Federici, Fabio Meloni, Antonio Catarinella & Claudia Mazzeschi - 2017 - Frontiers in Psychology 8.
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  • Positive–Negative Asymmetry in the Evaluations of Political Candidates. The Role of Features of Similarity and Affect in Voter Behavior.Andrzej Falkowski & Magdalena Jabłońska - 2018 - Frontiers in Psychology 9.
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  • Semantics and complexity of recursive aggregates in answer set programming.Wolfgang Faber, Gerald Pfeifer & Nicola Leone - 2011 - Artificial Intelligence 175 (1):278-298.
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  • Formalizing nonmonotonic reasoning systems.David W. Etherington - 1987 - Artificial Intelligence 31 (1):41-85.
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  • On the Problems Solved by Cognitive Processes.Paul E. Smaldino, David Pietraszewski & Annie E. Wertz - 2023 - Cognitive Science 47 (6):e13297.
    Cognitive scientists have focused too narrowly on the acquisition of data and on the methods to extract patterns from those data. We argue that a successful science of the mind requires widening our focus to include the problems being solved by cognitive processes. Frameworks that characterize cognitive processes in terms of instrumental problem‐solving, such as those within the evolutionary social sciences, become necessary if we wish to discover more accurate descriptions of those processes.
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  • Artificial intelligence & games: Should computational psychology be revalued?Marco Ernandes - 2005 - Topoi 24 (2):229-242.
    The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to promote (...)
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  • On the Meaning of Words and Dinosaur Bones: Lexical Knowledge Without a Lexicon.Jeffrey L. Elman - 2009 - Cognitive Science 33 (4):547-582.
    Although for many years a sharp distinction has been made in language research between rules and words—with primary interest on rules—this distinction is now blurred in many theories. If anything, the focus of attention has shifted in recent years in favor of words. Results from many different areas of language research suggest that the lexicon is representationally rich, that it is the source of much productive behavior, and that lexically specific information plays a critical and early role in the interpretation (...)
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  • A society of ideas on cognition.Michael G. Dyer - 1991 - Artificial Intelligence 48 (3):321-334.
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  • On some supposed contributions of artificial intelligence to the scientific study of language.B. Elan Dresher & Norbert Hornstein - 1976 - Cognition 4 (December):321-398.
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  • Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services.Jon Doyle & Ramesh S. Patil - 1991 - Artificial Intelligence 48 (3):261-297.
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  • A truth maintenance system.Jon Doyle - 1979 - Artificial Intelligence 12 (3):231-272.
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  • Comparative concepts.Richard Dietz - 2013 - Synthese 190 (1):139-170.
    Comparative concepts such as greener than or higher than are ways of ordering objects. They are fundamental to our grasp of gradable concepts, that is, the type of meanings expressed by gradable general terms, such as "is green" or "is high", which are embeddable in comparative constructions in natural language. Some comparative concepts seem natural, whereas others seem gerrymandered. The aim of this paper is to outline a theoretical approach to comparative concepts that bears both on the account of naturalness (...)
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  • Peculiarities in Mind; Or, on the Absence of Darwin.Tanya de Villiers-Botha - 2011 - South African Journal of Philosophy 30 (3):282-302.
    A key failing in contemporary philosophy of mind is the lack of attention paid to evolutionary theory in its research projects. Notably, where evolution is incorporated into the study of mind, the work being done is often described as philosophy of cognitive science rather than philosophy of mind. Even then, whereas possible implications of the evolution of human cognition are taken more seriously within the cognitive sciences and the philosophy of cognitive science, its relevance for cognitive science has only been (...)
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  • Mundane reasoning by settling on a plausible model.Mark Derthick - 1990 - Artificial Intelligence 46 (1-2):107-157.
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  • The intelligence left in AI.Denis L. Baggi - 2000 - AI and Society 14 (3-4):348-378.
    In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents a (...)
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  • Metaphorical Expressions and Culture: An Indirect Link.Alice Deignan - 2003 - Metaphor and Symbol 18 (4):255-271.
    Lakoff (1993) argued that basic level conceptual metaphors are grounded in human experience, and are therefore likely to be found widely across different languages and cultures. However, other mappings may not be shared. It is well documented that many metaphorical expressions vary across languages, and a number of researchers have argued cultural motivations for this. Possible reasons for cross-linguistic differences in metaphor are that different cultures hold different attitudes to metaphor vehicles, or that the source domain entities and events are (...)
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  • Temporal data base management.Thomas L. Dean & Drew V. McDermott - 1987 - Artificial Intelligence 32 (1):1-55.
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  • Visual Narrative Structure.Neil Cohn - 2013 - Cognitive Science 37 (3):413-452.
    Narratives are an integral part of human expression. In the graphic form, they range from cave paintings to Egyptian hieroglyphics, from the Bayeux Tapestry to modern day comic books (Kunzle, 1973; McCloud, 1993). Yet not much research has addressed the structure and comprehension of narrative images, for example, how do people create meaning out of sequential images? This piece helps fill the gap by presenting a theory of Narrative Grammar. We describe the basic narrative categories and their relationship to a (...)
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  • Domains and image schemas.Timothy C. Clausner & William Croft - 1999 - Cognitive Linguistics 10 (1):1-31.
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  • A biological metaphor.Andy Clark - 1986 - Mind and Language 1 (1):45-64.
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  • Why did John Herschel fail to understand polarization? The differences between object and event concepts.Xiang Chen - 2003 - Studies in History and Philosophy of Science Part A 34 (3):491-513.
    This paper offers a solution to a problem in Herschel studies by drawing on the dynamic frame model for concept representation offered by cognitive psychology. Applying the frame model to represent the conceptual frameworks of the particle and wave theories, this paper shows that discontinuity between the particle and wave frameworks consists mainly in the transition from a particle notion ‘side’ to a wave notion ‘phase difference’. By illustrating intraconceptual relations within concepts, the frame representations reveal the ontological differences between (...)
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  • The Function of Scientific Concepts.Hyundeuk Cheon - forthcoming - Foundations of Science:1-15.
    The function of concepts must be taken seriously to understand the scientific practices of developing and working with concepts. Despite its significance, little philosophical attention has been paid to the function of concepts. A notable exception is Brigandt (2010), who suggests incorporating the epistemic goal pursued with the concept’s use as an additional semantic property along with the reference and inferential role. The suggestion, however, has at least two limitations. First, his proposal to introduce epistemic goals as the third component (...)
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  • The paradox of social interaction: Shared intentionality, we-reasoning, and virtual bargaining.Nick Chater, Hossam Zeitoun & Tigran Melkonyan - 2022 - Psychological Review 129 (3):415-437.
    Social interaction is both ubiquitous and central to understanding human behavior. Such interactions depend, we argue, on shared intentionality: the parties must form a common understanding of an ambiguous interaction. Yet how can shared intentionality arise? Many well-known accounts of social cognition, including those involving “mind-reading,” typically fall into circularity and/or regress. For example, A’s beliefs and behavior may depend on her prediction of B’s beliefs and behavior, but B’s beliefs and behavior depend in turn on her prediction of A’s (...)
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  • On the use of framed knowledge in language comprehension.Eugene Charniak - 1978 - Artificial Intelligence 11 (3):225-265.
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  • A usage-based account of subextraction effects.Rui P. Chaves & Adriana King - 2019 - Cognitive Linguistics 30 (4):719-750.
    The idea that conventionalized general knowledge – sometimes referred to as a frame – guides the perception and interpretation of the world around us has long permeated various branches of cognitive science, including psychology, linguistics, and artificial intelligence. In this paper we provide experimental evidence suggesting that frames also play a role in explaining certain long-distance dependency phenomena, as originally proposed by Deane. We focus on a constraint that restricts the extraction of an NP from another NP, called subextraction, which (...)
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  • A common representation for problem-solving and language-comprehension information.Eugene Charniak - 1981 - Artificial Intelligence 16 (3):225-255.
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  • Attention-based visual routines: sprites.Patrick Cavanagh, Angela T. Labianca & Ian M. Thornton - 2001 - Cognition 80 (1-2):47-60.
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  • How to Frame Understanding in Mathematics: A Case Study Using Extremal Proofs.Merlin Carl, Marcos Cramer, Bernhard Fisseni, Deniz Sarikaya & Bernhard Schröder - 2021 - Axiomathes 31 (5):649-676.
    The frame concept from linguistics, cognitive science and artificial intelligence is a theoretical tool to model how explicitly given information is combined with expectations deriving from background knowledge. In this paper, we show how the frame concept can be fruitfully applied to analyze the notion of mathematical understanding. Our analysis additionally integrates insights from the hermeneutic tradition of philosophy as well as Schmid’s ideal genetic model of narrative constitution. We illustrate the practical applicability of our theoretical analysis through a case (...)
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  • Book Review. [REVIEW]Rosario Caballero - 2007 - Metaphor and Symbol 22 (1):109-118.
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  • Language, Common Sense, and the Winograd Schema Challenge.Jacob Browning & Yann LeCun - forthcoming - Artificial Intelligence.
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  • Legal ontologies in knowledge engineering and information management.Joost Breuker, André Valente & Radboud Winkels - 2004 - Artificial Intelligence and Law 12 (4):241-277.
    In this article we describe two core ontologies of law that specify knowledge that is common to all domains of law. The first one, FOLaw describes and explains dependencies between types of knowledge in legal reasoning; the second one, LRI-Core ontology, captures the main concepts in legal information processing. Although FOLaw has shown to be of high practical value in various applied European ICT projects, its reuse is rather limited as it is rather concerned with the structure of legal reasoning (...)
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  • Taking Up Thagard’s Challenge: A Formal Model of Conceptual Revision.Sena Bozdag & Matteo De Benedetto - 2022 - Journal of Philosophical Logic 51 (4):791-824.
    Thagard presented a framework for conceptual change in science based on conceptual systems. Thagard challenged belief revision theorists, claiming that traditional belief-revision systems are able to model only the two most conservative types of changes in his framework, but not the more radical ones. The main aim of this work is to take up Thagard’s challenge, presenting a belief-revision-like system able to mirror radical types of conceptual change. We will do that with a conceptual revision system, i.e. a belief-revision-like system (...)
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  • Directed recursive labelnode hypergraphs: A new representation-language.Harold Boley - 1977 - Artificial Intelligence 9 (1):49-85.
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  • Troubling Anomalies and Exciting Conjectures: A Bipolar Model of Scientific Discovery.Bruno R. Bocanegra - 2017 - Emotion Review 9 (2).
    A model is proposed to explain how emotional and cognitive processes drive epistemic activities within individual scientists. In this account, emotion–cognition interactions produce cyclical phases of accommodative and assimilative epistemic activities, called thought experiments and empirical experiments, respectively. During thought experiments, scientists ruminate over troubling anomalies in order to generate the theoretical ingredients necessary for constructing new conjectures. During empirical experiments, scientists explore exciting conjectures in order to cover the empirical ground necessary to discover new anomalies. Critically, epistemic activities are (...)
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  • GUS, a frame-driven dialog system.Daniel G. Bobrow, Ronald M. Kaplan, Martin Kay, Donald A. Norman, Henry Thompson & Terry Winograd - 1977 - Artificial Intelligence 8 (2):155-173.
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  • Different structures for concepts of individuals, stuffs, and real kinds: One mama, more milk, and many mice.Paul Bloom - 1998 - Behavioral and Brain Sciences 21 (1):66-67.
    Although our concepts of “Mama,” “milk,” and “mice” have much in common, the suggestion that they are identical in structure in the mind of the prelinguistic child is mistaken. Even infants think about objects as different from substances and appreciate the distinction between kinds (e.g., mice) and individuals (e.g., Mama). Such cognitive capacities exist in other animals as well, and have important adaptive consequences.
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  • Non-resolution theorem proving.W. W. Bledsoe - 1977 - Artificial Intelligence 9 (1):1-35.
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  • Rigor mortis: A response to Nilsson's 'logic and artificial intelligence'.Lawrence Birnbaum - 1991 - Artificial Intelligence 47 (1-3):57-78.
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  • The Systemic Concept of Contextual Truth.Andrzej Bielecki - 2020 - Foundations of Science 26 (4):807-824.
    In this paper the truth is studied in the frame of autonomous systems theory. The method of the truth verification is worked out in its functional aspect. The verification is based on comparison of the predicted inner state of the autonomous agent, that is the cognitive subject, to the achieved inner state of the agent. The state is achieved as the result of performing the action in the real world—the agent’s environment. The action design is created on the basis of (...)
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  • Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Tarek R. Besold, Artur D’Avila Garcez, Keith Stenning, Leendert van der Torre & Michiel van Lambalgen - 2017 - Minds and Machines 27 (1):37-77.
    This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic (...)
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  • The Force-field Puzzle and Mindreading in Non-human Primates.José Luis Bermúdez - 2011 - Review of Philosophy and Psychology 2 (3):397-410.
    What is the relation between philosophical theorizing and experimental data? A modest set of naturalistic assumptions leads to what I term the force-field puzzle. The assumption that philosophy is continuous with natural science, as captured in Quine’s force-field metaphor, seems to push us simultaneously towards thinking that there have to be conceptual constraints upon how we interpret experimental data and towards thinking that there cannot be such conceptual constraints, because all theorizing must be accountable to data and observation. The key (...)
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  • Reliability of retrieving information from knowledge structures in memory: Scripts.Francis S. Bellezza - 1988 - Bulletin of the Psychonomic Society 26 (1):11-14.
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  • Theory Construction in Psychology: The Interpretation and Integration of Psychological Data.Gordon M. Becker - 1981 - Theory and Decision 13 (3):251.
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  • Representations and cognitive explanations: Assessing the dynamicist challenge in cognitive science.William Bechtel - 1998 - Cognitive Science 22 (3):295-317.
    Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...)
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  • Beyond the exclusively propositional era.William P. Bechtel & A. Abrahamson - 1990 - Synthese 82 (2):223-53.
    Contemporary epistemology has assumed that knowledge is represented in sentences or propositions. However, a variety of extensions and alternatives to this view have been proposed in other areas of investigation. We review some of these proposals, focusing on (1) Ryle's notion of knowing how and Hanson's and Kuhn's accounts of theory-laden perception in science; (2) extensions of simple propositional representations in cognitive models and artificial intelligence; (3) the debate concerning imagistic versus propositional representations in cognitive psychology; (4) recent treatments of (...)
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  • The proactive brain: using analogies and associations to generate predictions.Moshe Bar - 2007 - Trends in Cognitive Sciences 11 (7):280-289.
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  • Computing the least common subsumer w.r.t. a background terminology.Franz Baader, Baris Sertkaya & Anni-Yasmin Turhan - 2007 - Journal of Applied Logic 5 (3):392-420.
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  • An overview of tableau algorithms for description logics.Franz Baader & Ulrike Sattler - 2001 - Studia Logica 69 (1):5-40.
    Description logics are a family of knowledge representation formalisms that are descended from semantic networks and frames via the system Kl-one. During the last decade, it has been shown that the important reasoning problems (like subsumption and satisfiability) in a great variety of description logics can be decided using tableau-like algorithms. This is not very surprising since description logics have turned out to be closely related to propositional modal logics and logics of programs (such as propositional dynamic logic), for which (...)
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  • Episodic elaboration: Investigating the structure of retrieved past events and imagined future events.Rachel J. Anderson, Lien Peters & Stephen A. Dewhurst - 2015 - Consciousness and Cognition 33:112-124.
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  • A Structuralist Theory of Belief Revision.Holger Andreas - 2011 - Journal of Logic, Language and Information 20 (2):205-232.
    The present paper aims at a synthesis of belief revision theory with the Sneed formalism known as the structuralist theory of science. This synthesis is brought about by a dynamisation of classical structuralism, with an abductive inference rule and base generated revisions in the style of Rott (2001). The formalism of prioritised default logic (PDL) serves as the medium of the synthesis. Why seek to integrate the Sneed formalism into belief revision theory? With the hybrid system of the present investigation, (...)
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