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  1. A Symbolic Model of the Nonconscious Acquisition of Information.Charles X. Ling & Marin Marinov - 1994 - Cognitive Science 18 (4):595-621.
    This article presents counter evidence against Smolensky's theory that human intuitive/nonconscious congnitive processes can only be accurately explained in terms of subsymbolic computations carried out in artificial neural networks. We presentsymboliclearning models of two well‐studied, complicated cognitive tasks involving nonconscious acquisition of information: learning production rules and artificial finite state grammars. Our results demonstrate that intuitive learning does not imply subsymbolic computation, and that the already well‐established, perceived correlation between “conscious” and “symbolic” on the one hand, and between “nonconscious” and (...)
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  • Adaptive complexity in sound patterns.Björn Lindblom - 1990 - Behavioral and Brain Sciences 13 (4):743-744.
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  • Language evolved – So what's new?John Limber - 1990 - Behavioral and Brain Sciences 13 (4):742-743.
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  • Not invented here.Philip Lieberman - 1990 - Behavioral and Brain Sciences 13 (4):741-742.
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  • Neuroanatomical structures and segregated circuits.Philip Lieberman - 1996 - Behavioral and Brain Sciences 19 (4):641-641.
    Segregated neural circuits that effect particular domain-specific behaviors can be differentiated from neuroanatomical structures implicated in many different aspects of behavior. The basal ganglionic components of circuits regulating nonlinguistic motor behavior, speech, and syntax all function in a similar manner. Hence, it is unlikely that special properties and evolutionary mechanisms are associated with the neural bases of human language.
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  • Time-delays in conscious processes.Benjamin Libet - 1990 - Behavioral and Brain Sciences 13 (4):672-672.
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  • Unified cognitive theory: Having one's apple pie and eating it.Stephan Lewandowsky - 1992 - Behavioral and Brain Sciences 15 (3):449-450.
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  • How much did the brain have to change for speech?R. C. Lewontin - 1990 - Behavioral and Brain Sciences 13 (4):740-741.
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  • On learnability, empirical foundations, and naturalness.W. J. M. Levelt - 1990 - Behavioral and Brain Sciences 13 (3):501-501.
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  • Connectionism and motivation are compatible.Daniel S. Levine - 1987 - Behavioral and Brain Sciences 10 (3):487-487.
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  • A self-organizing perceptual system.James R. Levenick - 1989 - Behavioral and Brain Sciences 12 (3):409-410.
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  • A nonspatial solution to a spatial problem.Ronald M. Lesperance & Stephen Kaplan - 1989 - Behavioral and Brain Sciences 12 (3):408-409.
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  • Nonmonotonic reasoning by inhibition nets☆☆This paper has been supported by the Austrian Research Fund FWF (SFB F012).Hannes Leitgeb - 2001 - Artificial Intelligence 128 (1-2):161-201.
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  • Evolution, development, and learning in cognitive science.David Leiser - 1990 - Behavioral and Brain Sciences 13 (1):80-81.
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  • Beliefs in conditionals vs. conditional beliefs.Hannes Leitgeb - 2007 - Topoi 26 (1):115-132.
    On the basis of impossibility results on probability, belief revision, and conditionals, it is argued that conditional beliefs differ from beliefs in conditionals qua mental states. Once this is established, it will be pointed out in what sense conditional beliefs are still conditional, even though they may lack conditional contents, and why it is permissible to still regard them as beliefs, although they are not beliefs in conditionals. Along the way, the main logical, dispositional, representational, and normative properties of conditional (...)
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  • Questions For The Dynamicist: The Use of Dynamical Systems Theory in the Philosophy of Cognition.Marco Van Leeuwen - 2005 - Minds and Machines 15 (3):271-333.
    The concepts and powerful mathematical tools of Dynamical Systems Theory (DST) yield illuminating methods of studying cognitive processes, and are even claimed by some to enable us to bridge the notorious explanatory gap separating mind and matter. This article includes an analysis of some of the conceptual and empirical progress Dynamical Systems Theory is claimed to accomodate. While sympathetic to the dynamicist program in principle, this article will attempt to formulate a series of problems the proponents of the approach in (...)
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  • Neurosymbolic Systems of Perception and Cognition: The Role of Attention.Hugo Latapie, Ozkan Kilic, Kristinn R. Thórisson, Pei Wang & Patrick Hammer - 2022 - Frontiers in Psychology 13.
    A cognitive architecture aimed at cumulative learning must provide the necessary information and control structures to allow agents to learn incrementally and autonomously from their experience. This involves managing an agent's goals as well as continuously relating sensory information to these in its perception-cognition information processing stack. The more varied the environment of a learning agent is, the more general and flexible must be these mechanisms to handle a wider variety of relevant patterns, tasks, and goal structures. While many researchers (...)
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  • Generality and applications.Jill H. Larkin - 1987 - Behavioral and Brain Sciences 10 (3):486-487.
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  • A psychologically implausible architecture that is always conscious, always active.Mark Vincent LaPolla & Bernard J. Baars - 1992 - Behavioral and Brain Sciences 15 (3):448-449.
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  • Parallel reasoning in structured connectionist networks: Signatures versus temporal synchrony.Trent E. Lange & Michael G. Dyer - 1996 - Behavioral and Brain Sciences 19 (2):328-331.
    Shastri & Ajjanagadde argue convincingly that both structured connectionist networks and parallel dynamic inferencing are necessary for reflexive reasoning - a kind of inferencing and reasoning that occurs rapidly, spontaneously, and without conscious effort, and which seems necessary for everyday tasks such as natural language understanding. As S&A describe, reflexive reasoning requires a solution to thedynamic binding problem, that is, how to encode systematic and abstract knowledge and instantiate it in specific situations to draw appropriate inferences. Although symbolic artificial intelligence (...)
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  • Equilibrium-point hypothesis, minimum effort control strategy and the triphasic muscle activation pattern.Ning Lan & Patrick E. Crago - 1992 - Behavioral and Brain Sciences 15 (4):769-771.
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  • Distributed cell assemblies and detailed cell models.Anders Lansner & Erik Fransén - 1995 - Behavioral and Brain Sciences 18 (4):637-638.
    Hebbian cell-assembly theory and attractor networks are good starting points for modeling cortical processing. Detailed cell models can be useful in understanding the dynamics of attractor networks. Cell assemblies are likely to be distributed, with the cortical column as the local processing unit. Synaptic memory may be dominant in all but the first couple of seconds.
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  • Approaches to learning and representation.Pat Langley - 1990 - Behavioral and Brain Sciences 13 (3):500-501.
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  • What can psychologists learn from hidden-unit nets?K. Lamberts & G. D'Ydewalle - 1990 - Behavioral and Brain Sciences 13 (3):499-500.
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  • Explanations in AI as Claims of Tacit Knowledge.Nardi Lam - 2022 - Minds and Machines 32 (1):135-158.
    As AI systems become increasingly complex it may become unclear, even to the designer of a system, why exactly a system does what it does. This leads to a lack of trust in AI systems. To solve this, the field of explainable AI has been working on ways to produce explanations of these systems’ behaviors. Many methods in explainable AI, such as LIME, offer only a statistical argument for the validity of their explanations. However, some methods instead study the internal (...)
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  • Impulse Processing: A Dynamical Systems Model of Incremental Eye Movements in the Visual World Paradigm.Anuenue Kukona & Whitney Tabor - 2011 - Cognitive Science 35 (6):1009-1051.
    The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a (...)
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  • The power of explicit knowing.Deanna Kuhn - 1994 - Behavioral and Brain Sciences 17 (4):722-723.
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  • State transitions in constraint satisfaction networks.John K. Kruschke - 1989 - Behavioral and Brain Sciences 12 (3):407-408.
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  • How connectionist models learn: The course of learning in connectionist networks.John K. Kruschke - 1990 - Behavioral and Brain Sciences 13 (3):498-499.
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  • Features and locations: Dichotomy or continuum?Lester E. Krueger & Leann M. Stadtlander - 1989 - Behavioral and Brain Sciences 12 (3):406-407.
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  • An evolutionary perspective on Hebb's reverberatory representations.David C. Krakauer & Alasdair I. Houston - 1995 - Behavioral and Brain Sciences 18 (4):636-637.
    Hebbian mechanisms are justified according to their functional utility in an evolutionary sense. The selective advantage of correlating content-contingent stimuli reflects the putative common cause of temporally or spatially contiguous inputs. The selective consequences of such correlations are discussed by using examples from the evolution of signal form in sexual selection and model-mimic coevolution. We suggest that evolutionary justification might be considered in addition to neurophysiology plansibility when constructing representational models.
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  • The informational basis for nursing intuition: philosophical underpinnings.Judith A. Effken - 2007 - Nursing Philosophy 8 (3):187-200.
    In a previous paper, I argued that expert nursing intuition is a form of what James J. Gibson termed ‘direct perception’ and, as such, is information‐based and can be accepted as part of nursing science. In this paper, I explore the philosophical basis for these claims. I begin by describing analogous problems in philosophy and psychology related to how we know the world. After describing the various solutions proposed and the problems they engender, I summarize Gibson’s theoretical solution together with (...)
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  • Synchronization and cognitive carpentry: From systematic structuring to simple reasoning. E. Koerner - 1993 - Behavioral and Brain Sciences 16 (3):465-466.
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  • Dynamic Cognition Applied to Value Learning in Artificial Intelligence.Nythamar De Oliveira & Nicholas Corrêa - 2021 - Aoristo - International Journal of Phenomenology, Hermeneutics and Metaphysics 4 (2):185-199.
    Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted (...)
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  • Does intentional psychology need vindicating by cognitive science?Jonathan Knowles - 2001 - Minds and Machines 11 (3):347-377.
    I argue that intentional psychology does not stand in need of vindication by a lower-level implementation theory from cognitive science, in particular the representational theory of mind (RTM), as most famously Jerry Fodor has argued. The stance of the paper is novel in that I claim this holds even if one, in line with Fodor, views intentional psychology as an empirical theory, and its theoretical posits as as real as those of other sciences. I consider four metaphysical arguments for the (...)
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  • Lessons from the study of speech perception.Keith R. Kluender - 1990 - Behavioral and Brain Sciences 13 (4):739-740.
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  • The functional meaning of reverberations for sensoric and contextual encoding.Wolfgang Klimesch - 1995 - Behavioral and Brain Sciences 18 (4):636-636.
    Amit argues that the local neuronal spike rate that persists (reverberating) in the absence of the eliciting stimulus represents the code of the eliciting stimulus. Based on the general argument that the inferred functional meaning of reverberation depends in part on the type of representational assumptions, reverberations may only be important for the encoding of contextual information.
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  • Underestimating the importance of the implementational level.Michael Van Kleeck - 1987 - Behavioral and Brain Sciences 10 (3):497-498.
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  • Foundations of AI: The big issues.David Kirsh - 1991 - Artificial Intelligence 47 (1-3):3-30.
    The objective of research in the foundations of Al is to explore such basic questions as: What is a theory in Al? What are the most abstract assumptions underlying the competing visions of intelligence? What are the basic arguments for and against each assumption? In this essay I discuss five foundational issues: (1) Core Al is the study of conceptualization and should begin with knowledge level theories. (2) Cognition can be studied as a disembodied process without solving the symbol grounding (...)
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  • Five exaptations in speech: Reducing the arbitrariness of the constraints on language.John Kingston - 1990 - Behavioral and Brain Sciences 13 (4):738-739.
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  • Flawed Like Us and the Starry Moral Law: Review of Machines Like Me by Ian McEwan. [REVIEW]Tae Wan Kim - 2021 - Journal of Business Ethics 170 (4):875-879.
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  • Parallelism and patterns of thought.R. W. Kentridge - 1990 - Behavioral and Brain Sciences 13 (4):670-671.
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  • Innateness, autonomy, universality, and the neurobiology of regular and irregular inflectional morphology.David Kemmerer - 1996 - Behavioral and Brain Sciences 19 (4):639-641.
    Müller's goal of bringing neuroscience to bear on controversies in linguistics is laudable. However, some of his specific proposals about innateness and autonomy are misguided. Recent studies on the neurobiology of regular and irregular inflectional morphology indicate that these two linguistic processes are subserved by anatomically and physiologically distinct neural subsystems, whose functional organization is likely to be under direct genetic control rather than assembled by strictly epigenetic factors.
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  • Constraints on Constraints: Surveying the Epigenetic Landscape.Frank C. Keil - 1990 - Cognitive Science 14 (1):135-168.
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  • Behavioral systems interpreted as autonomous agents and as coupled dynamical systems: A criticism.Fred A. Keijzer & Sacha Bem - 1996 - Philosophical Psychology 9 (3):323-46.
    Cognitive science's basic premises are under attack. In particular, its focus on internal cognitive processes is a target. Intelligence is increasingly interpreted, not as a matter of reclusive thought, but as successful agent-environment interaction. The critics claim that a major reorientation of the field is necessary. However, this will only occur when there is a distinct alternative conceptual framework to replace the old one. Whether or not a serious alternative is provided is not clear. Among the critics there is some (...)
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  • The Sciences of Complexity and “Origins of Order”.Stuart A. Kauffman - 1990 - PSA Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990 (2):299-322.
    A new science, the science of complexity, is birthing. This science boldly promises to transform the biological and social sciences in the forthcoming century. My own book, Origins of Order: Self Organization and Selection in Evolution, (Kauffman, 1992), is at most one strand in this transformation. I feel deeply honored that Marjorie Grene undertook organizing a session at the Philosophy of Science meeting discussing Origins, and equally glad that Dick Burian, Bob Richardson and Rob Page have undertaken their reading of (...)
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  • Transforming a partially structured brain into a creative mind.Annette Karmiloff-Smith - 1994 - Behavioral and Brain Sciences 17 (4):732-745.
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  • Précis of Beyond modularity: A developmental perspective on cognitive science.Annette Karmiloff-Smith - 1994 - Behavioral and Brain Sciences 17 (4):693-707.
    Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...)
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  • Perception and Cognition: Essays in the Philosophy of Psychology.David Michael Kaplan - 2012 - Philosophical Psychology 25 (3):463-468.
    Philosophical Psychology, Volume 0, Issue 0, Page 1-6, Ahead of Print.
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  • Explanation and description in computational neuroscience.David Michael Kaplan - 2011 - Synthese 183 (3):339-373.
    The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...)
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