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  1. Implicit learning: News from the front.Axel Cleeremans, Arnaud Destrebecqz & Maud Boyer - 1998 - Trends in Cognitive Sciences 2 (10):406-416.
    69 Thompson-Schill, S.L. _et al. _(1997) Role of left inferior prefrontal cortex 59 Buckner, R.L. _et al. _(1996) Functional anatomic studies of memory in retrieval of semantic knowledge: a re-evaluation _Proc. Natl. Acad._ retrieval for auditory words and pictures _J. Neurosci. _16, 6219–6235 _Sci. U. S. A. _94, 14792–14797 60 Buckner, R.L. _et al. _(1995) Functional anatomical studies of explicit and 70 Baddeley, A. (1992) Working memory: the interface between memory implicit memory retrieval tasks _J. Neurosci. _15, 12–29 and cognition (...)
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  • The cognizer's innards: A psychological and philosophical perspective on the development of thought.Andy Clark & Annette Karmiloff-Smith - 1993 - Mind and Language 8 (4):487-519.
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  • A Study of Thinking.Jerome S. Bruner, Jacqueline J. Goodnow & George A. Austin - 1958 - Philosophy and Phenomenological Research 19 (1):118-119.
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  • The theory of learning by doing.Yuichiro Anzai & Herbert A. Simon - 1979 - Psychological Review 86 (2):124-140.
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  • Memory for goals: an activation‐based model.Erik M. Altmann & J. Gregory Trafton - 2002 - Cognitive Science 26 (1):39-83.
    Goal‐directed cognition is often discussed in terms of specialized memory structures like the “goal stack.” The goal‐activation model presented here analyzes goal‐directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in (...)
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  • The Cognizer's Innards: A Psychological and Philosophical Perspective on the Development of Thought.Andy Clark & Annette Karmiloff-Smith - 1991 - School of Cognitive and Computing Sciences, University of Sussex.
    We show that a popular class of connectionist models (which we label 'first order connectionism') looks unlikely to provide the kind of resources required by the hypothesis. We examine some alternative hybrid models that seem more promising. Finally, we raise a more purely philosophical issue concerning the conditions under which a being can count as a genuine believer or cognizer.".
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  • Representations in distributed cognitive tasks.Jianhui Zhang & Donald A. Norman - 1994 - Cognitive Science 18 (1):87-122.
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  • Representations in Distributed Cognitive Tasks.Jiaje Zhang & Donald A. Norman - 1994 - Cognitive Science 18 (1):87-122.
    In this article we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasks—tasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations Is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose (...)
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  • Robust reasoning: integrating rule-based and similarity-based reasoning.Ron Sun - 1995 - Artificial Intelligence 75 (2):241-295.
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  • Accounting for the computational basis of consciousness: A connectionist approach.Ron Sun - 1999 - Consciousness and Cognition 8 (4):529-565.
    This paper argues for an explanation of the mechanistic (computational) basis of consciousness that is based on the distinction between localist (symbolic) representation and distributed representation, the ideas of which have been put forth in the connectionist literature. A model is developed to substantiate and test this approach. The paper also explores the issue of the functional roles of consciousness, in relation to the proposed mechanistic explanation of consciousness. The model, embodying the representational difference, is able to account for the (...)
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  • On the proper treatment of connectionism.Paul Smolensky - 1988 - Behavioral and Brain Sciences 11 (1):1-23.
    A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...)
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  • Perceptual manifestations of an analytic structure: The priority of holistic individuation.Glenn Regehr & Lee R. Brooks - 1993 - Journal of Experimental Psychology: General 122 (1):92.
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  • Implicit learning and tacit knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology: General 118 (3):219-235.
    I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of (...)
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  • Analogic and abstraction strategies in synthetic grammar learning: A functionalist interpretation.Arthur S. Reber & Rhianon Allen - 1978 - Cognition 6 (3):189-221.
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  • Rule-plus-exception model of classification learning.Robert M. Nosofsky, Thomas J. Palmeri & Stephen C. McKinley - 1994 - Psychological Review 101 (1):53-79.
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  • From implicit skills to explicit knowledge: a bottom‐up model of skill learning.Edward Merrillb & Todd Petersonb - 2001 - Cognitive Science 25 (2):203-244.
    This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...)
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  • Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory.James L. McClelland, Bruce L. McNaughton & Randall C. O'Reilly - 1995 - Psychological Review 102 (3):419-457.
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  • How to build a baby: II. Conceptual primitives.Jean M. Mandler - 1992 - Psychological Review 99 (4):587-604.
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  • From meta-processes to conscious access: Evidence from children's metalinguistic and repair data.Annette Karmiloff-Smith - 1986 - Cognition 23 (2):95-147.
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  • Unified model of attention and problem solving.Earl Hunt & Marcy Lansman - 1986 - Psychological Review 93 (4):446-461.
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  • A study of the effects of verbalization on problem solving.Robert M. Gagné & Ernest C. Smith - 1962 - Journal of Experimental Psychology 63 (1):12.
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  • Connectionist and Memory‐Array Models of Artificial Grammar Learning.Zoltan Dienes - 1992 - Cognitive Science 16 (1):41-79.
    Subjects exposed to strings of letters generated by a finite state grammar can later classify grammatical and nongrammatical test strings, even though they cannot adequately say what the rules of the grammar are (e.g., Reber, 1989). The MINERVA 2 (Hintzman, 1986) and Medin and Schaffer (1978) memory‐array models and a number of connectionist outoassociator models are tested against experimental data by deriving mainly parameter‐free predictions from the models of the rank order of classification difficulty of test strings. The importance of (...)
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  • Inductive Logic Programming: Techniques and Applications.Nada Lavrač & Sašo Džeroski - 1994 - Ellis Horwood.
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  • Unified theories of cognition.Allen Newell - 1990 - Cambridge, Mass.: Harvard University Press.
    In this book, Newell makes the case for unified theories by setting forth a candidate.
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  • Handbook of Implicit Learning.Michael A. Stadler & Peter A. Frensch - 1998 - Sage Publications.
    Research on implicit learning - a cognitive phenomenon in which people acquire knowledge without conscious intent or awareness - has been growing exponentially. This volume draws together this research, offering the first complete reference on implicit learning by those who have been instrumental in shaping the field. The contributors explore controversies in the field, and examine: functional characteristics, brain mechanisms and neurological foundations of implicit learning; connectionist models; and applications of implicit learning to acquiring new mental skills.
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  • Autonomous Learning of Sequential Tasks: Experiments and Analyses.Todd Peterson - unknown
    This paper presents a novel learning model Clarion , which is a hybrid model based on the two-level approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and (...)
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  • A bottom-up model of skill learning.Ron Sun, Todd Peterson & Edward Merrill - unknown
    We present a skill learning model CLARION. Different from existing models of high-level skill learning that use a topdown approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. CLAR- ION is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a minefield navigation task. A match between the model and (...)
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  • Attention and awareness in sequence learning.Axel Cleeremans - forthcoming - Proceedings of the Fiftheenth Annual Conference of the Cognitive Science Society:227-232.
    referred to as implicit learning (Reber, 1989). Implicit learning contrasts with explicit learning (exhibited for.
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  • Learning, action, and consciousness: A hybrid approach toward modeling consciousness.Ron Sun - 1997 - Neural Networks 10:1317-33.
    _role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the model. Comparisons with (...)
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  • Duality of the mind.Ron Sun - manuscript
    Synthesizing situated cognition, reinforcement learning, and hybrid connectionist modeling, a generic cognitive architecture focused on situated involvement and interaction with the world is developed in this book. The architecture notably incorporates the distinction of implicit and explicit processes.
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  • Implicit memory: History and current status.Daniel L. Schacter - 1987 - Journal of Experimental Psychology 13 (3):501-18.
    Je lui ai associÉ un court extrait d'une revue de questions portant sur le même thème. Implicit memory is revealed when previous experiences facilitate perf on a task that does not require conscious or intentional recollection of those expces. Explicit memory is revealed when perf on a task requires conscious recolelction of previous expces. Il s'agit de defs descriptives qui n'impliquent pas l'existence de deux systs de mÉmo sÉparÉs. Historiquement Descartes est le premier ˆ faire mention de phÉnomènes de mÉmo (...)
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  • Implicit learning and tacit knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology 118:219-35.
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  • Conscious and nonconscious aspects of memory: A neuropsychological framework of modules and central systems.Morris Moscovitch & Carlo Umilta - 1991 - In R Lister & H. Weingartner (eds.), Perspectives on Cognitive Neuroscience. Oxford University Press.
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