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Finding Structure in Time

Cognitive Science 14 (2):179-211 (1990)

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  1. 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.
  • Awareness and Abstraction Are Graded Dimensions.Axel Cleeremans - 1994 - Behavioral and Brain Sciences 17 (3):402-403.
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  • The Construction of 'Reality' in the Robot: Constructivist Perspectives on Situated Artificial Intelligence and Adaptive Robotics. [REVIEW]Tom Ziemke - 2001 - Foundations of Science 6 (1-3):163-233.
    This paper discusses different approaches incognitive science and artificial intelligenceresearch from the perspective of radicalconstructivism, addressing especially theirrelation to the biologically based theories ofvon Uexküll, Piaget as well as Maturana andVarela. In particular recent work in New AI and adaptive robotics on situated and embodiedintelligence is examined, and we discuss indetail the role of constructive processes asthe basis of situatedness in both robots andliving organisms.
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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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  • Principles for Implicit Learning.Axel Cleeremans - 1997 - In Dianne C. Berry (ed.), How Implicit is Implicit Learning? Oxford University Press.
    Complete URL to this document: http://srsc.ulb.ac.be/axcWWW/93-Principles.html.
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  • High-Level Perception, Representation, and Analogy:A Critique of Artificial Intelligence Methodology.David J. Chalmers, Robert M. French & Douglas R. Hofstadter - 1992 - Journal of Experimental and Theoretical Artificial Intellige 4 (3):185 - 211.
    High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...)
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  • What Levels of Explanation in the Behavioural Sciences?Giuseppe Boccignone & Roberto Cordeschi (eds.) - 2015 - Frontiers Media SA.
    Complex systems are to be seen as typically having multiple levels of organization. For instance, in the behavioural and cognitive sciences, there has been a long lasting trend, promoted by the seminal work of David Marr, putting focus on three distinct levels of analysis: the computational level, accounting for the What and Why issues, the algorithmic and the implementational levels specifying the How problem. However, the tremendous developments in neuroscience knowledge about processes at different scales of organization together with the (...)
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  • Interactive Effects of Explicit Emergent Structure: A Major Challenge for Cognitive Computational Modeling.Robert M. French & Elizabeth Thomas - 2015 - Topics in Cognitive Science 7 (2):206-216.
    David Marr's three-level analysis of computational cognition argues for three distinct levels of cognitive information processing—namely, the computational, representational, and implementational levels. But Marr's levels are—and were meant to be—descriptive, rather than interactive and dynamic. For this reason, we suggest that, had Marr been writing today, he might well have gone even farther in his analysis, including the emergence of structure—in particular, explicit structure at the conceptual level—from lower levels, and the effect of explicit emergent structures on the level that (...)
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  • Acquiring Complex Communicative Systems: Statistical Learning of Language and Emotion.Ashley L. Ruba, Seth D. Pollak & Jenny R. Saffran - 2022 - Topics in Cognitive Science 14 (3):432-450.
    Topics in Cognitive Science, Volume 14, Issue 3, Page 432-450, July 2022.
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  • On Being Systematically Connectionist.L. F. Niklasson & Tim van Gelder - 1994 - Mind and Language 9 (3):288-30.
    In 1988 Fodor and Pylyshyn issued a challenge to the newly-popular connectionism: explain the systematicity of cognition without merely implementing a so-called classical architecture. Since that time quite a number of connectionist models have been put forward, either by their designers or by others, as in some measure demonstrating that the challenge can be met (e.g., Pollack, 1988, 1990; Smolensky, 1990; Chalmers, 1990; Niklasson and Sharkey, 1992; Brousse, 1993). Unfortu- nately, it has generally been unclear whether these models actually do (...)
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  • Auditory Expectation: The Information Dynamics of Music Perception and Cognition.Marcus T. Pearce & Geraint A. Wiggins - 2012 - Topics in Cognitive Science 4 (4):625-652.
    Following in a psychological and musicological tradition beginning with Leonard Meyer, and continuing through David Huron, we present a functional, cognitive account of the phenomenon of expectation in music, grounded in computational, probabilistic modeling. We summarize a range of evidence for this approach, from psychology, neuroscience, musicology, linguistics, and creativity studies, and argue that simulating expectation is an important part of understanding a broad range of human faculties, in music and beyond.
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  • Surprisal-Based Comparison Between a Symbolic and a Connectionist Model of Sentence Processing.Stefan L. Frank - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 1139--1144.
  • Content and Its Vehicles in Connectionist Systems.Nicholas Shea - 2007 - Mind and Language 22 (3):246–269.
    This paper advocates explicitness about the type of entity to be considered as content- bearing in connectionist systems; it makes a positive proposal about how vehicles of content should be individuated; and it deploys that proposal to argue in favour of representation in connectionist systems. The proposal is that the vehicles of content in some connectionist systems are clusters in the state space of a hidden layer. Attributing content to such vehicles is required to vindicate the standard explanation for some (...)
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  • Characteristics of Dissociable Human Learning Systems.David R. Shanks & Mark F. St John - 1994 - Behavioral and Brain Sciences 17 (3):367-447.
    A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning (...)
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  • Inside Doubt: On the Non-Identity of the Theory of Mind and Propositional Attitude Psychology. [REVIEW]David Landy - 2005 - Minds and Machines 15 (3-4):399-414.
    Eliminative materialism is a popular view of the mind which holds that propositional attitudes, the typical units of our traditional understanding, are unsupported by modern connectionist psychology and neuroscience, and consequently that propositional attitudes are a poor scientific postulate, and do not exist. Since our traditional folk psychology employs propositional attitudes, the usual argument runs, it too represents a poor theory, and may in the future be replaced by a more successful neurologically grounded theory, resulting in a drastic improvement in (...)
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  • Characteristics of Dissociable Human Learning Systems.David R. Shanks & Mark F. St John - 1994 - Behavioral and Brain Sciences 17 (3):367-395.
    A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, between learning that takes place with versus without concurrent awareness, and between learning that involves the encoding of instances versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, (...)
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  • A Unifying Perspective on Perception and Cognition Through Linguistic Representations of Emotion.Prakash Mondal - 2022 - Frontiers in Psychology 13.
    This article will provide a unifying perspective on perception and cognition via the route of linguistic representations of emotion. Linguistic representations of emotions provide a fertile ground for explorations into the nature and form of integration of perception and cognition because emotion has facets of both perceptual and cognitive processes. In particular, this article shows that certain types of linguistic representations of emotion allow for the integration of perception and cognition through a series of steps and operations in cognitive systems, (...)
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  • Quasiregularity and Its Discontents: The Legacy of the Past Tense Debate.Mark S. Seidenberg & David C. Plaut - 2014 - Cognitive Science 38 (6):1190-1228.
    Rumelhart and McClelland's chapter about learning the past tense created a degree of controversy extraordinary even in the adversarial culture of modern science. It also stimulated a vast amount of research that advanced the understanding of the past tense, inflectional morphology in English and other languages, the nature of linguistic representations, relations between language and other phenomena such as reading and object recognition, the properties of artificial neural networks, and other topics. We examine the impact of the Rumelhart and McClelland (...)
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  • Out of Control: An Associative Account of Congruency Effects in Sequence Learning.Tom Beesley, Fergal W. Jones & David R. Shanks - 2012 - Consciousness and Cognition 21 (1):413-421.
    The demonstration of a sequential congruency effect in sequence learning has been offered as evidence for control processes that act to inhibit automatic response tendencies via unconscious conflict monitoring. Here we propose an alternative interpretation of this effect based on the associative learning of chains of sequenced contingencies. This account is supported by simulations with a Simple Recurrent Network, an associative model of sequence learning. We argue that the control- and associative-based accounts differ in their predictions concerning the magnitude of (...)
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  • Implications of Neural Networks for How We Think About Brain Function.David A. Robinson - 1992 - Behavioral and Brain Sciences 15 (4):644-655.
    Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other (...)
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  • Editors' Review and Forthcoming Topic Event‐Predictive Cognition: From Sensorimotor Via Conceptual to Language‐Based Structures and Processes.Martin V. Butz, Asya Achimova, David Bilkey & Alistair Knott - forthcoming - Topics in Cognitive Science.
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  • An Alternative View of the Mental Lexicon.Jeffrey Elman L. - 2004 - Trends in Cognitive Sciences 8 (7):301-306.
    An essential aspect of knowing language is knowing the words of that language. This knowledge is usually thought to reside in the mental lexicon, a kind of dictionary that contains information regarding a word’s meaning, pronunciation, syntactic characteristics, and so on. In this article, a very different view is presented. In this view, words are understood as stimuli that operate directly on mental states. The phonological, syntactic and semantic properties of a word are revealed by the effects it has on (...)
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  • A Critical Review of Network‐Based and Distributional Approaches to Semantic Memory Structure and Processes.Abhilasha A. Kumar, Mark Steyvers & David A. Balota - 2022 - Wiley: Topics in Cognitive Science 14 (1):54-77.
    Topics in Cognitive Science, Volume 14, Issue 1, Page 54-77, January 2022.
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  • Event‐Predictive Cognition: A Root for Conceptual Human Thought.Martin V. Butz, Asya Achimova, David Bilkey & Alistair Knott - 2021 - Topics in Cognitive Science 13 (1):10-24.
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  • How Variability Shapes Learning and Generalization.Limor Raviv, Gary Lupyan & Shawn C. Green - forthcoming - Trends in Cognitive Sciences.
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  • What Does It Mean to Claim That Something Is 'Innate'? Response to Clark, Harris, Lightfoot and Samuels.Annette Karmiloff-Smith, Kim Plunkett & Mark H. Johnson - 1998 - Mind and Language 13 (4):588-597.
  • Connectionist Natural Language Processing: The State of the Art.Morten H. Christiansen & Nick Chater - 1999 - Cognitive Science 23 (4):417-437.
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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.
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  • Learning the Generative Principles of a Symbol System From Limited Examples.Lei Yuan, Violet Xiang, David Crandall & Linda Smith - 2020 - Cognition 200:104243.
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  • Large-Scale Modeling of Wordform Learning and Representation.Daragh E. Sibley, Christopher T. Kello, David C. Plaut & Jeffrey L. Elman - 2008 - Cognitive Science 32 (4):741-754.
  • Learning to Talk About Events From Narrated Video in a Construction Grammar Framework.Dominey Peter Ford & Jean-David Boucher - 2005 - Artificial Intelligence 167 (1-2):31-61.
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  • Doing Without Schema Hierarchies: A Recurrent Connectionist Approach to Normal and Impaired Routine Sequential Action.Matthew Botvinick & David C. Plaut - 2004 - Psychological Review 111 (2):395-429.
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  • Short-Term Memory for Serial Order: A Recurrent Neural Network Model.Matthew M. Botvinick & David C. Plaut - 2006 - Psychological Review 113 (2):201-233.
  • Language Acquisition in the Absence of Explicit Negative Evidence: How Important is Starting Small?Douglas L. T. Rohde & David C. Plaut - 1999 - Cognition 72 (1):67-109.
  • Neuropsychological Dissociations Between Priming and Recognition: A Single-System Connectionist Account.Annette Kinder & David R. Shanks - 2003 - Psychological Review 110 (4):728-744.
  • Learning Representations of Wordforms.Daragh E. Sibley, Christopher T. Kello, David C. Plaut & Jeffrey L. Elman - 2008 - Cognitive Science 32 (4):741-754.
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  • A Connectionist Approach to Word Reading and Acquired Dyslexia: Extension to Sequential Processing.David C. Plaut - 1999 - Cognitive Science 23 (4):543-568.
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  • Introduction to the Issue on Computational Models of Natural Language.John Hale & David Reitter - 2013 - Topics in Cognitive Science 5 (3):388-391.
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  • Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on Performance.Nicolas Riesterer, Daniel Brand & Marco Ragni - 2020 - Topics in Cognitive Science 12 (3):960-974.
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  • Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning.Willem Zuidema, Robert M. French, Raquel G. Alhama, Kevin Ellis, Timothy J. O'Donnell, Tim Sainburg & Timothy Q. Gentner - 2020 - Topics in Cognitive Science 12 (3):925-941.
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  • Learning Higher‐Order Transitional Probabilities in Nonhuman Primates.Arnaud Rey, Joël Fagot, Fabien Mathy, Laura Lazartigues, Laure Tosatto, Guillem Bonafos, Jean-Marc Freyermuth & Frédéric Lavigne - 2022 - Wiley: Cognitive Science 46 (4):e13121.
    Cognitive Science, Volume 46, Issue 4, April 2022.
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  • Knowledge-Augmented Face Perception: Prospects for the Bayesian Brain-Framework to Align AI and Human Vision.Martin Maier, Florian Blume, Pia Bideau, Olaf Hellwich & Rasha Abdel Rahman - 2022 - Consciousness and Cognition 101:103301.
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  • Prediction‐Based Learning and Processing of Event Knowledge.Ken McRae, Kevin S. Brown & Jeffrey L. Elman - 2021 - Topics in Cognitive Science 13 (1):206-223.
    Topics in Cognitive Science, Volume 13, Issue 1, Page 206-223, January 2021.
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  • The Effects of Feature-Label-Order and Their Implications for Symbolic Learning.Michael Ramscar, Daniel Yarlett, Melody Dye, Katie Denny & Kirsten Thorpe - 2010 - Cognitive Science 34 (6):909-957.
    Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a (...)
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  • Uncovering the Richness of the Stimulus: Structure Dependence and Indirect Statistical Evidence.Florencia Reali & Morten H. Christiansen - 2005 - Cognitive Science 29 (6):1007-1028.
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  • An Activation‐Based Model of Sentence Processing as Skilled Memory Retrieval.Richard L. Lewis & Shravan Vasishth - 2005 - Cognitive Science 29 (3):375-419.
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  • Developmental Motifs Reveal Complex Structure in Cell Lineages.Nicholas Geard, Seth Bullock, Rolf Lohaus, Ricardo B. R. Azevedo & Janet Wiles - 2011 - Complexity 16 (4):48-57.
    Many natural and technological systems are complex, with organizational structures that exhibit characteristic patterns but defy concise description. One effective approach to analyzing such systems is in terms of repeated topological motifs. Here, we extend the motif concept to characterize the dynamic behavior of complex systems by introducing developmental motifs, which capture patterns of system growth. As a proof of concept, we use developmental motifs to analyze the developmental cell lineage of the nematode Caenorhabditis elegans, revealing a new perspective on (...)
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  • A Computational Model of Event Segmentation From Perceptual Prediction.Jeremy R. Reynolds, Jeffrey M. Zacks & Todd S. Braver - 2007 - Cognitive Science 31 (4):613-643.
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  • Connectionist Models of Language Production: Lexical Access and Grammatical Encoding.Gary S. Dell, Franklin Chang & Zenzi M. Griffin - 1999 - Cognitive Science 23 (4):517-542.
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  • Distributional Information: A Powerful Cue for Acquiring Syntactic Categories.Martin Redington, Nick Chater & Steven Finch - 1998 - Cognitive Science 22 (4):425-469.
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