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  1. Learning Multiple Layers of Representation.Geoffrey E. Hinton - 2007 - Trends in Cognitive Sciences 11 (10):428-434.
  • Are Developmental Disorders Like Cases of Adult Brain Damage? Implications From Connectionist Modelling.Michael Thomas & Annette Karmiloff-Smith - 2002 - Behavioral and Brain Sciences 25 (6):727-750.
    It is often assumed that similar domain-specific behavioural impairments found in cases of adult brain damage and developmental disorders correspond to similar underlying causes, and can serve as convergent evidence for the modular structure of the normal adult cognitive system. We argue that this correspondence is contingent on an unsupported assumption that atypical development can produce selective deficits while the rest of the system develops normally (Residual Normality), and that this assumption tends to bias data collection in the field. Based (...)
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  • The Leabra Architecture: Specialization Without Modularity.Alexander A. Petrov, David J. Jilk, Randall C. O'Reilly & Michael L. Anderson - 2010 - Behavioral and Brain Sciences 33 (4):286.
    The posterior cortex, hippocampus, and prefrontal cortex in the Leabra architecture are specialized in terms of various neural parameters, and thus are predilections for learning and processing, but domain-general in terms of cognitive functions such as face recognition. Also, these areas are not encapsulated and violate Fodorian criteria for modularity. Anderson's terminology obscures these important points, but we applaud his overall message.
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  • Neural Reuse: A Fundamental Organizational Principle of the Brain.Michael L. Anderson - 2010 - Behavioral and Brain Sciences 33 (4):245.
    An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role of neural plasticity (...)
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  • Modeling Language and Cognition with Deep Unsupervised Learning: A Tutorial Overview.Marco Zorzi, Alberto Testolin & Ivilin P. Stoianov - 2013 - Frontiers in Psychology 4.
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  • Recurrent Processing During Object Recognition.Randall C. O’Reilly, Dean Wyatte, Seth Herd, Brian Mingus & David J. Jilk - 2013 - Frontiers in Psychology 4.
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  • Speechreading: Illusion or Window Into Pattern Recognition.Dominic W. Massaro - 1999 - Trends in Cognitive Sciences 3 (8):310-317.
  • Cognitive Theory Development as We Know It: Specificity, Explanatory Power, and the Brain.Davide Crepaldi & Simona Amenta - 2013 - Frontiers in Psychology 4.
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  • Complementary Learning Systems.Randall C. O.’Reilly, Rajan Bhattacharyya, Michael D. Howard & Nicholas Ketz - 2014 - Cognitive Science 38 (6):1229-1248.
    This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O’Reilly (1995). This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern-separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). We review the application (...)
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  • Multiscale Modeling of Gene–Behavior Associations in an Artificial Neural Network Model of Cognitive Development.Michael S. C. Thomas, Neil A. Forrester & Angelica Ronald - 2016 - Cognitive Science 40 (1):51-99.
    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...)
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  • Learning Representations in a Gated Prefrontal Cortex Model of Dynamic Task Switching.Nicolas P. Rougier & Randall C. O'Reilly - 2002 - Cognitive Science 26 (4):503-520.
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  • Learning Orthographic Structure With Sequential Generative Neural Networks.Alberto Testolin, Ivilin Stoianov, Alessandro Sperduti & Marco Zorzi - 2016 - Cognitive Science 40 (3):579-606.
    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual (...)
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  • Automatic Generation of Cognitive Theories Using Genetic Programming.Enrique Frias-Martinez & Fernand Gobet - 2007 - Minds and Machines 17 (3):287-309.
    Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental (...)
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  • Modeling Hippocampal and Neocortical Contributions to Recognition Memory: A Complementary-Learning-Systems Approach.Kenneth A. Norman & Randall C. O'Reilly - 2003 - Psychological Review 110 (4):611-646.
  • Persistence and Accommodation in Short‐Term Priming and Other Perceptual Paradigms: Temporal Segregation Through Synaptic Depression.David E. Huber & Randall C. O'Reilly - 2003 - Cognitive Science 27 (3):403-430.
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  • Against the “System” Module.John Zerilli - 2017 - Philosophical Psychology 30 (3):231-246.
    Modularity is a fundamental doctrine in the cognitive sciences. It holds a preeminent position in cognitive psychology and generative linguistics, as well as a long history in neurophysiology, with roots going all the way back to the early nineteenth century. But a mature field of neuroscience is a comparatively recent phenomenon and has challenged orthodox conceptions of the modular mind. One way of accommodating modularity within the new framework suggested by these developments is to go for increasingly soft versions of (...)
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  • Anatomy of a Decision: Striato-Orbitofrontal Interactions in Reinforcement Learning, Decision Making, and Reversal.Michael J. Frank & Eric D. Claus - 2006 - Psychological Review 113 (2):300-326.
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  • Mechanisms for Robust Cognition.Matthew M. Walsh & Kevin A. Gluck - 2015 - Cognitive Science 39 (6):1131-1171.
    To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness (...)
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  • Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon.Sami Virpioja, Minna Lehtonen, Annika Hultén, Henna Kivikari, Riitta Salmelin & Krista Lagus - 2018 - Cognitive Science 42 (3):939-973.
    Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole-word model, or a (...)
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  • Emotion, Cognition, and the Classical Elements of Mind.William A. Cunningham & Tabitha Kirkland - 2012 - Emotion Review 4 (4):369-370.
    The scientific study of emotion faces a potentially serious problem: after over a hundred years of psychological study, we lack consensus regarding the very definition of emotion. We propose that part of the problem may be the tendency to define emotion in contrast to cognition, rather than viewing both “emotion” and “cognition” as being comprised of more elemental processes. We argue that considering emotion as a type of cognition (viewed broadly as information processing) may provide an understanding of the mechanisms (...)
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  • Developing PFC Representations Using Reinforcement Learning.Jeremy R. Reynolds & Randall C. O’Reilly - 2009 - Cognition 113 (3):281-292.
  • Flexible Cognitive Resources: Competitive Content Maps for Attention and Memory.Steven L. Franconeri, George A. Alvarez & Patrick Cavanagh - 2013 - Trends in Cognitive Sciences 17 (3):134-141.
  • Conjunctive Representations in Learning and Memory: Principles of Cortical and Hippocampal Function.Randall C. O'Reilly & Jerry W. Rudy - 2001 - Psychological Review 108 (2):311-345.
  • Persistence and Accommodation in Short-Term Priming and Other Perceptual Paradigms: Temporal Segregation Through Synaptic Depression.David E. Huber & Randall C. O'Reilly - 2003 - Cognitive Science 27 (3):403-430.
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