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  1. How do we know what we are doing? Time, intention and awareness of action.Jean-Christophe Sarrazin, Axel Cleeremans & Patrick Haggard - 2008 - Consciousness and Cognition 17 (3):602-615.
    Time is a fundamental dimension of consciousness. Many studies of the “sense of agency” have investigated whether we attribute actions to ourselves based on a conscious experience of intention occurring prior to action, or based on a reconstruction after the action itself has occurred. Here, we ask the same question about a lower level aspect of action experience, namely awareness of the detailed spatial form of a simple movement. Subjects reached for a target, which unpredictably jumped to the side on (...)
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  • How do we know what we are doing? Time, intention and awareness of action☆.Jean-Christophe Sarrazin, Axel Cleeremans & Patrick Haggard - 2008 - Consciousness and Cognition 17 (3):602-615.
    Time is a fundamental dimension of consciousness. Many studies of the “sense of agency” have investigated whether we attribute actions to ourselves based on a conscious experience of intention occurring prior to action, or based on a reconstruction after the action itself has occurred. Here, we ask the same question about a lower level aspect of action experience, namely awareness of the detailed spatial form of a simple movement. Subjects reached for a target, which unpredictably jumped to the side on (...)
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  • How to Make the Most out of Very Little.Charles Yang - 2020 - Topics in Cognitive Science 12 (1):136-152.
    Yang returns to the problem of referential ambiguity, addressed in the opening paper by Gleitman and Trueswell. Using a computational approach, he argues that “big data” approaches to resolving referential ambiguity are destined to fail, because of the inevitable computational explosion needed to keep track of contextual associations present when a word is uttered. Yang tests several computational models, two of which depend on one‐trial learning, as described in Gleitman and Trueswell’s paper. He concludes that such models outperform cross‐situational learning (...)
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  • A computational model of the temporal dynamics of plasticity in procedural learning: sensitivity to feedback timing.Vivian V. Valentin, W. Todd Maddox & F. Gregory Ashby - 2014 - Frontiers in Psychology 5.
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  • The Pursuit of Word Meanings.Jon Scott Stevens, Lila R. Gleitman, John C. Trueswell & Charles Yang - 2017 - Cognitive Science 41 (S4):638-676.
    We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from (...)
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  • How automatic are crossmodal correspondences?Charles Spence & Ophelia Deroy - 2013 - Consciousness and Cognition 22 (1):245-260.
    The last couple of years have seen a rapid growth of interest in the study of crossmodal correspondences – the tendency for our brains to preferentially associate certain features or dimensions of stimuli across the senses. By now, robust empirical evidence supports the existence of numerous crossmodal correspondences, affecting people’s performance across a wide range of psychological tasks – in everything from the redundant target effect paradigm through to studies of the Implicit Association Test, and from speeded discrimination/classification tasks through (...)
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  • Reward Prediction Error Signals are Meta‐Representational.Nicholas Shea - 2014 - Noûs 48 (2):314-341.
    1. Introduction 2. Reward-Guided Decision Making 3. Content in the Model 4. How to Deflate a Metarepresentational Reading Proust and Carruthers on metacognitive feelings 5. A Deflationary Treatment of RPEs? 5.1 Dispensing with prediction errors 5.2 What is use of the RPE focused on? 5.3 Alternative explanations—worldly correlates 5.4 Contrast cases 6. Conclusion Appendix: Temporal Difference Learning Algorithms.
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  • Modeling the Emergence of Lexicons in Homesign Systems.Russell Richie, Charles Yang & Marie Coppola - 2014 - Topics in Cognitive Science 6 (1):183-195.
    It is largely acknowledged that natural languages emerge not just from human brains but also from rich communities of interacting human brains (Senghas, ). Yet the precise role of such communities and such interaction in the emergence of core properties of language has largely gone uninvestigated in naturally emerging systems, leaving the few existing computational investigations of this issue at an artificial setting. Here, we take a step toward investigating the precise role of community structure in the emergence of linguistic (...)
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  • The Dopamine Prediction Error: Contributions to Associative Models of Reward Learning.Helen M. Nasser, Donna J. Calu, Geoffrey Schoenbaum & Melissa J. Sharpe - 2017 - Frontiers in Psychology 8.
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  • Application of a Prediction Error Theory to Pavlovian Conditioning in an Insect.Makoto Mizunami, Kanta Terao & Beatriz Alvarez - 2018 - Frontiers in Psychology 9.
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  • Object‐Label‐Order Effect When Learning From an Inconsistent Source.Timmy Ma & Natalia L. Komarova - 2019 - Cognitive Science 43 (8):e12737.
    Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, (...)
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  • Elemental representations of stimuli in associative learning.Justin A. Harris - 2006 - Psychological Review 113 (3):584-605.
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  • A new approach to the formulation and testing of learning models.Joseph F. Hanna - 1966 - Synthese 16 (3-4):344 - 380.
    It is argued that current attempts to model human learning behavior commonly fail on one of two counts: either the model assumptions are artificially restricted so as to permit the application of mathematical techniques in deriving their consequences, or else the required complex assumptions are imbedded in computer programs whose technical details obscure the theoretical content of the model. The first failing is characteristic of so-called mathematical models of learning, while the second is characteristic of computer simulation models. An approach (...)
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  • Deep and beautiful. The reward prediction error hypothesis of dopamine.Matteo Colombo - 2014 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 45 (1):57-67.
    According to the reward-prediction error hypothesis of dopamine, the phasic activity of dopaminergic neurons in the midbrain signals a discrepancy between the predicted and currently experienced reward of a particular event. It can be claimed that this hypothesis is deep, elegant and beautiful, representing one of the largest successes of computational neuroscience. This paper examines this claim, making two contributions to existing literature. First, it draws a comprehensive historical account of the main steps that led to the formulation and subsequent (...)
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  • Behavior Stability and Individual Differences in Pavlovian Extended Conditioning.Gianluca Calcagni, Ernesto Caballero-Garrido & Ricardo Pellón - 2020 - Frontiers in Psychology 11.
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  • Effect of Frustration on Brain Activation Pattern in Subjects with Different Temperament.Maria Bierzynska, Maksymilian Bielecki, Artur Marchewka, Weronika Debowska, Anna Duszyk, Wojciech Zajkowski, Marcel Falkiewicz, Anna Nowicka, Jan Strelau & Malgorzata Kossut - 2015 - Frontiers in Psychology 6.
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  • The use of models in experimental psychology.Richard C. Atkinson - 1960 - Synthese 12 (2-3):162 - 171.
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  • Cheap talk, reinforcement learning, and the emergence of cooperation.J. McKenzie Alexander - 2015 - Philosophy of Science 82 (5):969-982.
    Cheap talk has often been thought incapable of supporting the emergence of cooperation because costless signals, easily faked, are unlikely to be reliable (Zahavi and Zahavi, 1997). I show how, in a social network model of cheap talk with reinforcement learning, cheap talk does enable the emergence of cooperation, provided that individuals also temporally discount the past. This establishes one mechanism that suffices for moving a population of initially uncooperative individuals to a state of mutually beneficial cooperation even in the (...)
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  • Models of memory.Jeroen Gw Raaijmakers & Richard M. Shiffrin - 2002 - In J. Wixted & H. Pashler (eds.), Stevens' Handbook of Experimental Psychology. Wiley.
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