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  1. Two Neurocomputational Building Blocks of Social Norm Compliance.Matteo Colombo - 2014 - Biology and Philosophy 29 (1):71-88.
    Current explanatory frameworks for social norms pay little attention to why and how brains might carry out computational functions that generate norm compliance behavior. This paper expands on existing literature by laying out the beginnings of a neurocomputational framework for social norms and social cognition, which can be the basis for advancing our understanding of the nature and mechanisms of social norms. Two neurocomputational building blocks are identified that might constitute the core of the mechanism of norm compliance. They consist (...)
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  • Spontaneous Task Structure Formation Results in a Cost to Incidental Memory of Task Stimuli.Christina Bejjani & Tobias Egner - 2019 - Frontiers in Psychology 10.
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  • Visual Sexual Stimuli—Cue or Reward? A Perspective for Interpreting Brain Imaging Findings on Human Sexual Behaviors.Mateusz Gola, Małgorzata Wordecha, Artur Marchewka & Guillaume Sescousse - 2016 - Frontiers in Human Neuroscience 10.
  • Toward a Unified Sub-Symbolic Computational Theory of Cognition.Martin V. Butz - 2016 - Frontiers in Psychology 7.
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  • Constructing the Context Through Goals and Schemata: Top-Down Processes in Comprehension and Beyond.Marco Mazzone - 2015 - Frontiers in Psychology 6.
    My main purpose here is to provide an account of context selection in utterance understanding in terms of the role played by schemata and goals in top-down processing. The general idea is that information is organized hierarchically, with items iteratively organized in chunks—here called “schemata”—at multiple levels, so that the activation of any items spreads to schemata that are the most accessible due to previous experience. The activation of a schema, in turn, activates its other components, so as to predict (...)
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  • Novelty and Inductive Generalization in Human Reinforcement Learning.Samuel J. Gershman & Yael Niv - 2015 - Topics in Cognitive Science 7 (3):391-415.
    In reinforcement learning, a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and (...)
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  • The Goal Circuit Model: A Hierarchical Multi‐Route Model of the Acquisition and Control of Routine Sequential Action in Humans.Richard P. Cooper, Nicolas Ruh & Denis Mareschal - 2014 - Cognitive Science 38 (2):244-274.
    Human control of action in routine situations involves a flexible interplay between (a) task-dependent serial ordering constraints; (b) top-down, or intentional, control processes; and (c) bottom-up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top-down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal-directed action (...)
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  • Am I Self-Conscious?Karl Friston - 2018 - Frontiers in Psychology 9.
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  • Integrating Computation Into the Mechanistic Hierarchy in the Cognitive and Neural Sciences.Lotem Elber-Dorozko & Oron Shagrir - forthcoming - Synthese:1-24.
    It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...)
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  • A Goal-Directed Bayesian Framework for Categorization.Francesco Rigoli, Giovanni Pezzulo, Raymond Dolan & Karl Friston - 2017 - Frontiers in Psychology 8.
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  • An Empirical Solution to the Puzzle of Weakness of Will.Julia Haas - 2018 - Synthese (12):1-21.
    This paper presents an empirical solution to the puzzle of weakness of will. Specifically, it presents a theory of action, grounded in contemporary cognitive neuroscientific accounts of decision making, that explains the phenomenon of weakness of will without resulting in a puzzle.
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  • The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers.Matthew M. Botvinick - 2014 - Cognitive Science 38 (6):1249-1285.
    Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive (...)
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  • Individual Differences in the Simon Effect Are Underpinned by Differences in the Competitive Dynamics in the Basal Ganglia: An Experimental Verification and a Computational Model.Andrea Stocco, Nicole L. Murray, Brianna L. Yamasaki, Taylor J. Renno, Jimmy Nguyen & Chantel S. Prat - 2017 - Cognition 164:31-45.
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  • Mario Becomes Cognitive.Fabian Schrodt, Jan Kneissler, Stephan Ehrenfeld & Martin V. Butz - 2017 - Topics in Cognitive Science 9 (2):343-373.
    In line with Allen Newell's challenge to develop complete cognitive architectures, and motivated by a recent proposal for a unifying subsymbolic computational theory of cognition, we introduce the cognitive control architecture SEMLINCS. SEMLINCS models the development of an embodied cognitive agent that learns discrete production rule-like structures from its own, autonomously gathered, continuous sensorimotor experiences. Moreover, the agent uses the developing knowledge to plan and control environmental interactions in a versatile, goal-directed, and self-motivated manner. Thus, in contrast to several well-known (...)
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  • The Mental Representation of Human Action.Sydney Levine, Alan M. Leslie & John Mikhail - 2018 - Cognitive Science 42 (4):1229-1264.
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  • Context-Dependence and Context-Invariance in the Neural Coding of Intentional Action.David Wisniewski - 2018 - Frontiers in Psychology 9.
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  • Learning and Transfer of Working Memory Gating Policies.Apoorva Bhandari & David Badre - 2018 - Cognition 172:89-100.
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  • When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition.Christian P. Janssen & Wayne D. Gray - 2012 - Cognitive Science 36 (2):333-358.
    Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). In this article, (...)
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