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  1. 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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  • 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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  • Action understanding as inverse planning.Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum - 2009 - Cognition 113 (3):329-349.
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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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  • Evidence for positive and negative transfer of abstract task knowledge in adults and school-aged children.Kaichi Yanaoka, Félice van’T. Wout, Satoru Saito & Christopher Jarrold - 2024 - Cognition 242 (C):105650.
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  • The Mental Representation of Human Action.Sydney Levine, Alan M. Leslie & John Mikhail - 2018 - Cognitive Science 42 (4):1229-1264.
    Various theories of moral cognition posit that moral intuitions can be understood as the output of a computational process performed over structured mental representations of human action. We propose that action plan diagrams—“act trees”—can be a useful tool for theorists to succinctly and clearly present their hypotheses about the information contained in these representations. We then develop a methodology for using a series of linguistic probes to test the theories embodied in the act trees. In Study 1, we validate the (...)
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  • An algorithmic account for how humans efficiently learn, transfer, and compose hierarchically structured decision policies.Jing-Jing Li & Anne G. E. Collins - 2025 - Cognition 254 (C):105967.
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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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  • Exploring the hierarchical structure of human plans via program generation.Carlos G. Correa, Sophia Sanborn, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw & Thomas L. Griffiths - 2025 - Cognition 255 (C):105990.
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  • Integrating computation into the mechanistic hierarchy in the cognitive and neural sciences.Lotem Elber-Dorozko & Oron Shagrir - 2019 - Synthese 199 (Suppl 1):43-66.
    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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  • Goal neglect and knowledge chunking in the construction of novel behaviour.Apoorva Bhandari & John Duncan - 2014 - Cognition 130 (1):11-30.
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  • Expanding horizons in reinforcement learning for curious exploration and creative planning.Dale Zhou & Aaron M. Bornstein - 2024 - Behavioral and Brain Sciences 47:e118.
    Curiosity and creativity are expressions of the trade-off between leveraging that with which we are familiar or seeking out novelty. Through the computational lens of reinforcement learning, we describe how formulating the value of information seeking and generation via their complementary effects on planning horizons formally captures a range of solutions to striking this balance.
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  • Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning.Rex G. Liu & Michael J. Frank - 2022 - Artificial Intelligence 312 (C):103770.
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  • Perspectives on Natural Philosophy.Stanley N. Salthe - 2018 - Philosophies 3 (3):23.
    This paper presents a viewpoint on natural philosophy focusing on the organization of substance, as well as its changes as invited by the Second Law of thermodynamics. Modes of change are pointed to as definitive of levels of organization; these include physical, chemical, and biological modes of change. Conceptual uses of the subsumptive hierarchy format are employed throughout this paper. Developmental change in dissipative structures is examined in some detail, generating an argument for the use of final causality in studies (...)
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  • Mental space maps into the future.Anna Belardinelli, Johannes Lohmann, Alessandro Farnè & Martin V. Butz - 2018 - Cognition 176 (C):65-73.
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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 (C):31-45.
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  • 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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  • 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.
    Butz, Achimova, Bilkey, and Knott provide a topic overview and discuss whether the special issue contributions may imply that event‐predictive abilities constitute a root for conceptual human thought, because they enable complex, mutually beneficial, but also intricately competitive, social interactions and language communication.
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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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  • London taxi drivers exploit neighbourhood boundaries for hierarchical route planning.Eva-Maria Griesbauer, Pablo Fernandez Velasco, Antoine Coutrot, Jan M. Wiener, Jeremy G. Morley, Daniel McNamee, Ed Manley & Hugo J. Spiers - 2025 - Cognition 256 (C):106014.
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  • Resource‐rational Models of Human Goal Pursuit.Ben Prystawski, Florian Mohnert, Mateo Tošić & Falk Lieder - 2022 - Topics in Cognitive Science 14 (3):528-549.
    Topics in Cognitive Science, Volume 14, Issue 3, Page 528-549, July 2022.
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  • Arranging Objects in Space: Measuring Task‐Relevant Organizational Behaviors During Goal Pursuit.Grayden J. F. Solman & Alan Kingstone - 2017 - Cognitive Science 41 (4):1042-1070.
    Human behavior unfolds primarily in built environments, where the arrangement of objects is a result of ongoing human decisions and actions, yet these organizational decisions have received limited experimental study. In two experiments, we introduce a novel paradigm designed to explore how individuals organize task-relevant objects in space. Participants completed goals by locating and accessing sequences of objects in a computer-based task, and they were free to rearrange the positions of objects at any time. We measure a variety of organization (...)
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  • Developing PFC Representations Using Reinforcement Learning.Jeremy R. Reynolds & Randall C. O’Reilly - 2009 - Cognition 113 (3):281-292.
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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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  • 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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  • How cognitive theory guides neuroscience.Michael J. Frank & David Badre - 2015 - Cognition 135 (C):14-20.
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  • Resourceful Event-Predictive Inference: The Nature of Cognitive Effort.Martin V. Butz - 2022 - Frontiers in Psychology 13.
    Pursuing a precise, focused train of thought requires cognitive effort. Even more effort is necessary when more alternatives need to be considered or when the imagined situation becomes more complex. Cognitive resources available to us limit the cognitive effort we can spend. In line with previous work, an information-theoretic, Bayesian brain approach to cognitive effort is pursued: to solve tasks in our environment, our brain needs to invest information, that is, negative entropy, to impose structure, or focus, away from a (...)
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  • Reinforcement learning and higher level cognition: Introduction to special issue.Nathaniel D. Daw & Michael J. Frank - 2009 - Cognition 113 (3):259-261.
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  • How the inference of hierarchical rules unfolds over time.Maria K. Eckstein, Ariel Starr & Silvia A. Bunge - 2019 - Cognition 185:151-162.
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