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  1. Cognitive Niches: An Ecological Model of Strategy Selection.Julian N. Marewski & Lael J. Schooler - 2011 - Psychological Review 118 (3):393-437.
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  • An Integrated Model of Cognitive Control in Task Switching.Erik M. Altmann & Wayne D. Gray - 2008 - Psychological Review 115 (3):602-639.
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  • The Soft Constraints Hypothesis: A Rational Analysis Approach to Resource Allocation for Interactive Behavior.Wayne D. Gray, Chris R. Sims, Wai-Tat Fu & Michael J. Schoelles - 2006 - Psychological Review 113 (3):461-482.
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  • Simulations in Cyber-Security: A Review of Cognitive Modeling of Network Attackers, Defenders, and Users. [REVIEW]Vladislav D. Veksler, Norbou Buchler, Blaine E. Hoffman, Daniel N. Cassenti, Char Sample & Shridat Sugrim - 2018 - Frontiers in Psychology 9.
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  • Short Term Gains, Long Term Pains: How Cues About State Aid Learning in Dynamic Environments.Bradley C. Love Todd M. Gureckis - 2009 - Cognition 113 (3):293.
  • 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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  • Integrating Reinforcement Learning with Models of Representation Learning.Matt Jones & Fabián Canas - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1258--1263.
  • Embodied Spatial Cognition.J. Gregory Trafton & Anthony M. Harrison - 2011 - Topics in Cognitive Science 3 (4):686-706.
    We present a spatial system called Specialized Egocentrically Coordinated Spaces embedded in an embodied cognitive architecture (ACT-R Embodied). We show how the spatial system works by modeling two different developmental findings: gaze-following and Level 1 perspective taking. The gaze-following model is based on an experiment by Corkum and Moore (1998), whereas the Level 1 visual perspective-taking model is based on an experiment by Moll and Tomasello (2006). The models run on an embodied robotic system.
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  • Are People Successful at Learning Sequences of Actions on a Perceptual Matching Task?Reiko Yakushijin & Robert A. Jacobs - 2011 - Cognitive Science 35 (5):939-962.
    We report the results of an experiment in which human subjects were trained to perform a perceptual matching task. Subjects were asked to manipulate comparison objects until they matched target objects using the fewest manipulations possible. An unusual feature of the experimental task is that efficient performance requires an understanding of the hidden or latent causal structure governing the relationships between actions and perceptual outcomes. We use two benchmarks to evaluate the quality of subjects’ learning. One benchmark is based on (...)
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  • SAwSu: An Integrated Model of Associative and Reinforcement Learning.Vladislav D. Veksler, Christopher W. Myers & Kevin A. Gluck - 2014 - Cognitive Science 38 (3):580-598.
    Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid-navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The (...)
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  • Rational and Mechanistic Perspectives on Reinforcement Learning.Nick Chater - 2009 - Cognition 113 (3):350-364.
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  • Goal‐Proximity Decision‐Making.Vladislav D. Veksler, Wayne D. Gray & Michael J. Schoelles - 2013 - Cognitive Science 37 (4):757-774.
    Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal-Proximity Decision-making (GPD), is implemented within the ACT-R cognitive framework. GPD is found to be more efficient than RL in three maze-navigation simulations. GPD advantages over RL seem to grow as task difficulty is increased. (...)
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  • Short-Term Gains, Long-Term Pains: How Cues About State Aid Learning in Dynamic Environments.Todd M. Gureckis & Bradley C. Love - 2009 - Cognition 113 (3):293-313.
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  • The Past, Present, and Future of Cognitive Architectures.Niels Taatgen & John R. Anderson - 2010 - Topics in Cognitive Science 2 (4):693-704.
    Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, they share many identical or similar mechanisms, permitting possible future convergence. In judging the quality of a particular cognitive model, it is pertinent to not just (...)
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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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