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  1. The Acquisition of Robust and Flexible Cognitive Skills.Niels A. Taatgen, David Huss, Daniel Dickison & John R. Anderson - 2008 - Journal of Experimental Psychology: General 137 (3):548-565.
  • Human Symbol Manipulation Within an Integrated Cognitive Architecture.John R. Anderson - 2005 - Cognitive Science 29 (3):313-341.
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  • Modeling Parallelization and Flexibility Improvements in Skill Acquisition: From Dual Tasks to Complex Dynamic Skills.Niels Taatgen - 2005 - Cognitive Science 29 (3):421-455.
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  • Linguistic Self‐Correction in the Absence of Feedback: A New Approach to the Logical Problem of Language Acquisition.Michael Ramscar & Daniel Yarlett - 2007 - Cognitive Science 31 (6):927-960.
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  • Resolving the Paradox of the Active User: Stable Suboptimal Performance in Interactive Tasks.Wai-Tat Fu & Wayne D. Gray - 2004 - Cognitive Science 28 (6):901-935.
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  • Modeling Developmental Transitions on the Balance Scale Task.Hedderik Rijn, Maarten Someren & Han Maas - 2003 - Cognitive Science 27 (2):227-257.
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  • The Role of Falsification in the Development of Cognitive Architectures: Insights From a Lakatosian Analysis.Richard P. Cooper - 2007 - Cognitive Science 31 (3):509-533.
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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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  • Building an ACT‐R Reader for Eye‐Tracking Corpus Data.Jakub Dotlačil - 2018 - Topics in Cognitive Science 10 (1):144-160.
    Cognitive architectures have often been applied to data from individual experiments. In this paper, I develop an ACT-R reader that can model a much larger set of data, eye-tracking corpus data. It is shown that the resulting model has a good fit to the data for the considered low-level processes. Unlike previous related works, the model achieves the fit by estimating free parameters of ACT-R using Bayesian estimation and Markov-Chain Monte Carlo techniques, rather than by relying on the mix of (...)
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  • RACE/A: An Architectural Account of the Interactions Between Learning, Task Control, and Retrieval Dynamics.Leendert van Maanen, Hedderik van Rijn & Niels Taatgen - 2012 - Cognitive Science 36 (1):62-101.
    This article discusses how sequential sampling models can be integrated in a cognitive architecture. The new theory Retrieval by Accumulating Evidence in an Architecture (RACE/A) combines the level of detail typically provided by sequential sampling models with the level of task complexity typically provided by cognitive architectures. We will use RACE/A to model data from two variants of a picture–word interference task in a psychological refractory period design. These models will demonstrate how RACE/A enables interactions between sequential sampling and long-term (...)
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  • 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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  • How Children Process Reduced Forms: A Computational Cognitive Modeling Approach to Pronoun Processing in Discourse.Margreet Vogelzang, Maria Teresa Guasti, Hedderik van Rijn & Petra Hendriks - 2021 - Cognitive Science 45 (4):e12951.
  • On the Necessity of U-Shaped Learning.Lorenzo Carlucci & John Case - 2013 - Topics in Cognitive Science 5 (1):56-88.
    A U-shaped curve in a cognitive-developmental trajectory refers to a three-step process: good performance followed by bad performance followed by good performance once again. U-shaped curves have been observed in a wide variety of cognitive-developmental and learning contexts. U-shaped learning seems to contradict the idea that learning is a monotonic, cumulative process and thus constitutes a challenge for competing theories of cognitive development and learning. U-shaped behavior in language learning (in particular in learning English past tense) has become a central (...)
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  • How Does the Mind Work? Insights From Biology.Gary Marcus - 2009 - Topics in Cognitive Science 1 (1):145-172.
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  • Can Resources Save Rationality? ‘Anti-Bayesian’ Updating in Cognition and Perception.Eric Mandelbaum, Isabel Won, Steven Gross & Chaz Firestone - 2020 - Behavioral and Brain Sciences 143:e16.
    Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-Bayesian” effects where the mind updates in a direction opposite the one it should? We present two phenomena — belief polarization and the size-weight illusion — that are not obviously explained by performance- or resource-based constraints, nor by the authors’ brief discussion of reference repulsion. Can resource rationality accommodate them?
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  • Five-Year-Olds’ Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study.Burcu Arslan, Niels A. Taatgen & Rineke Verbrugge - 2017 - Frontiers in Psychology 8.
  • The Nature and Transfer of Cognitive Skills.Niels A. Taatgen - 2013 - Psychological Review 120 (3):439-471.
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  • Cognitive Modeling of Individual Variation in Reference Production and Comprehension.Petra Hendriks - 2016 - Frontiers in Psychology 7.
  • Models of Atypical Development Must Also Be Models of Normal Development.Gert Westermann & Denis Mareschal - 2002 - Behavioral and Brain Sciences 25 (6):771-772.
    Connectionist models aiming to reveal the mechanisms of atypical development must in their undamaged form constitute plausible models of normal development and follow a developmental trajectory that matches empirical data. Constructivist models that adapt their structure to the learning task satisfy this demand. They are therefore more informative in the study of atypical development than the static models employed by Thomas & Karmiloff-Smith (T&K-S).
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  • Thinking as a Production System.Marsha C. Lovett & John R. Anderson - 2005 - In K. Holyoak & B. Morrison (eds.), The Cambridge Handbook of Thinking and Reasoning. Cambridge University Press. pp. 401--429.
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  • Modeling Developmental Transitions on the Balance Scale Task.Hedderik van Rijn, Maarten van Someren & Han van der Maas - 2003 - Cognitive Science 27 (2):227-257.
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  • 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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  • Re-Thinking Stages of Cognitive Development: An Appraisal of Connectionist Models of the Balance Scale Task.Philip T. Quinlan, Han L. J. van der Maas, Brenda R. J. Jansen, Olaf Booij & Mark Rendell - 2007 - Cognition 103 (3):413-459.
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  • Cognitive Costs of Decision-Making Strategies: A Resource Demand Decomposition Analysis with a Cognitive Architecture.Hanna B. Fechner, Lael J. Schooler & Thorsten Pachur - 2018 - Cognition 170:102-122.
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  • Cognitive Architectures as Lakatosian Research Programs: Two Case Studies.Richard P. Cooper - 2006 - Philosophical Psychology 19 (2):199-220.
    Cognitive architectures - task-general theories of the structure and function of the complete cognitive system - are sometimes argued to be more akin to frameworks or belief systems than scientific theories. The argument stems from the apparent non-falsifiability of existing cognitive architectures. Newell was aware of this criticism and argued that architectures should be viewed not as theories subject to Popperian falsification, but rather as Lakatosian research programs based on cumulative growth. Newell's argument is undermined because he failed to demonstrate (...)
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  • An Integrated Theory of the Mind.John R. Anderson, Daniel Bothell, Michael D. Byrne, Scott Douglass, Christian Lebiere & Yulin Qin - 2004 - Psychological Review 111 (4):1036-1060.
  • Modeling Language Acquisition in Atypical Phenotypes.Michael S. C. Thomas & Annette Karmiloff-Smith - 2003 - Psychological Review 110 (4):647-682.
  • Mechanisms of Knowledge Transfer.Timothy J. Nokes - 2009 - Thinking and Reasoning 15 (1):1 – 36.
    A central goal of cognitive science is to develop a general theory of transfer to explain how people use and apply their prior knowledge to solve new problems. Previous work has identified multiple mechanisms of transfer including (but not limited to) analogy, knowledge compilation, and constraint violation. The central hypothesis investigated in the current work is that the particular profile of transfer processes activated for a given situation depends on both (a) the type of knowledge to be transferred and how (...)
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  • Logic and Social Cognition: The Facts Matter, and so Do Computational Models.Rineke Verbrugge - 2009 - Journal of Philosophical Logic 38 (6):649-680.
    This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that the (...)
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