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  1. FAST: A Novel, Executive Function-Based Approach to Cognitive Enhancement.Jessamy Norton-Ford Almquist, Santosh Mathan, Anna-Katharine Brem, Franziska Plessow, James McKanna, Emiliano Santarnecchi, Alvaro Pascual-Leone, Roi Cohen Kadosh, Misha Pavel & Nick Yeung - 2019 - Frontiers in Human Neuroscience 13.
  • Central and Peripheral Components of Working Memory Storage.Nelson Cowan, J. Scott Saults & Christopher L. Blume - 2014 - Journal of Experimental Psychology: General 143 (5):1806-1836.
  • Popper's Severity of Test as an Intuitive Probabilistic Model of Hypothesis Testing.Fenna H. Poletiek - 2009 - Behavioral and Brain Sciences 32 (1):99-100.
    Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.
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  • The Uncertain Reasoner: Bayes, Logic, and Rationality.Mike Oaksford & Nick Chater - 2009 - Behavioral and Brain Sciences 32 (1):105-120.
    Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...)
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  • On the Impacts of Working Memory Training on Executive Functioning.Tiina Salminen, Tilo Strobach & Torsten Schubert - 2012 - Frontiers in Human Neuroscience 6.
  • A General Structure for Legal Arguments About Evidence Using Bayesian Networks.Norman Fenton, Martin Neil & David A. Lagnado - 2013 - Cognitive Science 37 (1):61-102.
    A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where (...)
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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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  • Planning Following Stroke: A Relational Complexity Approach Using the Tower of London.Glenda Andrews, Graeme S. Halford, Mark Chappell, Annick Maujean & David H. K. Shum - 2014 - Frontiers in Human Neuroscience 8.
  • Three-Year-Olds' Theories of Mind Are Symbolic but of Low Complexity.Graeme S. Halford & Glenda Andrews - 2014 - Frontiers in Psychology 5.
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  • Relational Knowledge: The Foundation of Higher Cognition.Graeme S. Halford, William H. Wilson & Steven Phillips - 2010 - Trends in Cognitive Sciences 14 (11):497-505.
  • Models as Relational Categories.Tommi Kokkonen - 2017 - Science & Education 26 (7-9):777-798.
    Model-based learning has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, (...)
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  • Fractions: The New Frontier for Theories of Numerical Development.Robert S. Siegler, Lisa K. Fazio, Drew H. Bailey & Xinlin Zhou - 2013 - Trends in Cognitive Sciences 17 (1):13-19.
  • Economic Complexities and Cognitive Hurdles: Accounting for Specific Economic Misconceptions Without an Ultimate Cause.David Leiser & Yhonatan Shemesh - 2018 - Behavioral and Brain Sciences 41.
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  • Probabilistic Representation in Syllogistic Reasoning: A Theory to Integrate Mental Models and Heuristics.Masasi Hattori - 2016 - Cognition 157:296-320.
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