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  1. Formal Epistemology and the New Paradigm Psychology of Reasoning.Niki Pfeifer & Igor Douven - 2013 - Review of Philosophy and Psychology 5 (2):199-221.
    This position paper advocates combining formal epistemology and the new paradigm psychology of reasoning in the studies of conditionals and reasoning with uncertainty. The new paradigm psychology of reasoning is characterized by the use of probability theory as a rationality framework instead of classical logic, used by more traditional approaches to the psychology of reasoning. This paper presents a new interdisciplinary research program which involves both formal and experimental work. To illustrate the program, the paper discusses recent work on the (...)
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  • From Blickets to Synapses: Inferring Temporal Causal Networks by Observation.Chrisantha Fernando - 2013 - Cognitive Science 37 (8):1426-1470.
    How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time (...)
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  • Hierarchical Bayesian Models as Formal Models of Causal Reasoning.York Hagmayer & Ralf Mayrhofer - 2013 - Argument and Computation 4 (1):36 - 45.
    (2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 36-45. doi: 10.1080/19462166.2012.700321.
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  • The Imaginary Fundamentalists: The Unshocking Truth About Bayesian Cognitive Science.Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum - 2011 - Behavioral and Brain Sciences 34 (4):194-196.
    If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.
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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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  • Category Transfer in Sequential Causal Learning: The Unbroken Mechanism Hypothesis.York Hagmayer, Björn Meder, Momme von Sydow & Michael R. Waldmann - 2011 - Cognitive Science 35 (5):842-873.
    The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for (...)
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  • Mechanisms of Cognitive Development: Domain-General Learning or Domain-Specific Constraints?Vladimir M. Sloutsky - 2010 - Cognitive Science 34 (7):1125-1130.
  • Domain-Creating Constraints.Robert L. Goldstone & David Landy - 2010 - Cognitive Science 34 (7):1357-1377.
    The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A learning system must be constrained to learn efficiently, but some of these constraints are themselves learnable. To know how something will behave, a learner must know what kind of thing it is. Although this has led previous researchers to argue for domain-specific constraints that are tied to different kinds/domains, an exciting possibility is that kinds/domains themselves can be (...)
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  • The Role of Causal Models in Multiple Judgments Under Uncertainty.Brett K. Hayes, Guy E. Hawkins, Ben R. Newell, Martina Pasqualino & Bob Rehder - 2014 - Cognition 133 (3):611-620.
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  • Reasoning With Causal Cycles.Bob Rehder - 2017 - Cognitive Science 41 (S5):944-1002.
    This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new formalisms that allow cycles are introduced and evaluated. Dynamic Bayesian networks represent cycles by unfolding them over time. Chain graphs augment CGMs by allowing the presence of undirected links (...)
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