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  1. Rational decision making: balancing RUN and JUMP modes of analysis.Tilmann Betsch & Carsten Held - 2012 - Mind and Society 11 (1):69-80.
    Rationality in decision making is commonly assessed by comparing choice performance against normative standards. We argue that such a performance-centered approach blurs the distinction between rational choice and adaptive behavior. Instead, rational choice should be assessed with regard to the way individuals make analytic decisions. We suggest that analytic decisions can be made in two different modes in which control processes are directed at different levels. In a RUN mode, thought is directed at controlling the operation of a decision strategy. (...)
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  • Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik - 2011 - Cognitive Science 35 (8):1407-1455.
    People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...)
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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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  • 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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  • Understanding repeated simple choices.Iddo Gal - 1996 - Thinking and Reasoning 2 (1):81 – 98.
    This study examined students' reasoning about simple repeated choices. Each choice involved ''betting'' on two events, differing in probability. We asked subjects to generate or evaluate alternative strategies such as betting on the most likely event on every trial, betting on it on almost every trial, or employing a ''probability matching'' strategy. Almost half of the college students did not generate or rank strategies according to their expected value, but few subjects preferred a strategy of strict probability matching. High-school students (...)
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  • Applied cognitive psychology and the "strong replacement" of epistemology by normative psychology.Carole J. Lee - 2008 - Philosophy of the Social Sciences 38 (1):55-75.
    is normative in the sense that it aims to make recommendations for improving human judgment; it aims to have a practical impact on morally and politically significant human decisions and actions; and it studies normative, rational judgment qua rational judgment. These nonstandard ways of understanding ACP as normative collectively suggest a new interpretation of the strong replacement thesis that does not call for replacing normative epistemic concepts, relations, and inquiries with descriptive, causal ones. Rather, it calls for recognizing that the (...)
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  • Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers.Alison Gopnik - 2004 - Cognitive Science 28 (3):303-333.
    Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector”, a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s activation that (...)
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  • Are people programmed to commit fallacies? Further thoughts about the interpretation of experimental data on probability judgment.L. Jonathan Cohen - 1982 - Journal for the Theory of Social Behaviour 12 (3):251–274.
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  • The folk psychology of free will: Fits and starts.Shaun Nichols - 2004 - Mind and Language 19 (5):473-502.
    According to agent-causal accounts of free will, agents have the capacity to cause actions, and for a given action, an agent could have done otherwise. This paper uses existing results and presents experimental evidence to argue that young children deploy a notion of agent-causation. If young children do have such a notion, however, it remains quite unclear how they acquire it. Several possible acquisition stories are canvassed, including the possibility that the notion of agent-causation develops from a prior notion of (...)
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  • Objectivity, Simulation and the Unity of Consciousness: Current Issues in the Philosophy of Mind.Christopher Peacocke - 1995 - Philosophy 70 (273):469-472.
    Notes on Contributors • Preface • Christopher Peacocke, Introduction: The Issues and their Further Development I OBJECTIVE THOUGHT • John Campbell, Objects and Objectivity Commentaries • Bill Brewer, Thoughts about Objects, Places and Times • John O'Keefe, Cognitive Maps, Time and Causality II OBJECTIVITY AND THE UNITY OF CONSCIOUSNESS • Susan Hurley, Unity and Objectivity Commentaries • Anthony Marcel, What is Relevant to the Unity of Consciousness? • Michael Lockwood, Issues of Unity and Objectivity III UNDERSTANDING THE MENTAL:THEORY OR SIMULATION (...)
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  • Teaching Bayesian reasoning in less than two hours.Peter Sedlmeier & Gerd Gigerenzer - 2001 - Journal of Experimental Psychology: General 130 (3):380.
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  • Decision making: Nonrational theories.Gerd Gigerenzer - 2001 - In Neil J. Smelser & Paul B. Baltes (eds.), International Encyclopedia of the Social and Behavioral Sciences. Elsevier. pp. 5--3304.
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  • The origins of probabilistic inference in human infants.Stephanie Denison & Fei Xu - 2014 - Cognition 130 (3):335-347.
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  • The strategy of optimality revisited.Paul J. H. Schoemaker - 1991 - Behavioral and Brain Sciences 14 (2):237-245.
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  • Optimal confusion.Stephanie Stolarz-Fantino & Edmund Fantino - 1991 - Behavioral and Brain Sciences 14 (2):234-234.
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  • Optimality as a prescriptive tool.Alexander H. G. Rinnooy Kan - 1991 - Behavioral and Brain Sciences 14 (2):230-231.
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  • Rational agents, real people and the quest for optimality.Eldar Shafir - 1991 - Behavioral and Brain Sciences 14 (2):232-232.
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  • Extremum descriptions, process laws and minimality heuristics.Elliott Sober - 1991 - Behavioral and Brain Sciences 14 (2):232-233.
    The examples and concepts that Shoemaker cites are rather heterogeneous. Some distinctions need to be drawn. An optimality thesis involves not just an ordering of options, but a value judgment about them. So let us begin by distinguishing minimality from optimality. And the concept of minimality can play a variety of roles, among which I distinguish between extremum descriptions, statements hypothesizing an optimizing process, and methodological recommendations. Finally, I consider how the three categories relate to Shoemaker’s question that “Who is (...)
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  • Avoid the push-pull dilemma in explanation.Kenneth M. Steele - 1991 - Behavioral and Brain Sciences 14 (2):233-234.
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  • The infinite regress of optimization.Philippe Mongin - 1991 - Behavioral and Brain Sciences 14 (2):229-230.
    A comment on Paul Schoemaker's target article in Behavioral and Brain Sciences, 14 (1991), p. 205-215, "The Quest for Optimality: A Positive Heuristic of Science?" (https://doi.org/10.1017/S0140525X00066140). This comment argues that the optimizing model of decision leads to an infinite regress, once internal costs of decision (i.e., information and computation costs) are duly taken into account.
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  • Vaulting optimality.Peter Dayan & Jon Oberlander - 1991 - Behavioral and Brain Sciences 14 (2):221-222.
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  • Optimality and human memory.John R. Anderson - 1991 - Behavioral and Brain Sciences 14 (2):215-216.
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  • On the psychology of prediction: Whose is the fallacy?L. Jonathan Cohen - 1979 - Cognition 7 (December):385-407.
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  • Rational Number Representation by the Approximate Number System.Chuyan Qu, Sam Clarke, Francesca Luzzi & Elizabeth Brannon - 2024 - Cognition 250 (105839):1-13.
    The approximate number system (ANS) enables organisms to represent the approximate number of items in an observed collection, quickly and independently of natural language. Recently, it has been proposed that the ANS goes beyond representing natural numbers by extracting and representing rational numbers (Clarke & Beck, 2021a). Prior work demonstrates that adults and children discriminate ratios in an approximate and ratio-dependent manner, consistent with the hallmarks of the ANS. Here, we use a well-known “connectedness illusion” to provide evidence that these (...)
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  • The Development of Spatial–Temporal, Probability, and Covariation Information to Infer Continuous Causal Processes.Selma Dündar-Coecke, Andrew Tolmie & Anne Schlottmann - 2021 - Frontiers in Psychology 12.
    This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical (...)
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  • Developing a Learning Progression for Probability Based on the GDINA Model in China.Shengnan Bai - 2020 - Frontiers in Psychology 11.
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  • The quest for plausibility: A negative heuristic for science?R. W. Byrne - 1991 - Behavioral and Brain Sciences 14 (2):217-218.
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  • Intuitive statistical inferences in chimpanzees and humans follow Weber’s law.Johanna Eckert, Josep Call, Jonas Hermes, Esther Herrmann & Hannes Rakoczy - 2018 - Cognition 180 (C):99-107.
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  • Executive function in learning mathematics by comparison: incorporating everyday classrooms into the science of learning.Kreshnik Nasi Begolli, Lindsey Engle Richland, Susanne M. Jaeggi, Emily McLaughlin Lyons, Ellen C. Klostermann & Bryan J. Matlen - 2018 - Thinking and Reasoning 24 (2):280-313.
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  • Children’s Neglect of Probabilities in Decision Making with and without Feedback.Anna Lang & Tilmann Betsch - 2018 - Frontiers in Psychology 9.
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  • Children’s quantitative Bayesian inferences from natural frequencies and number of chances.Stefania Pighin, Vittorio Girotto & Katya Tentori - 2017 - Cognition 168 (C):164-175.
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  • The Folk Psychology of Free Will: Fits and Starts.Shaun Nichols - 2004 - Mind and Language 19 (5):473-502.
    According to agent‐causal accounts of free will, agents have the capacity to cause actions, and for a given action, an agent could have done otherwise. This paper uses existing results and presents experimental evidence to argue that young children deploy a notion of agent‐causation. If young children do have such a notion, however, it remains quite unclear how they acquire it. Several possible acquisition stories are canvassed, including the possibility that the notion of agent‐causation develops from a prior notion of (...)
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  • The Physicalization of Mathematics.Peter Milne - 1994 - British Journal for the Philosophy of Science 45 (1):305-340.
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  • Making sense of randomness: Implicit encoding as a basis for judgment.Ruma Falk & Clifford Konold - 1997 - Psychological Review 104 (2):301-318.
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  • How to improve Bayesian reasoning without instruction: Frequency formats.Gerd Gigerenzer & Ulrich Hoffrage - 1995 - Psychological Review 102 (4):684-704.
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  • The Invisible Hand: Toddlers Connect Probabilistic Events With Agentive Causes.Yang Wu, Paul Muentener & Laura E. Schulz - 2016 - Cognitive Science 40 (8):1854-1876.
    Children posit unobserved causes when events appear to occur spontaneously. What about when events appear to occur probabilistically? Here toddlers saw arbitrary causal relationships in a fixed, alternating order. The relationships were then changed in one of two ways. In the Deterministic condition, the event order changed ; in the Probabilistic condition, the causal relationships changed. As intended, toddlers looked equally long at both changes. We then introduced a previously unseen candidate cause. Toddlers looked longer at the appearance of a (...)
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  • Toma de decisiones en la adolescencia: Entre la razón y la emoción.Ciencia Cognitiva - forthcoming - Ciencia Cognitiva.
    Yunier Broche-Pérez y Denisse Cruz-López Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Cuba Hospital … Read More →.
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  • Comparative metaphysics: the development of representing natural and normative regularities in human and non-human primates.Hannes Rakoczy - 2015 - Phenomenology and the Cognitive Sciences 14 (4):683-697.
    How do human children come up to carve up and think of the world around them in its most general and abstract structure? And to which degree are these general forms of viewing the world shared by other animals, notably by non-human primates? In response to these questions of what could be called comparative metaphysics, this paper discusses new evidence from developmental and comparative research to argue for the following picture: human children and non-human primates share a basic framework of (...)
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  • Apes are intuitive statisticians.Hannes Rakoczy, Annette Clüver, Liane Saucke, Nicole Stoffregen, Alice Gräbener, Judith Migura & Josep Call - 2014 - Cognition 131 (1):60-68.
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  • Children can solve Bayesian problems: the role of representation in mental computation.Liqi Zhu & Gerd Gigerenzer - 2006 - Cognition 98 (3):287-308.
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  • Is economics still immersed in the old concepts of the Enlightenment era?Andrzej P. Wierzbicki - 1991 - Behavioral and Brain Sciences 14 (2):236-237.
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  • The human being as a bumbling optimalist: A psychologist's viewpoint.Masanao Toda - 1991 - Behavioral and Brain Sciences 14 (2):235-235.
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  • Should the quest for optimality worry us?Nils-Eric Sahlin - 1991 - Behavioral and Brain Sciences 14 (2):231-231.
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  • Don't just sit there, optimise something.J. H. P. Paelinck - 1991 - Behavioral and Brain Sciences 14 (2):230-230.
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  • Complexity and optimality.Dauglas A. Miller & Steven W. Zucker - 1991 - Behavioral and Brain Sciences 14 (2):227-228.
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  • Two dynamic criteria for validating claims of optimality.Geoffrey F. Miller - 1991 - Behavioral and Brain Sciences 14 (2):228-229.
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  • The example of psychology: Optimism, not optimality.Daniel S. Levine - 1991 - Behavioral and Brain Sciences 14 (2):225-226.
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  • Straining the word “optimal”.James E. Mazur - 1991 - Behavioral and Brain Sciences 14 (2):227-227.
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  • Natural science, social science and optimality.Oleg Larichev - 1991 - Behavioral and Brain Sciences 14 (2):224-225.
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  • Why optimality is not worth arguing about.Stephen E. G. Lea - 1991 - Behavioral and Brain Sciences 14 (2):225-225.
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