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  1. Human and nonhuman systems are adaptive in a different sense.Tamás Zétényi - 1991 - Behavioral and Brain Sciences 14 (3):507-508.
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  • Base rates do not constrain nonprobability judgments.Paul D. Windschitl & Gary L. Wells - 1996 - Behavioral and Brain Sciences 19 (1):40-41.
    Base rates have no necessary relation to judgments that are not themselves probabilities. There is no logical imperative, for instance, that behavioral base rates must affect causal attributions or that base rate information should affect judgments of legal liability. Decision theorists should be cautious in arguing that base rates place normative constraints on judgments of anything other than posterior probabilities.
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  • Are there really two types of learning?Yorick Wilks - 1986 - Behavioral and Brain Sciences 9 (4):671-671.
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  • The St. Petersburg gamble and risk.Paul Weirich - 1984 - Theory and Decision 17 (2):193-202.
    One resolution of the St. Petersburg paradox recognizes that a gamble carries a risk sensitive to the gamble's stakes. If aversion to risk increases sufficiently fast as stakes go up, the St. Petersburg gamble has a finite utility.
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  • The hard questions about noninductive learning remain unanswered.Eric Wanner - 1986 - Behavioral and Brain Sciences 9 (4):670-670.
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  • The perils of reconstructive remembering and the value of representative design.Kim J. Vicente - 1996 - Behavioral and Brain Sciences 19 (1):40-40.
    Abstract(1) The miscitations of seminal experiments in the base rate literature adds to the existing database of systematic miscitations of wellknown psychological experiments. These miscitations may be caused by a process of reconstructive remembering. (2) Representative design should be the methodological core of Koehler's call for ecologically valid research. This approach can benefit both basic and applied research.
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  • Computational resources do constrain behavior.John K. Tsotsos - 1991 - Behavioral and Brain Sciences 14 (3):506-507.
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  • Rejecting induction: Using occam's razor too soon.J. T. Tolliver - 1986 - Behavioral and Brain Sciences 9 (4):669-670.
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  • Throwing out the baby with the bathwater? Let's not overstate the overselling of the base rate fallacy.Cynthia J. Thomsen & Eugene Borgida - 1996 - Behavioral and Brain Sciences 19 (1):39-40.
    Koehler's summary and critique of research on the base rate fallacy is cogent and persuasive. However, he may have overstated the case, and his suggestions for future research may be too restrictive. We agree that methodological approaches to this topic should be broadened, but we argue that experimental laboratory research and the Bayesian normative standard are useful and should not be abandoned.
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  • The pragmatics of induction.Paul Thagard - 1986 - Behavioral and Brain Sciences 9 (4):668-669.
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  • Nonhuman intentional systems.H. S. Terrace - 1983 - Behavioral and Brain Sciences 6 (3):378-379.
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  • Distinguishing indeterminate belief from “risk-averse” preferences.Katie Steele - 2007 - Synthese 158 (2):189-205.
    I focus my discussion on the well-known Ellsberg paradox. I find good normative reasons for incorporating non-precise belief, as represented by sets of probabilities, in an Ellsberg decision model. This amounts to forgoing the completeness axiom of expected utility theory. Provided that probability sets are interpreted as genuinely indeterminate belief, such a model can moreover make the “Ellsberg choices” rationally permissible. Without some further element to the story, however, the model does not explain how an agent may come to have (...)
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  • Where do you stand on the base rate issue?Douglas Stalker - 1996 - Behavioral and Brain Sciences 19 (1):38-39.
    This commentary presents a self-assessment inventory that will allow readers to determine their own attitude toward the base rate fallacy and its literature. The inventory is scientifically valid but not Medicare/Medicaid reimbursable.
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  • Advancing the rationality debate.Keith E. Stanovich & Richard F. West - 2000 - Behavioral and Brain Sciences 23 (5):701-717.
    In this response, we clarify several misunderstandings of the understanding/acceptance principle and defend our specific operationalization of that principle. We reiterate the importance of addressing the problem of rational task construal and we elaborate the notion of computational limitations contained in our target article. Our concept of thinking dispositions as variable intentional-level styles of epistemic and behavioral regulation is explained, as is its relation to the rationality debate. Many of the suggestions of the commentators for elaborating two-process models are easily (...)
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  • The implicit use of base rates in experiential and ecologically valid tasks.Barbara A. Spellman - 1996 - Behavioral and Brain Sciences 19 (1):38-38.
    When base rates are learned and used in an experiential manner subjects show better base rate use, perhaps because the implicit learning system is engaged. A causal framework in which base rates are relevant might also be necessary. Humans might thus perform better on more ecologically valid tasks, which are likely to contain those three components.
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  • Salvaging parts of the “classical theory” of categorization.Dan Sperber - 1986 - Behavioral and Brain Sciences 9 (4):668-668.
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  • Rationality and irrationality: Still fighting words.Paul Snow - 1991 - Behavioral and Brain Sciences 14 (3):505-506.
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  • A Bayesian theory of thought.Howard Smokler - 1991 - Behavioral and Brain Sciences 14 (3):505-505.
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  • Category differences/automaticity.Edward E. Smith - 1986 - Behavioral and Brain Sciences 9 (4):667-667.
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  • Acting on belief functions.Nicholas J. J. Smith - 2023 - Theory and Decision 95 (4):575-621.
    The degrees of belief of rational agents should be guided by the evidence available to them. This paper takes as a starting point the view—argued elsewhere—that the formal model best able to capture this idea is one that represents degrees of belief using Dempster–Shafer belief functions. However degrees of belief should not only respect evidence: they also guide decision and action. Whatever formal model of degrees of belief we adopt, we need a decision theory that works with it: that takes (...)
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  • Adaptation and satisficing.John Maynard Smith - 1983 - Behavioral and Brain Sciences 6 (3):370-371.
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  • But how does the brain think?Steven L. Small - 1991 - Behavioral and Brain Sciences 14 (3):504-505.
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  • A better way to deal with selection.B. F. Skinner - 1983 - Behavioral and Brain Sciences 6 (3):377-378.
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  • The rationality of causal inference.Thomas R. Shultz - 1991 - Behavioral and Brain Sciences 14 (3):503-504.
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  • Theory-laden concepts: Great, but what is the next step?Charles P. Shimp - 1986 - Behavioral and Brain Sciences 9 (4):666-667.
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  • On the nonapplicability of a rational analysis to human cognition.Eldar Shafir - 1991 - Behavioral and Brain Sciences 14 (3):502-503.
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  • Steps toward an ethological science.Mark S. Seidenberg - 1983 - Behavioral and Brain Sciences 6 (3):377-377.
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  • The learning of function and the function of learning.Roger C. Schank, Gregg C. Collins & Lawrence E. Hunter - 1986 - Behavioral and Brain Sciences 9 (4):672-686.
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  • Transcending inductive category formation in learning.Roger C. Schank, Gregg C. Collins & Lawrence E. Hunter - 1986 - Behavioral and Brain Sciences 9 (4):639-651.
    The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial (...)
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  • Rational analysis will not throw off the yoke of the precision-importance trade-off function.Wolfgang Schwarz - 1991 - Behavioral and Brain Sciences 14 (3):501-502.
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  • Three decision rules for generalized probability representations.Nils-Eric Sahlin - 1985 - Behavioral and Brain Sciences 8 (4):751-753.
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  • Unsharp Sharpness.Nils-Eric Sahlin & Paul Weirich - 2013 - Theoria 80 (1):100-103.
    In a recent, thought-provoking paper Adam Elga argues against unsharp – e.g., indeterminate, fuzzy and unreliable – probabilities. Rationality demands sharpness, he contends, and this means that decision theories like Levi's, Gärdenfors and Sahlin's, and Kyburg's, though they employ different decision rules, face a common, and serious, problem. This article defends the rule to maximize minimum expected utility against Elga's objection.
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  • How can representationalism accommodate degrees of belief? A dispositional representationalist proposal.Darrell P. Rowbottom - 2021 - Synthese 199 (3-4):8943-8964.
    This paper argues that representationalism of a Fodorian variety can accommodate the fact that beliefs come in degrees. First, it responds to two key arguments to the contrary. Second, it builds upon these responses and outlines a novel representationalist theory of degrees of beliefs. I call this theory dispositional representationalism, as it involves direct appeal to our dispositions to form representations and propositional attitudes concerning them.
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  • Content and consciousness versus the International stance.Alexander Rosenberg - 1983 - Behavioral and Brain Sciences 6 (3):375-376.
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  • Intentions and adaptations.H. L. Roitblat - 1983 - Behavioral and Brain Sciences 6 (3):375-375.
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  • International plovers or just dump brids?Carolyn A. Ristau - 1983 - Behavioral and Brain Sciences 6 (3):373-375.
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  • Testing the descriptive validity of possibility theory in human judgments of uncertainty.Eric Raufaste, Rui da Silva Neves & Claudette Mariné - 2003 - Artificial Intelligence 148 (1-2):197-218.
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  • Robust Bayes classifiers.Marco Ramoni & Paola Sebastiani - 2001 - Artificial Intelligence 125 (1-2):209-226.
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  • The cognitive laboratory, the library and the Skinner box.Howard Rachlin - 1991 - Behavioral and Brain Sciences 14 (3):501-501.
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  • The International stance faces backward.Howard Rachlin - 1983 - Behavioral and Brain Sciences 6 (3):373-373.
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  • Improving decision accuracy where base rates matter: The prediction of violent recidivism.Vernon L. Quinsey - 1996 - Behavioral and Brain Sciences 19 (1):37-38.
    Base rates are vital in predicting violent criminal recidivism. However, both lay people given simulated prediction tasks and professionals milking real life predictions appear insensitive to variations in the base rate of violent recidivism. Although there are techniques to help decision makers attend to base rates, increased decision accuracy is better sought in improved actuarial models as opposed to improved clinicians.
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  • Approaches, assumptions, and goals in modeling cognitive behavior.Richard E. Pastore & David G. Payne - 1986 - Behavioral and Brain Sciences 9 (4):665-666.
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  • The psychology of category learning: Current status and future prospect.Gregory L. Murphy - 1986 - Behavioral and Brain Sciences 9 (4):664-665.
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  • Of what use categories?Ruth Garrett Millikan - 1986 - Behavioral and Brain Sciences 9 (4):663-664.
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  • Dennett's rational animals: And how behavorism overlooked them.Ruth Garrett Millikan - 1983 - Behavioral and Brain Sciences 6 (3):372-373.
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  • Parlez-vous baboon, Bwana Sherlock?E. W. Menzel - 1983 - Behavioral and Brain Sciences 6 (3):371-372.
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  • Conservatism revisited: Base rates, prior probabilities, and averaging strategies.Nancy Paule Melone & Timothy W. McGuire - 1996 - Behavioral and Brain Sciences 19 (1):36-37.
    Consistent with Koehler's position, we propose a generalization of the base rate fallacy and earlier conservatism literatures. In studies using both traditional tasks and new tasks based on ecologically valid base rates, our subjects typically underweight individuating information at least as much as they underweight base rates. The implications of cue consistency for averaging heuristics are discussed.
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  • How are base rates used? Interactive and group effects.Peter J. McLeod & Margo Watt - 1996 - Behavioral and Brain Sciences 19 (1):35-36.
    Koehler is right that base rate information is used, to various degrees, both in laboratory tasks and in everyday life. However, it is not time to turn our backs on laboratory tasks and focus solely on ecologically valid decision making. Tightly controlled experimental data are still needed to understandhowbase rate information is used, and how this varies among groups.
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  • Which reference class is evoked?Craig R. M. McKenzie & Jack B. Soll - 1996 - Behavioral and Brain Sciences 19 (1):34-35.
    Any instance (i.e., event, behavior, trait) belongs to infinitely many reference classes, hence there are infinitely many base rates from which to choose. People clearly do not entertain all possible reference classes, however, so something must be limiting the search space. We suggest some possible mechanisms that determine which reference class is evoked for the purpose of judgment and decision.
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  • Intentions as goads.David McFarland - 1983 - Behavioral and Brain Sciences 6 (3):369-370.
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