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  1. There are no epistemic norms of inquiry.David Thorstad - 2022 - Synthese 200 (5):1-24.
    Epistemic nihilism for inquiry is the claim that there are no epistemic norms of inquiry. Epistemic nihilism was once the received stance towards inquiry, and I argue that it should be taken seriously again. My argument is that the same considerations which led us away from epistemic nihilism in the case of belief not only cannot refute epistemic nihilism for inquiry, but in fact may well support it. These include the argument from non-existence that there are no non-epistemic reasons for (...)
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  • Two paradoxes of bounded rationality.David Thorstad - 2022 - Philosophers' Imprint 22.
    My aim in this paper is to develop a unified solution to two paradoxes of bounded rationality. The first is the regress problem that incorporating cognitive bounds into models of rational decisionmaking generates a regress of higher-order decision problems. The second is the problem of rational irrationality: it sometimes seems rational for bounded agents to act irrationally on the basis of rational deliberation. I review two strategies which have been brought to bear on these problems: the way of weakening which (...)
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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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  • Cognitive Success: A Consequentialist Account of Rationality in Cognition.Gerhard Schurz & Ralph Hertwig - 2019 - Topics in Cognitive Science 11 (1):7-36.
    One of the most discussed issues in psychology—presently and in the past—is how to define and measure the extent to which human cognition is rational. The rationality of human cognition is often evaluated in terms of normative standards based on a priori intuitions. Yet this approach has been challenged by two recent developments in psychology that we review in this article: ecological rationality and descriptivism. Going beyond these contributions, we consider it a good moment for psychologists and philosophers to join (...)
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  • Naïve and Robust: Class‐Conditional Independence in Human Classification Learning.Jana B. Jarecki, Björn Meder & Jonathan D. Nelson - 2018 - Cognitive Science 42 (1):4-42.
    Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...)
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  • Can evolution get us off the hook? Evaluating the ecological defence of human rationality.Maarten Boudry, Michael Vlerick & Ryan McKay - 2015 - Consciousness and Cognition 33:524-535.
    This paper discusses the ecological case for epistemic innocence: does biased cognition have evolutionary benefits, and if so, does that exculpate human reasoners from irrationality? Proponents of ‘ecological rationality’ have challenged the bleak view of human reasoning emerging from research on biases and fallacies. If we approach the human mind as an adaptive toolbox, tailored to the structure of the environment, many alleged biases and fallacies turn out to be artefacts of narrow norms and artificial set-ups. However, we argue that (...)
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  • Mechanisms for Robust Cognition.Matthew M. Walsh & Kevin A. Gluck - 2015 - Cognitive Science 39 (6):1131-1171.
    To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness (...)
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  • An Integrated Model of Collaborative Skill Acquisition: Anticipation, Control Tuning, and Role Adoption.Cvetomir M. Dimov, John R. Anderson, Shawn A. Betts & Dan Bothell - 2023 - Cognitive Science 47 (7):e13303.
    We studied collaborative skill acquisition in a dynamic setting with the game Co-op Space Fortress. While gaining expertise, the majority of subjects became increasingly consistent in the role they adopted without being able to communicate. Moreover, they acted in anticipation of the future task state. We constructed a collaborative skill acquisition model in the cognitive architecture ACT-R that reproduced subject skill acquisition trajectory. It modeled role adoption through reinforcement learning and predictive processes through motion extrapolation and learned relevant control parameters (...)
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  • Building the Theory of Ecological Rationality.Peter M. Todd & Henry Brighton - 2016 - Minds and Machines 26 (1-2):9-30.
    While theories of rationality and decision making typically adopt either a single-powertool perspective or a bag-of-tricks mentality, the research program of ecological rationality bridges these with a theoretically-driven account of when different heuristic decision mechanisms will work well. Here we described two ways to study how heuristics match their ecological setting: The bottom-up approach starts with psychologically plausible building blocks that are combined to create simple heuristics that fit specific environments. The top-down approach starts from the statistical problem facing the (...)
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  • Can quantum probability provide a new direction for cognitive modeling?Emmanuel M. Pothos & Jerome R. Busemeyer - 2013 - Behavioral and Brain Sciences 36 (3):255-274.
    Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the (...)
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  • Ecological rationality and economics: where the Twain shall meet.Andreas Ortmann & Leonidas Spiliopoulos - 2023 - Synthese 201 (4):1-30.
    Over the past decades psychological theories have made significant headway into economics, culminating in the 2002 (partially) and 2017 Nobel prizes awarded for work in the field of Behavioral Economics. Many of the insights imported from psychology into economics share a common trait: the presumption that decision makers use shortcuts that lead to deviations from rational behaviour (the Heuristics-and-Biases program). Many economists seem unaware that this viewpoint has long been contested in cognitive psychology. Proponents of an alternative program (the Ecological-Rationality (...)
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  • Narratives, environments, and decision-making: A fascinating narrative, but one to be completed.Julian N. Marewski - 2023 - Behavioral and Brain Sciences 46:e102.
    I encourage Johnson et al. to ground Conviction Narrative Theory in more detail in foundational, earlier decision-making research – first and foremost in Herbert Simon's work. Moreover, I wonder if and how further reflections about narratives could aid tackling two interrelated grand challenges of the decision sciences: To describe decision-making environments; to understand how people select among decision-strategies in environments.
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  • Problem Solvers Adjust Cognitive Offloading Based on Performance Goals.Patrick P. Weis & Eva Wiese - 2019 - Cognitive Science 43 (12):e12802.
    When incorporating the environment into mental processing (cf., cognitive offloading), one creates novel cognitive strategies that have the potential to improve task performance. Improved performance can, for example, mean faster problem solving, more accurate solutions, or even higher grades at university.1 Although cognitive offloading has frequently been associated with improved performance, it is yet unclear how flexible problem solvers are at matching their offloading habits with their current performance goals (can people improve goal‐related instead of generic performance, e.g., when being (...)
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  • Approaches to Cognitive Modeling in Dynamic Systems Control.Daniel V. Holt & Magda Osman - 2017 - Frontiers in Psychology 8.
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  • Fast and frugal heuristics and naturalistic decision making: a review of their commonalities and differences. [REVIEW]Yixing Shan & Lili Yang - 2017 - Thinking and Reasoning 23 (1):10-32.
    Both the fast and frugal heuristics and the naturalistic decision making research programmes have identified important areas of inquiry previously neglected in the traditional study of human judgment and decision making, and have greatly contributed to the understanding of people's real-world decision making under environmental constraints. The two programmes share similar theoretical arguments regarding the rationality, optimality, and role of experience in decision making. Their commonalities have made them appealing to each other, and efforts have been made, by their leading (...)
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  • Nudge Versus Boost: How Coherent are Policy and Theory?Till Grüne-Yanoff & Ralph Hertwig - 2016 - Minds and Machines 26 (1-2):149-183.
    If citizens’ behavior threatens to harm others or seems not to be in their own interest, it is not uncommon for governments to attempt to change that behavior. Governmental policy makers can apply established tools from the governmental toolbox to this end. Alternatively, they can employ new tools that capitalize on the wealth of knowledge about human behavior and behavior change that has been accumulated in the behavioral sciences. Two contrasting approaches to behavior change are nudge policies and boost policies. (...)
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  • Familiarity‐Matching: An Ecologically Rational Heuristic for the Relationships‐Comparison Task.Masaru Shirasuna, Hidehito Honda, Toshihiko Matsuka & Kazuhiro Ueda - 2020 - Cognitive Science 44 (2):e12806.
    Previous studies have shown that people often use heuristics in making inferences and that subjective memory experiences, such as recognition or familiarity of objects, can be valid cues for inferences. So far, many researchers have used the binary choice task in which two objects are presented as alternatives (e.g., “Which city has the larger population, city A or city B?”). However, objects can be presented not only as alternatives but also in a question (e.g., “Which country is city X in, (...)
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  • Basic functional trade-offs in cognition: An integrative framework.Marco Del Giudice & Bernard J. Crespi - 2018 - Cognition 179 (C):56-70.
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  • Testing adaptive toolbox models: A Bayesian hierarchical approach.Benjamin Scheibehenne, Jörg Rieskamp & Eric-Jan Wagenmakers - 2013 - Psychological Review 120 (1):39-64.
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  • Processes models, environmental analyses, and cognitive architectures: Quo vadis quantum probability theory?Julian N. Marewski & Ulrich Hoffrage - 2013 - Behavioral and Brain Sciences 36 (3):297 - 298.
    A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.
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  • The interpretation of uncertainty in ecological rationality.Anastasia Kozyreva & Ralph Hertwig - 2019 - Synthese 198 (2):1517-1547.
    Despite the ubiquity of uncertainty, scientific attention has focused primarily on probabilistic approaches, which predominantly rely on the assumption that uncertainty can be measured and expressed numerically. At the same time, the increasing amount of research from a range of areas including psychology, economics, and sociology testify that in the real world, people’s understanding of risky and uncertain situations cannot be satisfactorily explained in probabilistic and decision-theoretical terms. In this article, we offer a theoretical overview of an alternative approach to (...)
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  • Cognitive architectures combine formal and heuristic approaches.Cleotilde Gonzalez & Christian Lebiere - 2013 - Behavioral and Brain Sciences 36 (3):285 - 286.
    Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.
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  • On the generality and cognitive basis of base-rate neglect.Elina Stengård, Peter Juslin, Ulrike Hahn & Ronald van den Berg - 2022 - Cognition 226 (C):105160.
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  • Knowledge Gaps: A Challenge for Agent‐Based Automatic Task Completion.Goonmeet Bajaj, Sean Current, Daniel Schmidt, Bortik Bandyopadhyay, Christopher W. Myers & Srinivasan Parthasarathy - 2022 - Topics in Cognitive Science 14 (4):780-799.
    The study of human cognition and the study of artificial intelligence (AI) have a symbiotic relationship, with advancements in one field often informing or creating new work in the other. Human cognition has many capabilities modern AI systems cannot compete with. One such capability is the detection, identification, and resolution of knowledge gaps (KGs). Using these capabilities as inspiration, we examine how to incorporate detection, identification, and resolution of KGs in artificial agents. We present a paradigm that enables research on (...)
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  • Predicting Short‐Term Remembering as Boundedly Optimal Strategy Choice.Andrew Howes, Geoffrey B. Duggan, Kiran Kalidindi, Yuan-Chi Tseng & Richard L. Lewis - 2016 - Cognitive Science 40 (5):1192-1223.
    It is known that, on average, people adapt their choice of memory strategy to the subjective utility of interaction. What is not known is whether an individual's choices are boundedly optimal. Two experiments are reported that test the hypothesis that an individual's decisions about the distribution of remembering between internal and external resources are boundedly optimal where optimality is defined relative to experience, cognitive constraints, and reward. The theory makes predictions that are tested against data, not fitted to it. The (...)
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  • The Adaptive Use of Recognition in Group Decision Making.Juliane E. Kämmer, Wolfgang Gaissmaier, Torsten Reimer & Carsten C. Schermuly - 2014 - Cognitive Science 38 (5):911-942.
    Applying the framework of ecological rationality, the authors studied the adaptivity of group decision making. In detail, they investigated whether groups apply decision strategies conditional on their composition in terms of task‐relevant features. The authors focused on the recognition heuristic, so the task‐relevant features were the validity of the group members' recognition and knowledge, which influenced the potential performance of group strategies. Forty‐three three‐member groups performed an inference task in which they had to infer which of two German companies had (...)
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  • Studies in Ecological Rationality.Ralph Hertwig, Christina Leuker, Thorsten Pachur, Leonidas Spiliopoulos & Timothy J. Pleskac - 2022 - Topics in Cognitive Science 14 (3):467-491.
    Topics in Cognitive Science, Volume 14, Issue 3, Page 467-491, July 2022.
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  • Memory‐Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models.Honda Hidehito, Matsuka Toshihiko & Ueda Kazuhiro - 2017 - Cognitive Science 41 (S5):1093-1118.
    Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge‐based inference depending on the subjective difficulty of (...)
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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 (C):102-122.
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  • Selecting decision strategies: The differential role of affect.Benjamin Scheibehenne & Bettina von Helversen - 2015 - Cognition and Emotion 29 (1):158-167.
    Many theories on cognition assume that people adapt their decision strategies depending on the situation they face. To test if and how affect guides the selection of decision strategies, we conducted an online study (N = 166), where different mood states were induced through video clips. Results indicate that mood influenced the use of decision strategies. Negative mood, in particular anger, facilitated the use of non-compensatory strategies, whereas positive mood promoted compensatory decision rules. These results are in line with the (...)
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  • (1 other version)The Accuracy–Coherence Trade-Off in Cognition.David Thorstad - 2024 - British Journal for the Philosophy of Science 75 (3):695-715.
    I argue that bounded agents face a systematic accuracy–coherence trade-off in cognition. Agents must choose whether to structure their cognition in ways likely to promote coherence or accuracy. I illustrate the accuracy–coherence trade-off by showing how it arises out of at least two component trade-offs: a coherence–complexity trade-off between coherence and cognitive complexity, and a coherence–variety trade-off between coherence and strategic variety. These trade-offs give rise to an accuracy–coherence trade-off because privileging coherence over complexity or strategic variety often leads to (...)
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  • Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.Hanna B. Fechner, Thorsten Pachur, Lael J. Schooler, Katja Mehlhorn, Ceren Battal, Kirsten G. Volz & Jelmer P. Borst - 2016 - Cognition 157 (C):77-99.
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  • Compound risk judgment in tasks with both idiosyncratic and systematic risk: The “Robust Beauty” of additive probability integration.Joakim Sundh & Peter Juslin - 2018 - Cognition 171 (C):25-41.
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