14 found
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  1.  12
    Instance-Based Learning: Integrating Sampling and Repeated Decisions From Experience.Cleotilde Gonzalez & Varun Dutt - 2011 - Psychological Review 118 (4):523-551.
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  2.  37
    Instance‐Based Learning in Dynamic Decision Making.Cleotilde Gonzalez, Javier F. Lerch & Christian Lebiere - 2003 - Cognitive Science 27 (4):591-635.
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  3.  18
    How Choice Ecology Influences Search in Decisions From Experience.Tomás Lejarraga, Ralph Hertwig & Cleotilde Gonzalez - 2012 - Cognition 124 (3):334-342.
  4.  37
    Managing the Budget: Stock‐Flow Reasoning and the CO2 Accumulation Problem.Ben R. Newell, Arthur Kary, Chris Moore & Cleotilde Gonzalez - 2016 - Topics in Cognitive Science 8 (1):138-159.
    The majority of people show persistent poor performance in reasoning about “stock-flow problems” in the laboratory. An important example is the failure to understand the relationship between the “stock” of CO2 in the atmosphere, the “inflow” via anthropogenic CO2 emissions, and the “outflow” via natural CO2 absorption. This study addresses potential causes of reasoning failures in the CO2 accumulation problem and reports two experiments involving a simple re-framing of the task as managing an analogous financial budget. In Experiment 1 a (...)
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  5.  11
    Creative Persuasion: A Study on Adversarial Behaviors and Strategies in Phishing Attacks.Prashanth Rajivan & Cleotilde Gonzalez - 2018 - Frontiers in Psychology 9.
  6.  13
    A Cognitive Model of Dynamic Cooperation With Varied Interdependency Information.Cleotilde Gonzalez, Noam Ben-Asher, Jolie M. Martin & Varun Dutt - 2015 - Cognitive Science 39 (3):457-495.
    We analyze the dynamics of repeated interaction of two players in the Prisoner's Dilemma under various levels of interdependency information and propose an instance-based learning cognitive model to explain how cooperation emerges over time. Six hypotheses are tested regarding how a player accounts for an opponent's outcomes: the selfish hypothesis suggests ignoring information about the opponent and utilizing only the player's own outcomes; the extreme fairness hypothesis weighs the player's own and the opponent's outcomes equally; the moderate fairness hypothesis weighs (...)
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  7.  4
    Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models.Edward A. Cranford, Cleotilde Gonzalez, Palvi Aggarwal, Sarah Cooney, Milind Tambe & Christian Lebiere - 2020 - Topics in Cognitive Science 12 (3):992-1011.
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  8.  5
    Refuting Data Aggregation Arguments and How the Instance-Based Learning Model Stands Criticism: A Reply to Hills and Hertwig.Cleotilde Gonzalez & Varun Dutt - 2012 - Psychological Review 119 (4):893-898.
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  9.  36
    Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness.Norbou Buchler, Sean M. Fitzhugh, Laura R. Marusich, Diane M. Ungvarsky, Christian Lebiere & Cleotilde Gonzalez - 2016 - Frontiers in Psychology 7.
  10.  20
    Making Sense of Dynamic Systems: How Our Understanding of Stocks and Flows Depends on a Global Perspective.Helen Fischer & Cleotilde Gonzalez - 2016 - Cognitive Science 40 (2):496-512.
    Stocks and flows are building blocks of dynamic systems: Stocks change through inflows and outflows, such as our bank balance changing with withdrawals and deposits, or atmospheric CO2 with absorptions and emissions. However, people make systematic errors when trying to infer the behavior of dynamic systems, termed SF failure, whose cognitive explanations are yet unknown. We argue that SF failure appears when people focus on specific system elements, rather than on the system structure and gestalt. Using a standard SF task, (...)
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  11.  18
    Framing From Experience: Cognitive Processes and Predictions of Risky Choice.Cleotilde Gonzalez & Katja Mehlhorn - 2016 - Cognitive Science 40 (5):1163-1191.
    A framing bias shows risk aversion in problems framed as “gains” and risk seeking in problems framed as “losses,” even when these are objectively equivalent and probabilities and outcomes values are explicitly provided. We test this framing bias in situations where decision makers rely on their own experience, sampling the problem's options and seeing the outcomes before making a choice. In Experiment 1, we replicate the framing bias in description-based decisions and find risk indifference in gains and losses in experience-based (...)
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  12. The Description–Experience Gap in Risky and Ambiguous Gambles.Varun Dutt, Horacio Arlo-Costa, Jeffrey Helzner & Cleotilde Gonzalez - 2014 - Journal of Behavioral Decision Making 27 (4):316-327.
  13.  9
    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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  14.  2
    Cognitive Models in Cybersecurity: Learning From Expert Analysts and Predicting Attacker Behavior.Vladislav D. Veksler, Norbou Buchler, Claire G. LaFleur, Michael S. Yu, Christian Lebiere & Cleotilde Gonzalez - 2020 - Frontiers in Psychology 11.
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