19 found
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  1.  22
    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.  8
    Learning and Dynamic Decision Making.Cleotilde Gonzalez - 2022 - Topics in Cognitive Science 14 (1):14-30.
    Topics in Cognitive Science, Volume 14, Issue 1, Page 14-30, January 2022.
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  3.  53
    Instance‐based learning in dynamic decision making.Cleotilde Gonzalez, Javier F. Lerch & Christian Lebiere - 2003 - Cognitive Science 27 (4):591-635.
    This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, (...)
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  4.  12
    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.
    The purpose of cognitive models is to make predictive simulations of human behaviour, but this is often done at the aggregate level. Cranford, Gonzalez, Aggarwal, Cooney, Tambe, and Lebiere show that they can automatically customize a model to a particular individual on‐the‐fly, and use it to make specific predictions about their next actions, in the context of a particular cybersecurity game.
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  5.  38
    How choice ecology influences search in decisions from experience.Tomás Lejarraga, Ralph Hertwig & Cleotilde Gonzalez - 2012 - Cognition 124 (3):334-342.
  6.  30
    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.  29
    Theory of Mind From Observation in Cognitive Models and Humans.Thuy Ngoc Nguyen & Cleotilde Gonzalez - 2022 - Topics in Cognitive Science 14 (4):665-686.
    A major challenge for research in artificial intelligence is to develop systems that can infer the goals, beliefs, and intentions of others (i.e., systems that have theory of mind, ToM). In this research, we propose a cognitive ToM framework that uses a well-known theory of decisions from experience to construct a computational representation of ToM. Instance-based learning theory (IBLT) is used to construct a cognitive model that generates ToM from the observation of other agents' behavior. The IBL model of the (...)
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  8.  41
    Managing the Budget: Stock‐Flow Reasoning and the CO 2 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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  9.  14
    Refuting data aggregation arguments and how the instance-based learning model stands criticism: A reply to Hills and Hertwig (2012).Cleotilde Gonzalez & Varun Dutt - 2012 - Psychological Review 119 (4):893-898.
  10.  15
    Creative Persuasion: A Study on Adversarial Behaviors and Strategies in Phishing Attacks.Prashanth Rajivan & Cleotilde Gonzalez - 2018 - Frontiers in Psychology 9.
  11. 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.
  12.  29
    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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  13.  15
    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.
  14.  7
    How to use a multicriteria comparison procedure to improve modeling competitions: A comment on Erev et al. (2017).Jason L. Harman, Michael Yu, Emmanouil Konstantinidis & Cleotilde Gonzalez - 2021 - Psychological Review 128 (5):995-1005.
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  15.  45
    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.
  16.  29
    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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  17.  13
    Towards a Cognitive Theory of Cyber Deception.Edward A. Cranford, Cleotilde Gonzalez, Palvi Aggarwal, Milind Tambe, Sarah Cooney & Christian Lebiere - 2021 - Cognitive Science 45 (7):e13013.
    This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present‐day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive (...)
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  18.  12
    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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  19.  9
    A Social Interpolation Model of Group Problem‐Solving.Sabina J. Sloman, Robert L. Goldstone & Cleotilde Gonzalez - 2021 - Cognitive Science 45 (12):e13066.
    How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an (...)
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