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  1. 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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  • Extending SME to Handle Large‐Scale Cognitive Modeling.Kenneth D. Forbus, Ronald W. Ferguson, Andrew Lovett & Dedre Gentner - 2017 - Cognitive Science 41 (5):1152-1201.
    Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...)
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  • Representation and Computation in Cognitive Models.Kenneth D. Forbus, Chen Liang & Irina Rabkina - 2017 - Topics in Cognitive Science 9 (3):694-718.
    One of the central issues in cognitive science is the nature of human representations. We argue that symbolic representations are essential for capturing human cognitive capabilities. We start by examining some common misconceptions found in discussions of representations and models. Next we examine evidence that symbolic representations are essential for capturing human cognitive capabilities, drawing on the analogy literature. Then we examine fundamental limitations of feature vectors and other distributed representations that, despite their recent successes on various practical problems, suggest (...)
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  • AI and Cognitive Science: The Past and Next 30 Years.Kenneth D. Forbus - 2010 - Topics in Cognitive Science 2 (3):345-356.
    Artificial Intelligence (AI) is a core area of Cognitive Science, yet today few AI researchers attend the Cognitive Science Society meetings. This essay examines why, how AI has changed over the last 30 years, and some emerging areas of potential interest where AI and the Society can go together in the next 30 years, if they choose.
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