12 found
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  1. Updating beliefs in light of uncertain evidence: Descriptive assessment of Jeffrey's rule.Daniel Osherson & Jiaying Zhao - 2010 - Thinking and Reasoning 16 (4):288-307.
    Jeffrey (1983) proposed a generalization of conditioning as a means of updating probability distributions when new evidence drives no event to certainty. His rule requires the stability of certain conditional probabilities through time. We tested this assumption (“invariance”) from the psychological point of view. In Experiment 1 participants offered probability estimates for events in Jeffrey’s candlelight example. Two further scenarios were investigated in Experiment 2, one in which invariance seems justified, the other in which it does not. Results were in (...)
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  2. Updating: Learning versus supposing.Jiaying Zhao, Vincenzo Crupi, Katya Tentori, Branden Fitelson & Daniel Osherson - 2012 - Cognition 124 (3):373-378.
  3.  6
    Motivated Attention in Climate Change Perception and Action.Yu Luo & Jiaying Zhao - 2019 - Frontiers in Psychology 10.
  4.  97
    On the provenance of judgments of conditional probability.Jiaying Zhao, Anuj Shah & Daniel Osherson - 2009 - Cognition 113 (1):26-36.
  5.  70
    Vision and abstraction: an empirical refutation of Nico Orlandi’s non-cognitivism.Christopher Mole & Jiaying Zhao - 2016 - Philosophical Psychology 29 (3):365-373.
    This article argues against the non-cognitivist theory of vision that has been formulated in the work of Nico Orlandi. It shows that, if we understand ‘representation’ in the way Orlandi recommends, then the visual system’s response to abstract regularities must involve the formation of representations. Recent experiments show that those representations must be used by the visual system in the production of visual experiences. Their effects cannot be explained by taking them to be non-visual effects involving attention or memory. This (...)
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  6.  8
    Statistical regularities reduce perceived numerosity.Jiaying Zhao & Ru Qi Yu - 2016 - Cognition 146:217-222.
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  7.  47
    Category-based updating.Jiaying Zhao & Daniel Osherson - 2014 - Thinking and Reasoning 20 (1):1-15.
  8.  7
    A Framework on Polarization, Cognitive Inflexibility, and Rigid Cognitive Specialization.James Shyan-Tau Wu, Christoph Hauert, Claire Kremen & Jiaying Zhao - 2022 - Frontiers in Psychology 13.
    Polarization is pervasive in the current sociopolitical discourse. Polarization tends to increase cognitive inflexibility where people become less capable of updating their beliefs upon new information or switching between different ways of thinking. Cognitive inflexibility can in turn increase polarization. We propose that this positive feedback loop between polarization and cognitive inflexibility is a form of threat response that has benefited humans throughout their evolutionary history. This feedback loop, which can be driven by conflict mindset, group conformity, and simplification of (...)
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  9.  12
    The “item” as a window into how prior knowledge guides visual search.Rachel Wu & Jiaying Zhao - 2017 - Behavioral and Brain Sciences 40.
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  10.  5
    Implicit updating of object representation via temporal associations.Ru Qi Yu & Jiaying Zhao - 2018 - Cognition 181 (C):127-134.
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  11. Detecting deviations from randomness.Jiaying Zhao & Daniel Osherson - unknown
    We explore the ability to distinguish random from non-random events. Randomness is defined in terms of radioactive decay whereas non-randomness is quantified by excess repetitions (“repeat”) or alternations (“switch”) between successive bits. In the first four experiments no mention was made of randomness, probability, or related concepts in task instructions. We found superior performance in distinguishing random stimuli from repeat stimuli compared to switch stimuli. The last three experiments explicitly evoked the concept of randomness, thus allowing comparison of perceptual and (...)
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  12.  3
    i-Frame interventions enhance s-frame interventions.Jiaying Zhao & Frances S. Chen - 2023 - Behavioral and Brain Sciences 46:e180.
    We argue that i-frame interventions can and do increase support for systemic reforms, and s-frame interventions should be pursued in parallel to address key societal issues. Without accompanying i-frame interventions, s-frame interventions can fail. We offer an operant conditioning framework to generate positive spillover effects. Behavioral scientists should develop i-frame interventions that enhance, rather than compete with, s-frame interventions.
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