17 found
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  1.  41
    Bayesian generic priors for causal learning.Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng & Keith J. Holyoak - 2008 - Psychological Review 115 (4):955-984.
  2.  13
    Probabilistic analogical mapping with semantic relation networks.Hongjing Lu, Nicholas Ichien & Keith J. Holyoak - 2022 - Psychological Review 129 (5):1078-1103.
  3.  29
    Bayesian analogy with relational transformations.Hongjing Lu, Dawn Chen & Keith J. Holyoak - 2012 - Psychological Review 119 (3):617-648.
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  4.  44
    A predictive coding perspective on autism spectrum disorders.Jeroen J. A. van Boxtel & Hongjing Lu - 2013 - Frontiers in Psychology 4.
  5.  35
    A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.Hongjing Lu, Randall R. Rojas, Tom Beckers & Alan L. Yuille - 2016 - Cognitive Science 40 (2):404-439.
    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that (...)
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  6.  14
    Is interpolation cognitively encapsulated? Measuring the effects of belief on Kanizsa shape discrimination and illusory contour formation.Brian P. Keane, Hongjing Lu, Thomas V. Papathomas, Steven M. Silverstein & Philip J. Kellman - 2012 - Cognition 123 (3):404-418.
  7.  11
    Parts beget parts: Bootstrapping hierarchical object representations through visual statistical learning.Alan L. F. Lee, Zili Liu & Hongjing Lu - 2021 - Cognition 209 (C):104515.
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  8.  17
    Causal actions enhance perception of continuous body movements.Yujia Peng, Nicholas Ichien & Hongjing Lu - 2020 - Cognition 194:104060.
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  9.  7
    Impaired Global, and Compensatory Local, Biological Motion Processing in People with High Levels of Autistic Traits.Jeroen J. A. van Boxtel & Hongjing Lu - 2013 - Frontiers in Psychology 4.
  10.  30
    Generative Inferences Based on Learned Relations.Dawn Chen, Hongjing Lu & Keith J. Holyoak - 2017 - Cognitive Science 41 (S5):1062-1092.
    A key property of relational representations is their generativity: From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from non-relational inputs. In the present paper, we show that a bottom-up model of relation learning, initially developed to discriminate between positive and negative examples of comparative relations, can be extended to make generative inferences. (...)
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  11.  11
    Two Computational Approaches to Visual Analogy: Task‐Specific Models Versus Domain‐General Mapping.Nicholas Ichien, Qing Liu, Shuhao Fu, Keith J. Holyoak, Alan L. Yuille & Hongjing Lu - 2023 - Cognitive Science 47 (9):e13347.
    Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to (...)
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  12.  78
    Perception of Human Interaction Based on Motion Trajectories: From Aerial Videos to Decontextualized Animations.Tianmin Shu, Yujia Peng, Lifeng Fan, Hongjing Lu & Song-Chun Zhu - 2018 - Topics in Cognitive Science 10 (1):225-241.
    People are adept at perceiving interactions from movements of simple shapes, but the underlying mechanism remains unknown. Previous studies have often used object movements defined by experimenters. The present study used aerial videos recorded by drones in a real-life environment to generate decontextualized motion stimuli. Motion trajectories of displayed elements were the only visual input. We measured human judgments of interactiveness between two moving elements and the dynamic change in such judgments over time. A hierarchical model was developed to account (...)
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  13.  13
    The Impact of Autistic Traits on Self-Recognition of Body Movements.Joseph M. Burling, Akila Kadambi, Tabitha Safari & Hongjing Lu - 2019 - Frontiers in Psychology 9.
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  14. Uncertainty in causal inference: The case of retrospective revaluation.Christopher D. Carroll, Patricia W. Cheng & Hongjing Lu - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  15.  28
    What the Bayesian framework has contributed to understanding cognition: Causal learning as a case study.Keith J. Holyoak & Hongjing Lu - 2011 - Behavioral and Brain Sciences 34 (4):203-204.
    The field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models.
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  16.  3
    For deep networks, the whole equals the sum of the parts.Philip J. Kellman, Nicholas Baker, Patrick Garrigan, Austin Phillips & Hongjing Lu - 2023 - Behavioral and Brain Sciences 46:e396.
    Deep convolutional networks exceed humans in sensitivity to local image properties, but unlike biological vision systems, do not discover and encode abstract relations that capture important properties of objects and events in the world. Coupling network architectures with additional machinery for encoding abstract relations will make deep networks better models of human abilities and more versatile and capable artificial devices.
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  17.  13
    Bayesian integration of position and orientation cues in perception of biological and non-biological forms.Steven M. Thurman & Hongjing Lu - 2014 - Frontiers in Human Neuroscience 8.