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Open AccessArticle
Multidimensional Representation Dynamics for Abstract Visual Objects in Encoded Tangram Paradigms
by
Yongxiang Lian
Yongxiang Lian
Yongxiang Lian is currently a Ph.D. candidate in the Department of Automation at Tsinghua under the [...]
Yongxiang Lian is currently a Ph.D. candidate in the Department of Automation at Tsinghua University, under the supervision of Professor Li Shi. He received his Bachelor's degree in Automation from Tsinghua University. His research interests lie at the intersection of artificial intelligence and neural mechanisms, focusing on computational modeling and cognitive neuroscience.
,
Shihao Pan
Shihao Pan
and
Li Shi
Li Shi
Li Shi is a Professor in the Department of Automation at Tsinghua University. Her research interests [...]
Li Shi is a Professor in the Department of Automation at Tsinghua University. Her research interests include vertical applications of large AI models, brain–computer interfaces, electrocardiographic simulation and clinical applications, as well as neural mechanisms in animal models. She leads interdisciplinary projects bridging artificial intelligence and biomedical engineering.
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Department of Automation, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Brain Sci. 2025, 15(9), 941; https://doi.org/10.3390/brainsci15090941 (registering DOI)
Submission received: 23 May 2025
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Revised: 24 August 2025
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Accepted: 27 August 2025
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Published: 28 August 2025
Abstract
Background: The human visual system is capable of processing large quantities of visual objects with varying levels of abstraction. The brain also exhibits hierarchical integration and learning capabilities that combine various attributes of visual objects (e.g., color, shape, local features, and categories) into coherent representations. However, prevailing theories in visual neuroscience employ simple stimuli or natural images with uncontrolled feature correlations, which constrains the systematic investigation of multidimensional representation dynamics. Methods: In this study, we aimed to bridge this methodological gap by developing a novel large tangram paradigm in visual cognition research and proposing cognitive-associative encoding as a mathematical basis. Critical representation dimensions—including animacy, abstraction level, and local feature density—were computed across a public dataset of over 900 tangrams, enabling the construction of a hierarchical model of visual representation. Results: Neural responses to 85 representative images were recorded using Electroencephalography (n = 24), and subsequent behavioral analyses and neural decoding revealed that distinct representational dimensions are independently encoded and dynamically expressed at different stages of cognitive processing. Furthermore, representational similarity analysis and temporal generalization analysis indicated that higher-order cognitive processes, such as “change of mind,” reflect the selective activation or suppression of local feature processing. Conclusions: These findings demonstrate that tangram stimuli, structured through cognitive-associative encoding, provide a generalizable computational framework for investigating the dynamic stages of human visual object cognition.
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MDPI and ACS Style
Lian, Y.; Pan, S.; Shi, L.
Multidimensional Representation Dynamics for Abstract Visual Objects in Encoded Tangram Paradigms. Brain Sci. 2025, 15, 941.
https://doi.org/10.3390/brainsci15090941
AMA Style
Lian Y, Pan S, Shi L.
Multidimensional Representation Dynamics for Abstract Visual Objects in Encoded Tangram Paradigms. Brain Sciences. 2025; 15(9):941.
https://doi.org/10.3390/brainsci15090941
Chicago/Turabian Style
Lian, Yongxiang, Shihao Pan, and Li Shi.
2025. "Multidimensional Representation Dynamics for Abstract Visual Objects in Encoded Tangram Paradigms" Brain Sciences 15, no. 9: 941.
https://doi.org/10.3390/brainsci15090941
APA Style
Lian, Y., Pan, S., & Shi, L.
(2025). Multidimensional Representation Dynamics for Abstract Visual Objects in Encoded Tangram Paradigms. Brain Sciences, 15(9), 941.
https://doi.org/10.3390/brainsci15090941
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