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195 results on '"Zhang, Ru-Yuan"'

4. A computationally rational analysis of response strategy in a probability learning task

Author

Fang, Zeming, Zhang, Ru-Yuan, and Sims, Chris R

Subjects
Psychology, Behavioral Science, Decision making, Learning, Computational Modeling
Abstract

Intelligent behavior requires the ability to adapt to an ever-changing environment. But are humans rational or normative in this ability? We apply a resource-rational analysis to the data from a probability learning task (Gagne et al., 2020). Our analysis hypothesizes that people seek to maximize the expected utility of behavior, while simultaneously minimizing the complexity of their behavioral policies. We report evidence consistent with this hypothesis. We also show that people adopt simpler policies in situations of greater environmental stability, and interpret this as a consequence of reward maximization.

Published
2022

5. Action video game play facilitates learning to learn.

Author

Zhang, Ru-Yuan, Chopin, Adrien, Shibata, Kengo, Lu, Zhong-Lin, Jaeggi, Susanne, Buschkuehl, Martin, Green, C, and Bavelier, Daphne

Subjects
Adult, Attention, Cognition, Humans, Learning, Middle Aged, Reaction Time, Task Performance and Analysis, Video Games, Visual Perception, Young Adult
Abstract

Previous work has demonstrated that action video game training produces enhancements in a wide range of cognitive abilities. Here we evaluate a possible mechanism by which such breadth of enhancement could be attained: that action game training enhances learning rates in new tasks (i.e., learning to learn). In an initial controlled intervention study, we show that individuals who were trained on action video games subsequently exhibited faster learning in the two cognitive domains that we tested, perception and working memory, as compared to individuals who trained on non-action games. We further confirmed the causal effect of action video game play on learning ability in a pre-registered follow-up study that included a larger number of participants, blinding, and measurements of participant expectations. Together, this work highlights enhanced learning speed for novel tasks as a mechanism through which action video game interventions may broadly improve task performance in the cognitive domain.

Published
2021

32. Rapid increase in depression within the first month of the Shanghai Covid lockdown in 2022

Author

Ma, Xianda, primary, He, Xinyi, additional, Wu, Mengyu, additional, Kuang, Yifang, additional, Zeng, Zuhua, additional, Zhao, Binglei, additional, Wang, Lihui, additional, Li, Yi, additional, Xue, Meihua, additional, Sang, Hong, additional, Shang, Mengnan, additional, Zhang, Xu, additional, Wang, Shuting, additional, ZHANG, RU-YUAN, additional, and Li, Weidong, additional

Published
2022
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35. NeuroGym: An open resource for developing and sharing neuroscience tasks

Author

Molano-Mazón, Manuel, Barbosa, Joao, Pastor-Ciurana, Jordi, Fradera, Marta, Zhang, Ru-Yuan, Forest, Jeremy, del Pozo, Jorge, Ji-An, Li, Cueva, Christopher J, de la Rocha, Jaime, Narain, Devika, Yang, Guangyu Robert, Molano-Mazón, Manuel, Barbosa, Joao, Pastor-Ciurana, Jordi, Fradera, Marta, Zhang, Ru-Yuan, Forest, Jeremy, del Pozo, Jorge, Ji-An, Li, Cueva, Christopher J, de la Rocha, Jaime, Narain, Devika, and Yang, Guangyu Robert

Abstract

Artificial Neural Networks (ANNs) trained on specific cognitive tasks have re-emerged as a useful tool to study the brain. However, ANNs would better aid cognitive neuroscience if a given network could be easily trained on a wide range of tasks for which neural recordings are available. Moreover, unintentional divergent implementations of cognitive tasks can produce variable results, which limits their interpretability. Towards this goal, we present NeuroGym, an open-source Python package that provides a large collection of customizable neuroscience tasks to test and compare network models. Building upon the OpenAI Gym toolbox, NeuroGym tasks (1) are written in a high-level flexible Python framework; (2) possess a shared interface tailored to common needs of neuroscience tasks that facilitates their design and usage; (3) support the training of ANNs using both Reinforcement and Supervised Learning techniques. The toolbox allows easy assembly of new tasks by modifying existing ones in a hierarchical and modular fashion. These design features make it straightforward to take a network designed for one task and train it on many other tasks. NeuroGym is a community-driven effort that contributes to a rapidly evolving open ecosystem of neural network development, data analysis, and model-data comparison.

Published
2022

37. NeuroGym: An open resource for developing and sharing neuroscience tasks

Author

Molano-Mazon, Manuel, primary, Barbosa, Joao, additional, Pastor-Ciurana, Jordi, additional, Fradera, Marta, additional, ZHANG, RU-YUAN, additional, Forest, Jeremy, additional, del Pozo Lerida, Jorge, additional, Ji-An, Li, additional, Cueva, Christopher J, additional, de la Rocha, Jaime, additional, Narain, Devika, additional, and Yang, Guangyu Robert, additional

Published
2022
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41. LAMOST observations in the K2 fields: project and observation progress

Author

Fu Jian Ning, Smith Martin C., Zhang Ru Yuan, Ren An Bing, Shi Jian Rong, Luo A Li, and Zhang Hao Tong

Subjects
Physics, QC1-999
Abstract

To complement the time-series observations of the Kepler space mission in the K2 fields, spectroscopic observations for hundreds of thousands of stars in these fields are hugely important. LAMOST, a Chinese 4-meter class telescope equipped with 4000 fibers on the focal plane, is an ideal facility to fulfill this task. The LAMOST-K2 project was approved in the autumn of 2015 and observations commenced during the 2015-2016 winter season. The project will initially cover 6 of the first 10 observed K2 campaigns, and may be extended to include later K2 campaigns. We describe the project and introduce the current progress of observation with this poster presentation.

Published
2017
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45. Functional reorganization of visual processing following long-term adaptation to optical defects

Author

Barbot, Antoine, Park, Woon-Ju, Zhang, Ru-Yuan, Huxlin, Krystel R, Tadin, Duje, and Yoon, Geunyoung

Subjects
genetic structures, sense organs, eye diseases
Abstract

Long-term exposure to poor ocular optics chronically alters the retinal input to the brain. Elucidating how this chronic change affects visual processing is vital for understanding the capacity for and limits of sensory plasticity in adults. Using adaptive optics to bypass the eye’s optical defects, we assessed visual processing in neurotypically-developed adults with keratoconus—a corneal disease resulting in severe optical aberrations during adulthood. When tested under similar fully-corrected optical conditions as neurotypical observers, KC patients showed altered contrast sensitivity, with impaired sensitivity for fine spatial details and better sensitivity for coarse details. Both gains and losses in sensitivity were more pronounced in patients with poorer habitual optical quality, and were mediated by changes in signal enhancement mechanisms. The present findings uncover fundamental properties of neural compensation mechanisms in response to long-term exposure to optical defects, with a large-scale functional reorganization favoring sensory information less affected by the eye’s optics.

Published
2020
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