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Updated: Mar 20, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
Translating Perceptual Learning from the Laboratory to Applications.
Zhong-Lin Lu1, Zhicheng Lin1, Barbara Anne Dosher2
1Center for Cognitive and Brain Sciences, Center for Cognitive and Behavioral Brain Imaging, and Department of Psychology, The Ohio State University, Columbus, OH 43210, USA.
Laboratory training shows brain plasticity and improved perception, driving the development of training apps. This study explores translating these findings from research settings to clinical and commercial applications.
Area of Science:
- Neuroscience
- Cognitive Science
- Human-Computer Interaction
Background:
- Laboratory studies demonstrate significant brain plasticity and perceptual performance enhancements through human training.
- These findings have spurred interest in developing accessible training applications and systems.
Purpose of the Study:
- To outline the critical next steps for translating laboratory-based training paradigms into clinical and commercial settings.
- To bridge the gap between scientific discovery and practical application in perceptual training.
Main Methods:
- Review and synthesis of existing human training studies focusing on brain plasticity and perceptual learning.
- Analysis of challenges and opportunities in the translation of these findings.
- Consideration of factors for successful implementation in clinical and commercial contexts.
Main Results:
- Identified key translational hurdles including standardization, efficacy validation, and user accessibility.
- Highlighted the potential for widespread application of perceptual training interventions.
- Emphasized the need for interdisciplinary collaboration between researchers, clinicians, and developers.
Conclusions:
- Successful translation requires addressing methodological, regulatory, and market-related challenges.
- The potential benefits of perceptual training warrant continued efforts toward clinical and commercial realization.
- Future directions include robust validation and scalable deployment of training systems.
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