Training-based improvements in visual performance are one possible non-invasive approach for remediation for a range of visual impairments. The last several decades of research in perceptual learning has demonstrated a remarkable ability of training or practice to enhance perception in the adult human, and has greatly improved our understanding of the associated plasticity in the human brain. Now is the time to use our understanding of the mechanisms and specificity of perceptual learning to develop predictive theories and new training paradigms that improve the efficiency or magnitude of perceptual improvement and its generalization. In this proposal, we develop a research program to systematically investigate how to improve the efficiency and magnitude of learning in a given task, how to improve the immediate generalization of those improvements to related tasks and stimuli, and how to improve the ability of the individual to learn new tasks. Improvements in perceptual task performance through perceptual learning or training, and the extent of transfer to related conditions, both depend critically upon the training protocol and the mixture of stimuli and tasks being trained. The current research uses computational models of visual perceptual learning, new and extended training and testing protocols, efficient estimation methods, and empirical tests to improve our understanding of the principles of learning that can be used to design protocols that enhance efficient learning and generalization. The goal of this research program is to develop the theories and practical implementation of perceptual learning in normal populations that could contribute to translational applications to developmental learning and to ameliorative training in populations with perceptual deficits.
These aims are consistent with the goals of the National Eye Institute.
Perceptual learning through training visual tasks can contribute to enhancing visual skills and may prove useful in remediation of some visual limitations. The current project seeks to understand the conditions that produce strong learning with broader generalization, and evaluates working memory training in learning to learn. The proposed program of model development and empirical testing will support the development of a framework for developing different training regimens in normal adults, and should suggest parallel applications in rehabilitative or developmental training.
| Zhang, Pan; Hou, Fang; Yan, Fang-Fang et al. (2018) High reward enhances perceptual learning. J Vis 18:11 |
| Baek, Jongsoo; Lesmes, Luis Andres; Lu, Zhong-Lin (2016) qPR: An adaptive partial-report procedure based on Bayesian inference. J Vis 16:25 |
| Lu, Zhong-Lin; Lin, Zhicheng; Dosher, Barbara Anne (2016) Translating Perceptual Learning from the Laboratory to Applications. Trends Cogn Sci 20:561-563 |
| Lin, Zhicheng; Lu, Zhong-Lin; He, Sheng (2016) Decomposing experience-driven attention: Opposite attentional effects of previously predictive cues. Atten Percept Psychophys 78:2185-98 |
| Lin, Zhicheng; Lu, Zhong-Lin (2016) Automaticity of phasic alertness: Evidence for a three-component model of visual cueing. Atten Percept Psychophys 78:1948-67 |
| Cabrera, Carlos Alexander; Lu, Zhong-Lin; Dosher, Barbara Anne (2015) Separating decision and encoding noise in signal detection tasks. Psychol Rev 122:429-60 |
| Tlapale, Émilien; Dosher, Barbara Anne; Lu, Zhong-Lin (2015) Construction and evaluation of an integrated dynamical model of visual motion perception. Neural Netw 67:110-20 |
| Zhou, Jiawei; Yan, Fangfang; Lu, Zhong-Lin et al. (2015) Broad bandwidth of perceptual learning in second-order contrast modulation detection. J Vis 15:20 |
| Liu, Jiajuan; Dosher, Barbara Anne; Lu, Zhong-Lin (2015) Augmented Hebbian reweighting accounts for accuracy and induced bias in perceptual learning with reverse feedback. J Vis 15:10 |
| Kawato, Mitsuo; Lu, Zhong-Lin; Sagi, Dov et al. (2014) Perceptual learning--the past, present and future. Vision Res 99:1-4 |
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