Research in perceptual learning has demonstrated a remarkable ability of training or practice to enhance perception in the adult human. The last thirty years have yielded many important findings about how people learn, what limits transfer, how generalization can be improved, how to model learning, and the nature of visual plasticity. At the same time, learning and transfer have been measured at a relatively coarse scale that leads to relatively inaccurate measures of learning in individuals, which could be very important to choosing adapted training options. Related issues of estimation have also limited the types of training protocols that have been studied. The objective of this research is to use innovative new adaptive performance assessment (based on Bayesian principles) to provide unbiased and high precision estimates of learning in individuals. We also use computational neural network models to generate predictions about more complicated training regimens that are then tested experimentally. We develop a framework for searching among these predictions computationally to identify better (optimized) training methods. The long-term goal is to develop efficient new assessments of learning and transfer and the modeling techniques that may then be applied to improve clinical applications, rehabilitation, and perceptual expertise identified as key aspects of the NEI mission.
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 perceptual learning and generalization using new rapid assessment methods, advanced statistical methods, and computational models of learning. The proposed program of model and test development and empirical testing will help to define a framework for more reliably measuring learning in individuals and predicting the efficacy of training regimens in normal adults, that could be the basis of 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 |
Showing the most recent 10 out of 46 publications
