Perceptual learning - the improvement of performance through practice or training - has been observed over a wide range of perceptual tasks in adult humans. The mechanisms of learning, the level of learning, and the potential modes of perceptual learning remain important questions. Previous research suggests three key working hypotheses related to these fundamental issues of perceptual learning: (1) Perceptual learning is accomplished through two independent mechanisms of improving stimulus enhancement and external noise exclusion. (2) Perceptual learning is often accomplished through the incremental re-weighting of early sensory inputs to task-specific response selection without altering early sensory representations. (3) Perceptual learning is generally accomplished through the learning of incremental Hebbian associations, with and without external feedback. The two independent mechanisms of perceptual learning are revealed using external noise methods and an observer model framework (the Perceptual Template Model). We extend the model to understand the perceptual space - perceptual performance as a joint function of the magnitude of feature differences between stimuli, the contrast of the signal stimulus, and the contrast of external noise. A new task analysis classifies and interprets previous studies of specificity and transfer. We developed a sequential alternating task protocol to distinguish independent and competitive co-learning and to evaluate the reweighting hypothesis. An augmented Hebbian learning rule generates guiding predictions about the importance of feedback in training protocols with different mixtures of trial difficulty. The study of the three working hypotheses provides a theoretical framework within which to understand a number of classical phenomena in the perceptual learning literature. The empirical results will provide significant constraints on the theories and practical implementation of perceptual learning in normal populations. The model framework may be applied to characterize perceptual limitations. Understanding these processes in normal adults will form the basis of possible applications to developmental learning and to ameliorative training in populations with perceptual deficits. ? ? ?
| 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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