scholarly journals The Time Course of Neural Changes Underlying Auditory Perceptual Learning

2002 ◽  
Vol 9 (3) ◽  
pp. 138-150 ◽  
Author(s):  
M. Atienza
Keyword(s):  
Perceptual Learning ◽  
Time Course

scholarly journals Assessing the detailed time course of perceptual sensitivity change in perceptual learning

2019 ◽  
Vol 19 (5) ◽  
pp. 9 ◽  
Author(s):  
Pan Zhang ◽  
Yukai Zhao ◽  
Barbara Anne Dosher ◽  
Zhong-Lin Lu
Keyword(s):  
Perceptual Learning ◽  
Time Course ◽  
Perceptual Sensitivity ◽  
Sensitivity Change

The time course of auditory perceptual learning

Neuroreport ◽  
1998 ◽  
Vol 9 (16) ◽  
pp. 3557-3560 ◽  
Author(s):  
K Tremblay ◽  
N Kraus ◽  
T McGee
Keyword(s):  
Perceptual Learning ◽  
Time Course

scholarly journals Improving the performance of the amblyopic visual system

2008 ◽  
Vol 364 (1515) ◽  
pp. 399-407 ◽  
Author(s):  
Dennis M Levi ◽  
Roger W Li
Keyword(s):  
Visual System ◽  
Perceptual Learning ◽  
Neural Plasticity ◽  
Time Course ◽  
Clinical Evidence ◽  
Sensory Function ◽  
Sensitive Period ◽  
Visual Improvement ◽  
Perceptual Performance ◽  
Effectiveness And Efficiency

Experience-dependent plasticity is closely linked with the development of sensory function; however, there is also growing evidence for plasticity in the adult visual system. This review re-examines the notion of a sensitive period for the treatment of amblyopia in the light of recent experimental and clinical evidence for neural plasticity. One recently proposed method for improving the effectiveness and efficiency of treatment that has received considerable attention is ‘perceptual learning’. Specifically, both children and adults with amblyopia can improve their perceptual performance through extensive practice on a challenging visual task. The results suggest that perceptual learning may be effective in improving a range of visual performance and, importantly, the improvements may transfer to visual acuity. Recent studies have sought to explore the limits and time course of perceptual learning as an adjunct to occlusion and to investigate the neural mechanisms underlying the visual improvement. These findings, along with the results of new clinical trials, suggest that it might be time to reconsider our notions about neural plasticity in amblyopia.

scholarly journals The time-course of rapid stimulus-specific perceptual learning

2013 ◽  
Vol 13 (9) ◽  
pp. 1090-1090
Author(s):  
A. Hashemi ◽  
J. W. Lass ◽  
D. Truong ◽  
A. B. Sekuler ◽  
P. J. Bennett
Keyword(s):  
Perceptual Learning ◽  
Time Course

scholarly journals Different Dynamics of Performance and Brain Activation in the Time Course of Perceptual Learning

Neuron ◽  
2008 ◽  
Vol 57 (6) ◽  
pp. 827-833 ◽  
Author(s):  
Yuko Yotsumoto ◽  
Takeo Watanabe ◽  
Yuka Sasaki
Keyword(s):  
Perceptual Learning ◽  
Time Course ◽  
Brain Activation

scholarly journals Top-down modulation of sensory cortex gates perceptual learning

2017 ◽  
Vol 114 (37) ◽  
pp. 9972-9977 ◽  
Author(s):  
Melissa L. Caras ◽  
Dan H. Sanes
Keyword(s):  
Perceptual Learning ◽  
Cortical Neurons ◽  
Time Course ◽  
Behavioral Plasticity ◽  
Behavioral Outcomes ◽  
Brain Regions ◽  
Top Down ◽  
Behavioral Sensitivity ◽  
Perceptual Performance ◽  
Sensory Encoding

Practice sharpens our perceptual judgments, a process known as perceptual learning. Although several brain regions and neural mechanisms have been proposed to support perceptual learning, formal tests of causality are lacking. Furthermore, the temporal relationship between neural and behavioral plasticity remains uncertain. To address these issues, we recorded the activity of auditory cortical neurons as gerbils trained on a sound detection task. Training led to improvements in cortical and behavioral sensitivity that were closely matched in terms of magnitude and time course. Surprisingly, the degree of neural improvement was behaviorally gated. During task performance, cortical improvements were large and predicted behavioral outcomes. In contrast, during nontask listening sessions, cortical improvements were weak and uncorrelated with perceptual performance. Targeted reduction of auditory cortical activity during training diminished perceptual learning while leaving psychometric performance largely unaffected. Collectively, our findings suggest that training facilitates perceptual learning by strengthening both bottom-up sensory encoding and top-down modulation of auditory cortex.

Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course

2013 ◽  
Vol 109 (2) ◽  
pp. 344-362 ◽  
Author(s):  
Hansem Sohn ◽  
Sang-Hun Lee
Keyword(s):  
Perceptual Learning ◽  
Time Course ◽  
Temporal Dynamics ◽  
Interval Timing ◽  
Time Interval ◽  
Psychophysical Experiment ◽  
Subjective Time ◽  
Sensory Stimuli ◽  
Accuracy And Precision ◽  
Observer Model

Our brain is inexorably confronted with a dynamic environment in which it has to fine-tune spatiotemporal representations of incoming sensory stimuli and commit to a decision accordingly. Among those representations needing constant calibration is interval timing, which plays a pivotal role in various cognitive and motor tasks. To investigate how perceived time interval is adjusted by experience, we conducted a human psychophysical experiment using an implicit interval-timing task in which observers responded to an invisible bar drifting at a constant speed. We tracked daily changes in distributions of response times for a range of physical time intervals over multiple days of training with two major types of timing performance, mean accuracy and precision. We found a decoupled dynamics of mean accuracy and precision in terms of their time course and specificity of perceptual learning. Mean accuracy showed feedback-driven instantaneous calibration evidenced by a partial transfer around the time interval trained with feedback, while timing precision exhibited a long-term slow improvement with no evident specificity. We found that a Bayesian observer model, in which a subjective time interval is determined jointly by a prior and likelihood function for timing, captures the dissociative temporal dynamics of the two types of timing measures simultaneously. Finally, the model suggested that the width of the prior, not the likelihoods, gradually shrinks over sessions, substantiating the important role of prior knowledge in perceptual learning of interval timing.

scholarly journals Increased Signal Complexity Improves the Breadth of Generalization in Auditory Perceptual Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
David J. Brown ◽  
Michael J. Proulx
Keyword(s):  
Perceptual Learning ◽  
Time Course ◽  
Frequency Interval ◽  
Complex Signals ◽  
Perceptual Training ◽  
Standard Interval ◽  
Effective Training ◽  
Human Adults ◽  
Signal Complexity

Perceptual learning can be specific to a trained stimulus or optimally generalized to novel stimuli with the breadth of generalization being imperative for how we structure perceptual training programs. Adapting an established auditory interval discrimination paradigm to utilise complex signals, we trained human adults on a standard interval for either 2, 4, or 10 days. We then tested the standard, alternate frequency, interval, and stereo input conditions to evaluate the rapidity of specific learning and breadth of generalization over the time course. In comparison with previous research using simple stimuli, the speed of perceptual learning and breadth of generalization were more rapid and greater in magnitude, including novel generalization to an alternate temporal interval within stimulus type. We also investigated the long term maintenance of learning and found that specific and generalized learning was maintained over 3 and 6 months. We discuss these findings regarding stimulus complexity in perceptual learning and how they can inform the development of effective training protocols.

Time Course of Auditory Perceptual Learning

1980 ◽  
Vol 89 (5_suppl) ◽  
pp. 96-102 ◽  
Author(s):  
Charles S. Watson PhD
Keyword(s):  
Perceptual Learning ◽  
Time Course ◽  
Residual Hearing ◽  
Stimulus Uncertainty ◽  
Complex Sounds

The time course of auditory perceptual learning can vary from one hour to more than a year, depending on the task, the complexity of the sounds, and the level of stimulus uncertainty under which the tasks are learned. Previous studies are reviewed which show that longer training is required to identify sounds than to discriminate between them, while the least time-consuming task is to learn to detect a sound's presence. The time course of each of these tasks is greatly extended for complex sounds compared to those for single tones. The learning of a speech-like code would thus be expected to require longer training than that employed in previous psychoacoustic research. The consequences of this fact for the exploitation of residual hearing are briefly discussed.

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