Predicting Perceptual Performance From Neural Activity

Individuals improve with practice on a variety of perceptual tasks, presumably reflecting plasticity in underlying neural mechanisms. We trained observers to discriminate biological motion from scrambled (nonbiological) motion and examined whether the resulting improvement in perceptual performance was accompanied by changes in activation within the posterior superior temporal sulcus and the fusiform “face area,” brain areas involved in perception of biological events. With daily practice, initially naive observers became more proficient at discriminating biological from scrambled animations embedded in an array of dynamic “noise” dots, with the extent of improvement varying among observers. Learning generalized to animations never seen before, indicating that observers had not simply memorized specific exemplars. In the same observers, neural activity prior to and following training was measured using functional magnetic resonance imaging. Neural activity within the posterior superior temporal sulcus and the fusiform “face area” reflected the participants' learning: BOLD signals were significantly larger after training in response both to animations experienced during training and to novel animations. The degree of learning was positively correlated with the amplitude changes in BOLD signals.

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Predicting Perceptual Performance From Neural Activity

Advances in Understanding Human Performance ◽

10.1201/ebk1439835012-29 ◽

2010 ◽

pp. 281-290

Keyword(s):

Neural Activity ◽

Perceptual Performance

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Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers

NeuroImage ◽

10.1016/j.neuroimage.2010.03.030 ◽

2010 ◽

Vol 51 (4) ◽

pp. 1425-1437 ◽

Cited By ~ 42

Author(s):

Koel Das ◽

Barry Giesbrecht ◽

Miguel P. Eckstein

Keyword(s):

Neural Activity ◽

Perceptual Performance

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ROC-Based Estimates of Neural-Behavioral Covariations Using Matched Filters

Neural Computation ◽

10.1162/neco_a_00616 ◽

2014 ◽

Vol 26 (8) ◽

pp. 1667-1689

Author(s):

Kamal Farah ◽

Jackson E. T. Smith ◽

Erik P. Cook

Keyword(s):

Neural Activity ◽

Cross Validation ◽

Matched Filter ◽

Visually Guided ◽

Functional Link ◽

Mt Neurons ◽

Visual Inputs ◽

Perceptual Performance ◽

And Behavior ◽

Short Time

Correlations between responses in visual cortex and perceptual performance help draw a functional link between neural activity and visually guided behavior. These correlations are commonly derived with ROC-based neural-behavioral covariances (referred to as choice or detect probability) using boxcar analysis windows. Although boxcar windows capture the covariation between neural activity and behavior during steady-state stimulus presentations, they are not optimized to capture these correlations during short time-varying visual inputs. In this study, we implemented a matched-filter technique, combined with cross-validation, to improve the estimation of ROC-based neural-behavioral covariance under short and dynamic stimulus conditions. We show that this approach maximizes the area under the ROC curve and converges to the true neural-behavioral covariance using a Poisson spiking model. We also demonstrate that the matched filter, combined with cross-validation, reveals the dynamics of the neural-behavioral covariations of individual MT neurons during the detection of a brief motion stimulus.

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Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning

10.1101/2021.04.08.438941 ◽

2021 ◽

Author(s):

Maarten A Immink ◽

Zachariah R Cross ◽

Alex Chatburn ◽

James Baumeister ◽

Matthias Schlesewsky ◽

...

Keyword(s):

Individual Differences ◽

Reaction Time ◽

Neural Activity ◽

Resting State ◽

Neural Dynamics ◽

Perceptual Sensitivity ◽

Initial Exposure ◽

Task Practice ◽

Perceptual Performance ◽

Visuomotor Performance

An emerging body of work has demonstrated that resting-state non-oscillatory, or aperiodic, 1/f neural activity is a functional and behaviorally relevant marker of cognitive function capacity. In the motor domain, previous work has only applied 1/f analyses to description of action coordination and performance. The value of aperiodic resting-state neural dynamics as a marker of individual visuomotor performance capacity remains unknown. Accordingly, the aim of this work was to investigate if individual 1/f intercept and slope parameters of aperiodic resting-state neural activity predict reaction time and perceptual sensitivity in an immersive virtual reality marksmanship task. The marksmanship task required speeded selection of target stimuli and avoidance of non-target stimuli selection. Motor and perceptual demands were incrementally increased across task blocks and participants performed the task across three training sessions spanning one week. When motor demands were high, steeper individual 1/f slope predicted shorter reaction time. This relationship did not change with practice. Increased 1/f intercept and a steeper 1/f slope were associated with higher perceptual sensitivity, measured as d′. However, this association was only observed under the highest levels of perceptual demand and only in the initial exposure to these conditions. Individuals with a lower 1/f intercept and a shallower 1/f slope demonstrated the greatest gains in perceptual sensitivity from task practice. These findings demonstrate that individual differences in motor and perceptual performance can be accounted for with resting-state aperiodic neural dynamics. The 1/f aperiodic parameters are most informative in predicting visuomotor performance under complex and demanding task conditions. In addition to predicting capacity for high visuomotor performance with a novel task, 1/f aperiodic parameters might also be useful in predicting which individuals might derive the most improvements from practice.

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Can Behavioral Speech-In-Noise Tests Improve the Quality of Hearing Aid Fittings?

Perspectives on Audiology ◽

10.1044/poa7.1.8 ◽

2011 ◽

Vol 7 (1) ◽

pp. 8-14

Author(s):

Robert Moore ◽

Susan Gordon-Hickey

Keyword(s):

Working Memory ◽

Speech Perception ◽

Performance Test ◽

Hearing Aid ◽

Speech Perception In Noise ◽

Speech In Noise ◽

Acceptable Noise Level ◽

Perceptual Performance ◽

Noise Test

The purpose of this article is to propose 4 dimensions for consideration in hearing aid fittings and 4 tests to evaluate those dimensions. The 4 dimensions and tests are (a) working memory, evaluated by the Revised Speech Perception in Noise test (Bilger, Nuetzel, & Rabinowitz, 1984); (b) performance in noise, evaluated by the Quick Speech in Noise test (QSIN; Killion, Niquette, Gudmundsen, Revit, & Banerjee, 2004); (c) acceptance of noise, evaluated by the Acceptable Noise Level test (ANL; Nabelek, Tucker, & Letowski, 1991); and (d) performance versus perception, evaluated by the Perceptual–Performance test (PPT; Saunders & Cienkowski, 2002). The authors discuss the 4 dimensions and tests in the context of improving the quality of hearing aid fittings.

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