Electrophysiological Correlates of the Effect of Task Difficulty on Inhibition of Return

Li, Ai-Su; Miao, Chengguo; Han, Yu; He, Xun; Zhang, Yang

doi:10.3389/fpsyg.2018.02403

Cited by 7 publications

(4 citation statements)

References 62 publications

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“…EEG was analysed using a customized toolbox (mfeeg: http://sourceforge.net/p/mfeeg) programmed with MATLAB (Mathworks, Natick, MA, USA). The topographic maps were generated with a customized MATLAB function based on EEGLAB (Delorme and Makeig, 2004;Li et al, 2018). Continuous EEG recordings were bandpass-filtered from 1 to 30 Hz and cut into six epochs (30 s each).…”

Section: Eeg Datamentioning

confidence: 99%

Effects of Monocular Perceptual Learning on Binocular Visual Processing in Adolescent and Adult Amblyopia

Deng

Feng

et al. 2020

iScience

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Re-establishing normal binocular visual processing is the key to amblyopia recovery beyond the critical period of visual development. Here, by combining perceptual learning, behavioral testing, and steady-state visually evoked potentials (SSVEPs), we examined how monocular perceptual learning in the amblyopic eye could change binocular visual processing in the adolescent and adult amblyopic visual system. We found that training reduced the interocular difference between amblyopic and fellow eyes and increased the amplitude of a binocular SSVEP component, with a significant negative correlation between the two measures. Our results demonstrate that training in the amblyopic eye primarily improves binocular rather than monocular visual processing in the amblyopic visual system, suggesting that behavioral training could potentially address key neural deficits in adolescent and adult amblyopia.

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Section: Eeg Datamentioning

confidence: 99%

Effects of Monocular Perceptual Learning on Binocular Visual Processing in Adolescent and Adult Amblyopia

Deng

Feng

et al. 2020

iScience

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“…The results of statistical analysis were shown in Figure 3. The topographic map was generated with Yang's topoplot_bcl function based on EEGLAB's topoplot function (Li et al, 2018). The asterisk indicates that the p-value is less than 0.01.…”

Section: Statistical Resultsmentioning

confidence: 99%

Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials

Zhang

Zhu

et al. 2021

Front. Aging Neurosci.

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Backgrounds: Nowadays, risks of Cognitive Impairment (CI) [highly suspected Alzheimer's disease (AD) in this study] threaten the quality of life for more older adults as the population ages. The emergence of Transcranial Magnetic Stimulation-Electroencephalogram (TMS-EEG) enables noninvasive neurophysiological investi-gation of the human cortex, which might be potentially used for CI detection.Objectives: The aim of this study is to explore whether the spatiotemporal features of TMS Evoked Potentials (TEPs) could classify CI from healthy controls (HC).Methods: Twenty-one patients with CI and 22 HC underwent a single-pulse TMS-EEG stimulus in which the pulses were delivered to the left dorsolateral prefrontal cortex (left DLPFC). After preprocessing, seven regions of interest (ROIs) and two most reliable TEPs' components: N100 and P200 were selected. Next, seven simple and interpretable linear features of TEPs were extracted for each region, three common machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used to detect CI. Meanwhile, data augmentation and voting strategy were used for a more robust model. Finally, the performance differences of features in classifiers and their contributions were investigated.Results: 1. In the time domain, the features of N100 had the best performance in the SVM classifier, with an accuracy of 88.37%. 2. In the aspect of spatiality, the features of the right frontal region and left parietal region had the best performance in the SVM classifier, with an accuracy of 83.72%. 3. The Local Mean Field Power (LMFP), Average Value (AVG), Latency and Amplitude contributed most in classification.Conclusions: The TEPs induced by TMS over the left DLPFC has significant differences spatially and temporally between CI and HC. Machine learning based on the spatiotemporal features of TEPs have the ability to separate the CI and HC which suggest that TEPs has potential as non-invasive biomarkers for CI diagnosis.

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“…In the meta-analysis from Paus et al (1998), a ranking of tasks by difficulty was equivalent to a ranking of working memory demands. Difficulty can be defined in relation to accuracy, efficiency, performance linked to reaction and response times, and it thus often reflects a consequence on behavior and not a mechanism or a specific property (Li et al, 2018). A high level of difficulty is usually associated with low accuracy and long reaction or response time, suggesting that the cognitive or attentional effort required in more difficult tasks is higher.…”

Section: Definitions Of Difficultymentioning

confidence: 99%

The Differential Weights of Motivational and Task Performance Measures on Medial and Lateral Frontal Neural Activity

Goussi-Denjean,

Fontanier,

Stoll

et al. 2023

J. Neurosci.

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Behavioral adaptations are triggered by different constraints given by rules, and are informed by outcomes, or motivational changes. Neural activity in multiple frontal areas is modulated during behavioral adaptations but the source of these modulations and the nature of the mechanisms involved are unclear. Here we tested how different variables related to changes in task performance and to behavioral adaptation impact the amplitude of event-related local field potentials (LFP) in the lateral prefrontal cortex and midcingulate cortex of male rhesus macaques. We found that the behavioral task used induced consistently different types of performance modulation: in relation to task difficulty (imposed by the experimental setup), to successes and errors, and to the time spent in the task. Difficulty had a significant effect on monkeys’ accuracy and reaction times. Interestingly, there is also a strong interaction between difficulty and trial success on the reaction times variation. However, LFP modulations were mostly related to reaction times, touch position, feedback valence and time-in-session, with little, if any, effect of difficulty. Hence difficulty modulated performance but not LFP activity. This suggests that in our experimental design, execution, regulation and motivation-related factors are the main factors influencing medial and lateral frontal activity.Significance Statement:Adapting decisions might be determined by several mechanisms and might be driven by motivational factors and/or factors inherent to the task at hand. Multiple frontal areas contribute to behavioral adaptations. One current challenge is to understand which information they process contributes to behavioral changes. Diverging views have emerged on whether task demands, like the decision difficulty, or factors linked to incentives to adapt, are driving frontal activity. Here we show that task difficulty had a strong effect on performance (accuracy and reaction times) but little effect on LFP recorded in monkey lateral prefrontal and midcingulate cortex. However, information related to actions, outcome valence and time-in-session had major influences. Thus, task difficulty modulated performance but not LFP activity in frontal areas.

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Electrophysiological Correlates of the Effect of Task Difficulty on Inhibition of Return

Cited by 7 publications

References 62 publications

Effects of Monocular Perceptual Learning on Binocular Visual Processing in Adolescent and Adult Amblyopia

Effects of Monocular Perceptual Learning on Binocular Visual Processing in Adolescent and Adult Amblyopia

Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials

The Differential Weights of Motivational and Task Performance Measures on Medial and Lateral Frontal Neural Activity

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