Feedback in Hypothesis Testing: An ERP Study

2003 ◽  
Vol 15 (4) ◽  
pp. 508-522 ◽  
Author(s):  
David Papo ◽  
Pierre-Marie Baudonniére ◽  
Laurent Hugueville ◽  
Jean-Paul Caverni
Keyword(s):  
College Students ◽  
Principal Component Analysis ◽  
Hypothesis Testing ◽  
Negative Feedback ◽  
Performance Feedback ◽  
Principal Component ◽  
Event Related Potentials ◽  
Related Potentials ◽  
Positive And Negative Feedback ◽  
Effects Of Feedback

We used event-related potentials (ERPs) to probe the effects of feedback in a hypothesis testing (HT) paradigm. Thirteen college students serially tested hypotheses concerning a hidden rule by judging its presence or absence in triplets of digits and revised them on the basis of an exogenous performance feedback. ERPs time-locked to performance feedback were then examined. The results showed differences between responses to positive and negative feedback at all cortical sites. Negative feedback, indicating incorrect performance, was associated to a negative deflection preceding a P300-like wave. Spatiotemporal principal component analysis (PCA) showed the interplay between early frontal components and later central and posterior ones. Lateralization of activity was selectively detectable at frontal sites, with a left frontal dominance for both positive and negative feedback. These results are discussed in terms of a proposed computational model of trial-to-trial feedback in HT in which the cognitive and emotive aspects of feedback are explicitly linked to putative mediating brain mechanisms. The properties of different feedback types and feedback-related deficits in depression are also discussed.

scholarly journals On Optimal Allocation of Treatment/Condition Variance in Principal Component Analysis

2018 ◽  
Vol 7 (4) ◽  
pp. 50
Author(s):  
André Beauducel ◽  
Norbert Hilger
Keyword(s):  
Principal Component Analysis ◽  
Treatment Condition ◽  
Optimal Allocation ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Necessary Condition ◽  
Condition Effect ◽  
Loading Patterns ◽  
Related Potentials

The allocation of a (treatment) condition-effect on the wrong principal component (misallocation of variance) in principal component analysis (PCA) has been addressed in research on event-related potentials of the electroencephalogram. However, the correct allocation of condition-effects on PCA components might be relevant in several domains of research. The present paper investigates whether different loading patterns at each condition-level are a basis for an optimal allocation of between-condition variance on principal components. It turns out that a similar loading shape at each condition-level is a necessary condition for an optimal allocation of between-condition variance, whereas a similar loading magnitude is not necessary.

A Preliminary Investigation of ERP Components of Attentional Bias in Anxious Adults Using Temporospatial Principal Component Analysis

2021 ◽  
pp. 1-14
Author(s):  
Resh S. Gupta ◽  
Autumn Kujawa ◽  
David R. Vago
Keyword(s):  
Principal Component Analysis ◽  
Attentional Bias ◽  
Preliminary Investigation ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Dot Probe ◽  
Face Pair ◽  
Related Potentials ◽  
Angry Face

Abstract. Threat-related attention bias is thought to contribute to the development and maintenance of anxiety disorders. Dot-probe studies using event-related potentials (ERPs) have indicated that several early ERP components are modulated by threatening and emotional stimuli in anxious populations, suggesting enhanced allocation of attention to threat and emotion at earlier stages of processing. However, ERP components selected for examination and analysis in these studies vary widely and remain inconsistent. The present study used temporospatial principal component analysis (PCA) to systematically identify ERP components elicited to face pair cues and probes in a dot-probe task in anxious adults. Cue-locked components sensitive to emotion included an early occipital C1 component enhanced for happy versus angry face pair cues and an early parieto-occipital P1 component enhanced for happy versus angry face pair cues. Probe-locked components sensitive to congruency included a parieto-occipital P2 component enhanced for incongruent probes (probes replacing neutral faces) versus congruent probes (probes replacing emotional faces). Split-half correlations indicated that the mean value around the PCA-derived peaks was reliably measured in the ERP waveforms. These results highlight promising neurophysiological markers for attentional bias research that can be extended to designs comparing anxious and healthy comparison groups. Results from a secondary exploratory PCA analysis investigating the effects of emotional face position and analyses on behavioral reaction time data are also presented.

scholarly journals Artifact Removing From Eeg Recordings Using Independent Component Analysis with High-order Statistics

2021 ◽  
Vol 15 ◽  
pp. 76-85
Author(s):  
Theodor D. Popescu
Keyword(s):  
Principal Component Analysis ◽  
Eye Movements ◽  
Independent Component Analysis ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Independent Component ◽  
Related Potentials ◽  
Eeg Recordings ◽  
High Order Statistics

Many methods have been proposed to remove artifacts from EEG recordings especially those arising from eye movements and blinks. Often regression in time and frequency domain on parallel EEG and electrooculographic recordings is used, but this approach can become problematic in some cases. Use of Principal Component Analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. This method is not effective when the activations from cerebral activity and artifacts have comparable amplitudes. In this paper it is presented a generally applicable method for removing a wide variety of artifacts from EEG recordings based on Independent Component Analysis (ICA) with highorder statistics. The method is applied with good results in the analysis of a sample lowpass event -related potentials (ERP) data.

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