Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict.

paper Cited Public

Authors: Cohen, Michael X; Cavanagh, James F
Year: 2011
Journal: Frontiers in psychology
PMID: 21713190
DOI: 10.3389/fpsyg.2011.00030
PMCID: PMC3111011

Figure 1

Conditions (x-axis) are labeled according to previous (first, lowercase letter) and current trial (second, uppercase letter) conflict: C = congruent, I = incongruent. (A) Reaction times in ms. (B) Spearman's rank correlation coefficients. Error bars denote SEM.

LLM interpretation

This figure consists of two bar charts comparing four conditions (cC, cI, iC, iI) based on trial conflict. Panel A shows reaction times in milliseconds, with the cI condition exhibiting the highest mean value. Panel B displays Spearman's rank correlation coefficients with luminance, where the cC condition shows the lowest correlation. Both panels include error bars representing the standard error of the mean (SEM).

Figure 10

Comparison of theta-band effects (trial-averaged power, power regression with reaction time, phase modulation by reaction time). Gray shading indicates that the regression coefficients are significantly greater than trial-averaged power at p < 0.01, minimum 300 ms continuous cluster threshold. Red and blue circles indicate subject-specific waveform peak times.

LLM interpretation

This line graph displays the time series of theta-band (4–8 Hz) effect sizes relative to peri-response time (ms). Three conditions are compared: trial-averaged power (blue), power regression with reaction time (red), and phase modulation (gray). A gray shaded region indicates where regression coefficients are significantly greater than trial-averaged power (p < 0.01), and red and blue circles mark subject-specific peak times.

Figure 2

Trial-averaged results, separated according to condition. (A,B) Condition-averaged topographical maps of power and phase coherence, which are the average of 150 ms surrounding the time point indicated below the maps. (C,D) Time–frequency plots of peri-response power and inter-trial phase coherence. Black areas enclose regions in which contiguous pixels were significantly different from inter-trial-interval baseline at p < 0.01 (two-tailed) for at least 300 ms and at least three consecutive frequencies.

LLM interpretation

This figure presents EEG data across four conditions (cC, cI, iC, iI). Panels A and B show topographical maps of power and phase coherence at three time points (-400, -200, and 0 ms), with intensity increasing toward the 0 ms mark. Panels C and D display time-frequency plots for power and cross-trial phase coherence at the FCz electrode, with black outlines denoting regions significantly different from baseline (p < 0.01).

Figure 3

Time–frequency plots of peri-response (time = 0) single-trial multiple regression coefficients, separated by condition (rows) and regression term (A–C). Black lines enclose significant regions, as in Figure 2. Time–frequency (TF) regression coefficient plots are taken from electrodes indicated by fuchsia circles.

LLM interpretation

This figure presents a grid of EEG topographic maps and corresponding time-frequency (TF) plots of multiple regression coefficients across four conditions (cC, cI, iC, iI) and three regression terms: Reaction time (A), Luminance (B), and the interaction RT*Lum (C). The topographic maps show spatial distributions of coefficients, while the TF plots display frequency (Hz) over time (ms), with black outlines marking significant regions. Fuchsia circles on the topographies indicate the specific electrodes used to generate the adjacent TF plots.

Figure 4

Individual subject single-trial data illustrating the relationship between theta power (x-axis) and RT (y-axis). Axes differ per subject and are unlabeled. Each point is a single trial; lines reflect the best linear fit. Red pluses and lines are taken from cI trials, blue circles and lines from cC trials. These data correspond to the regression from a single time–frequency point for each subject. To select this subject-specific time–frequency point, we averaged the RT regression coefficients from all conditions and selected the point with the largest coefficient within the range of 1–12 Hz, −400 to 200 ms (the average across subjects was −233 ms and 5.1 Hz). Note that this selection procedure is not based on maximizing differences between cI and cC trials.

LLM interpretation

This figure consists of a grid of 15 scatter plots showing the relationship between FCz theta power (x-axis) and reaction time (y-axis) for individual subjects. Each plot contains single-trial data points and linear fit lines for two conditions: cI trials (red pluses and lines) and cC trials (blue circles and lines). Most plots exhibit a positive correlation between theta power and reaction time across both conditions.

Figure 5

Time–frequency plots of peri-response single-trial phase modulation, separated by condition. Black lines enclose significant regions, as in Figure 2. Note the differences between these phase modulation plots – where the dominant effects occur in the theta band over frontal regions – and the trial-averaged phase coherence in Figure 2, which shows a dominant delta-band effect without a medial frontal focus.

LLM interpretation

This figure consists of four rows of paired visualizations (topographic maps and time-frequency plots) for four conditions: cC, cI, iC, and iI. The topographic maps show spatial distributions of phase modulation across the scalp, while the corresponding time-frequency plots display phase modulation across time (-500 to 0 ms) and frequency (2 to 31 Hz). Black outlines in the time-frequency plots highlight significant regions of modulation, primarily concentrated in the theta band (approximately 4–7 Hz) over frontal regions.

Figure 6

Phase synchronization between electrode FCz (over MFC) and lateral prefrontal sites including F6 is present generally prior to the response, and, during high conflict situations, increases in strength with increasing reaction time. (A) Time–frequency plots of FCz–F6 “standard” (no modulation) phase synchronization (left column) and synchronization modulated by reaction time, luminance, and their interaction (right three columns). (B) Topographical maps of reaction time-modulated synchronization with FCz over time (columns) and condition (rows). Statistical thresholding is the same as in previous figures.

LLM interpretation

Figure A consists of time-frequency plots showing phase synchronization between electrodes FCz and F6 across four conditions (cC, cL, iC, iL), comparing "Standard" synchronization (left) to synchronization modulated by reaction time, luminance, and their interaction (right). Figure B displays topographical maps of reaction time-modulated synchronization for the same conditions, plotted across pre-response time intervals from -500 ms to the response. Both panels use color scales to represent relative phase locking or modulation coefficients, with black outlines in Figure A indicating statistically significant clusters.

Figure 7

Selection of independent components for all subjects. Components were selected based on spatial correlation with a priori specified templates (left column). The average component across subjects was similar to the templates (middle column). Individual maps from all 15 subjects are shown in the right-most column.

LLM interpretation

This figure displays topographic scalp maps organized into three rows, each representing a different independent component. The columns show a reference "Template," the "Average maps" across subjects, and the "Individual subjects" maps for 15 participants. The visualizations demonstrate the spatial similarity between the a priori templates, the group average, and the individual subject distributions.

Figure 8

Trial-averaged power results from the independent components (same as Figure 2C but using components instead of electrode-specific data).

LLM interpretation

This figure consists of a grid of time-frequency spectrograms showing trial-averaged power (dB) for three independent components: Left frontal, Medial frontal, and Right frontal. The x-axis represents time (ms) and the y-axis represents frequency (Hz), with data divided into four conditions: cC, cI, iC, and iI. The Medial frontal component shows the most prominent increase in power (red areas) in the 2–7 Hz range, with significant clusters outlined in black across all conditions.

Figure 9

Single-trial multiple regression analyses from independent components. The analysis was identical to that presented in Figure 3 but using components time courses instead of electrode-specific data.

LLM interpretation

This figure consists of a grid of time-frequency heatmaps showing regression coefficients for three frontal components (Left, Medial, Right) across four conditions (cc, cl, ic, ii). The x-axis represents time (ms) from -500 to 0, and the y-axis represents frequency (Hz) on a logarithmic scale. The heatmaps compare the effects of "Reaction time," "Luminance," and their interaction ("RT*lum"), with significant clusters outlined by black borders.

#	Section	Preview
0	The “Many-to-Many Mapping” Problem of Cognitive Neuroscience	In this report, we demonstrate how single-trial multiple regression analyses can help elucidate…
1	The “Many-to-Many Mapping” Problem of Cognitive Neuroscience	Experimental or behavioral variables that vary from trial to trial are often ignored or amalgamated,…
2	Correlating Single-Trial Brain and Behavior Dynamics	Although most cognitive neuroscience studies use cross-trial averaging, there are many examples of…
3	A Dynamic Solution: Single-Trial Multiple Regression	The approach we advocate here is an extension of the single-trial correlation approach. Multiple…
4	A Dynamic Solution: Single-Trial Multiple Regression	Single-trial multiple regression has been applied to EEG data. For example, multiple regression of…
5	Response Conflict: a Trial-Varying Phenomena that is Often Averaged over in Experiments	Response conflict occurs when multiple response options are activated, but only one should be…
6	Response Conflict: a Trial-Varying Phenomena that is Often Averaged over in Experiments	Activation of the cognitive control system (e.g., during response conflict) can be measured through…
7	Response Conflict: a Trial-Varying Phenomena that is Often Averaged over in Experiments	In typical cognitive control experiments, response conflict is induced either at the response, the…
8	Response Conflict: a Trial-Varying Phenomena that is Often Averaged over in Experiments	It is clear that response conflict is not an all-or-none phenomenon in the brain. Given that the…
9	Response Conflict: a Trial-Varying Phenomena that is Often Averaged over in Experiments	Egner, 2007), but to the extent that they reflect internally experience conflict, reaction times can…
10	Methods — Subjects	Seventeen subjects from the University of Amsterdam community (aged: 18–31, two male) participated…
11	Methods — Task	A modified flankers task was used, in which subjects responded to a central target letter while…
12	Methods — Task	Luminance was randomly selected on each trial to be between 0 and 200 (RGB values that vary between…
13	Methods — Task	As discussed in the Section “Introduction,” response conflict effects depend on whether conflict…
14	Methods — Eeg data collection	EEG data were acquired at 512 Hz from 64 channels placed according to the international 10–20…
15	Methods — Eeg analyses: time–frequency decomposition	All analyses were performed in matlab. Single-trial data were first decomposed into their…
16	Methods — Eeg analyses: time–frequency decomposition	allow edge artifacts due to sudden transitions in signal values between trials to subside outside…
17	Methods — Eeg analyses: time–frequency decomposition	trials; Lachaux et al., 1999; Delorme and Makeig, 2004). Frequency band-specific phase…
18	Methods — Eeg analyses: single-trial regression	Single-trial analyses were conducted separately for power and phase. For power analyses, a robust…
19	Methods — Eeg analyses: single-trial regression	minimize the contribution of potential outliers via iterative reweighted least squares that…

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The interplay of cognitive control and feature integration: insights from theta oscillatory dynamics during conflict processing.	Pastötter B et al.	—	2024	→
The phase coherence of cortical oscillations predicts dynamic changes in perceived visibility.	Akdogan I et al.	—	2024	→
The promoting effect of the absence of second-party's punishment power on third-party punishment in maintaining social fairness norms: An EEG hyper-scanning study.	Liu Y et al.	—	2024	→
Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research.	Tan E et al.	—	2024	→
Theta oscillatory dynamics serving cognitive control index psychosocial distress in youth.	Schantell M et al.	—	2024	→
When the brain says "No!": An MRI study on the neural correlates of resistance to immoral orders.	Tricoche L et al.	—	2024	→
Abnormal Reactivity of Brain Oscillations to Visual Search Target in Children With Attention-Deficit/Hyperactivity Disorder.	Guo J et al.	—	2023	→
Aging attenuated theta response during multistable perception.	Küçük KM et al.	—	2023	→
A negative emotional context disrupts the framing effect on outcome evaluation in decision making under uncertainty: An ERP study.	Tao R et al.	—	2023	→
Beyond Infant's Looking: The Neural Basis for Infant Prediction Errors.	Berger A et al.	—	2023	→
Dissociable feedback valence effects on frontal midline theta during reward gain versus threat avoidance learning.	Stolz C et al.	—	2023	→
Distinguishing Fine Structure and Summary Representation of Sound Textures from Neural Activity.	Berto M et al.	—	2023	→
Flexible Changes in Attentional Focus and Task Rules Rely on A Shared Set of Frontoparietal Oscillatory Dynamics.	Kaiser J et al.	—	2023	→
Functional Magnetic Resonance Imaging Signatures of Pavlovian and Instrumental Valuation Systems during a Modified Orthogonalized Go/No-go Task.	Queirazza F et al.	—	2023	→
High (130 Hz)- and mid (60 Hz)-frequency deep brain stimulation in the subthalamic nucleus differentially modulate response inhibition: A preliminary combined EEG and eye tracking study.	Waldthaler J et al.	—	2023	→
Midfrontal mechanisms of performance monitoring continuously adapt to incoming information during outcome anticipation.	Lange L et al.	—	2023	→
Midfrontal theta as an index of conflict strength in approach-approach vs avoidance-avoidance conflicts.	Levy A et al.	—	2023	→
Oscillatory Activities in Multiple Frequency Bands in Patients with Schizophrenia During Motion Perception.	Başar-Eroğlu C et al.	—	2023	→
Pre-Stimulus Power but Not Phase Predicts Prefrontal Cortical Excitability in TMS-EEG.	Poorganji M et al.	—	2023	→
Reward network dysfunction is associated with cognitive impairment after stroke.	Wagner F et al.	—	2023	→
Social conformity is associated with inter-trial electroencephalogram variability.	Zhang H et al.	—	2023	→
Temporal dynamics of oscillatory activity during nonlexical language decoding: Evidence from Morse code and magnetoencephalography.	Junker FB et al.	—	2023	→
Two modes of midfrontal theta suggest a role in conflict and error processing.	Muralidharan V et al.	—	2023	→
Updating the relationship of the Ne/ERN to task-related behavior: A brief review and suggestions for future research.	LoTemplio SB et al.	—	2023	→
A multidimensional evaluation of the benefits of an ecologically realistic training based on pretend play for preschoolers' cognitive control and self-regulation: From behavior to the underlying theta neuro-oscillatory activity.	Adam N et al.	—	2022	→
An Action-Independent Role for Midfrontal Theta Activity Prior to Error Commission.	Estiveira J et al.	—	2022	→
A neuronal theta band signature of error monitoring during integration of facial expression cues.	Dias C et al.	—	2022	→
Are there consistent abnormalities in event-related EEG oscillations in patients with Alzheimer's disease compared to other diseases belonging to dementia?	Güntekin B et al.	—	2022	→
Auricular Transcutaneous Vagus Nerve Stimulation Diminishes Alpha-Band-Related Inhibitory Gating Processes During Conflict Monitoring in Frontal Cortices.	Konjusha A et al.	—	2022	→
Brain Dynamics of Action Monitoring in Higher-Order Motor Control Disorders: The Case of Apraxia.	Spinelli G et al.	—	2022	→
Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value.	Xi J et al.	—	2022	→
Evoked Acute Stress Alters Frontal Midline Neural Oscillations Affecting Behavioral Inhibition in College Students.	Wu X et al.	—	2022	→
Improving cognitive control: Is theta neurofeedback training associated with proactive rather than reactive control enhancement?	Eschmann KCJ et al.	—	2022	→
Inter-trial theta phase consistency during face processing in infants is associated with later emerging autism.	van Noordt S et al.	—	2022	→
Low pre-stimulus EEG alpha power amplifies visual awareness but not visual sensitivity.	Benwell CSY et al.	—	2022	→
Misleading Robot Signals in a Classification Task Induce Cognitive Load as Measured by Theta Synchronization Between Frontal and Temporo-parietal Brain Regions.	Abubshait A et al.	—	2022	→
On the cognitive mechanisms supporting prosocial disobedience in a post-genocidal context.	Caspar EA et al.	—	2022	→
Perceived gaze direction affects recollection processes in recognition of concrete and abstract words: electrophysiological evidence.	Chen Y et al.	—	2022	→
Regular cannabis use modulates the impact of HIV on the neural dynamics serving cognitive control.	Schantell M et al.	—	2022	→
Striatal BOLD and Midfrontal Theta Power Express Motivation for Action.	Algermissen J et al.	—	2022	→
The Average Reward Rate Modulates Behavioral and Neural Indices of Effortful Control Allocation.	Lin H et al.	—	2022	→
To see, not to see or to see poorly: Perceptual quality and guess rate as a function of electroencephalography (EEG) brain activity in an orientation perception task.	Sheldon SS et al.	—	2022	→
Visual working memory recruits two functionally distinct alpha rhythms in posterior cortex.	Rodriguez-Larios J et al.	—	2022	→
Circadian Sleep-Activity Rhythm across Ages in Down Syndrome.	Lovos A et al.	—	2021	→
Cognitive Control Promotes Either Honesty or Dishonesty, Depending on One's Moral Default.	Speer SP et al.	—	2021	→
Conjoint fluctuations of PFC-mediated processes and behavior: An investigation of error-related neural mechanisms in relation to sustained attention.	Chidharom M et al.	—	2021	→
Dual brain stimulation enhances interpersonal learning through spontaneous movement synchrony.	Pan Y et al.	—	2021	→
Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms.	Cavanagh JF et al.	—	2021	→
Enhancing neural markers of attention in children with ADHD using a digital therapeutic.	Gallen CL et al.	—	2021	→
Frontal midline theta differentiates separate cognitive control strategies while still generalizing the need for cognitive control.	Eisma J et al.	—	2021	→
Frontal-midline theta reflects different mechanisms associated with proactive and reactive control of inhibition.	Messel MS et al.	—	2021	→
Fronto-subthalamic phase synchronization and cross-frequency coupling during conflict processing.	Zeng K et al.	—	2021	→
Machine learning evaluates changes in functional connectivity under a prolonged cognitive load.	Frolov N et al.	—	2021	→
Mediofrontal theta-band oscillations reflect top-down influence in the ventriloquist illusion.	Kaiser M et al.	—	2021	→
Motor Interference, But Not Sensory Interference, Increases Midfrontal Theta Activity and Brain Synchronization during Reactive Control.	Kaiser J et al.	—	2021	→
Neural oscillatory responses to performance monitoring differ between high- and low-impulsive individuals, but are unaffected by TMS.	Barth B et al.	—	2021	→
Parametric Cortical Representations of Complexity and Preference for Artistic and Computer-Generated Fractal Patterns Revealed by Single-Trial EEG Power Spectral Analysis.	Rawls E et al.	—	2021	→
Parietal P3 and midfrontal theta prospectively predict the development of adolescent alcohol use.	Harper J et al.	—	2021	→
Preserved sensory processing but hampered conflict detection when stimulus input is task-irrelevant.	Nuiten SA et al.	—	2021	→
Selection for Action: The Medial Frontal Cortex Is an Executive Hub for Stimulus and Response Selection.	Asanowicz D et al.	—	2021	→
Synchronization between Keyboard Typing and Neural Oscillations.	Duprez J et al.	—	2021	→
Target Amplification and Distractor Inhibition: Theta Oscillatory Dynamics of Selective Attention in a Flanker Task.	Haciahmet CC et al.	—	2021	→
The role of cognitive control and top-down processes in object affordances.	Ferguson TD et al.	—	2021	→
Theta activity from frontopolar cortex, mid-cingulate cortex and anterior cingulate cortex shows different roles in cognitive planning performance.	Domic-Siede M et al.	—	2021	→
Tracking dynamic adjustments to decision making and performance monitoring processes in conflict tasks.	Feuerriegel D et al.	—	2021	→
Widespread theta coherence during spatial cognitive control.	Myers JC et al.	—	2021	→
Abnormalities in auditory and visual cognitive processes are differentiated with theta responses in patients with Parkinson's disease with and without dementia.	Güntekin B et al.	—	2020	→
Action information contributes to metacognitive decision-making.	Wokke ME et al.	—	2020	→
Acute Aerobic Exercise Ameliorates Cravings and Inhibitory Control in Heroin Addicts: Evidence From Event-Related Potentials and Frequency Bands.	Wang D et al.	—	2020	→
Closed-Loop Frontal Midlineθ Neurofeedback: A Novel Approach for Training Focused-Attention Meditation.	Brandmeyer T et al.	—	2020	→
Delta phase reset predicts conflict-related changes in P3 amplitude and behavior.	Rawls E et al.	—	2020	→
High-definition transcranial direct current stimulation dissociates fronto-visual theta lateralization during visual selective attention.	Spooner RK et al.	—	2020	→
Influence of cognitive-motor expertise on brain dynamics of anticipatory-based outcome processing.	Lu Y et al.	—	2020	→
Midfrontal theta phase coordinates behaviorally relevant brain computations during cognitive control.	Duprez J et al.	—	2020	→
Multiple Midfrontal Thetas Revealed by Source Separation of Simultaneous MEG and EEG.	Zuure MB et al.	—	2020	→
Patterns of Focal- and Large-Scale Synchronization in Cognitive Control and Inhibition: A Review.	Beppi C et al.	—	2020	→
Psychometrics of the continuous mind: Measuring cognitive sub-processes via mouse tracking.	Scherbaum S et al.	—	2020	→
The neurocognitive underpinnings of the Simon effect: An integrative review of current research.	Cespón J et al.	—	2020	→
The role of midfrontal theta oscillations across the development of cognitive control in preschoolers and school-age children.	Adam N et al.	—	2020	→
Toward a valid electrocortical correlate of regulation of craving using single-trial regression.	Dieterich R et al.	—	2020	→
Adolescent cognitive control, theta oscillations, and social observation.	Buzzell GA et al.	—	2019	→
Decoding task engagement from distributed network electrophysiology in humans.	Provenza NR et al.	—	2019	→
Defensive motivation increases conflict adaptation through local changes in cognitive control: Evidence from ERPs and mid-frontal theta.	Yang Q et al.	—	2019	→
Evaluation of Self-generated Behavior: Untangling Metacognitive Readout and Error Detection.	Kononowicz TW et al.	—	2019	→
Frontal theta predicts specific cognitive control-induced behavioural changes beyond general reaction time slowing.	Cooper PS et al.	—	2019	→
Increases in theta CSD power and coherence during a calibrated stop-signal task: implications for goal-conflict processing and the Behavioural Inhibition System.	Lockhart TS et al.	—	2019	→
Non-selective inhibition of inappropriate motor-tendencies during response-conflict by a fronto-subthalamic mechanism.	Wessel JR et al.	—	2019	→
Reduced premovement positivity during the stimulus-response interval precedes errors: Using single-trial and regression ERPs to understand performance deficits in ADHD.	Burwell SJ et al.	—	2019	→
Single trial prestimulus oscillations predict perception of the sound-induced flash illusion.	Kaiser M et al.	—	2019	→
Subthalamic nucleus local field potentials recordings reveal subtle effects of promised reward during conflict resolution in Parkinson's disease.	Duprez J et al.	—	2019	→
Target-related parietal P3 and medial frontal theta index the genetic risk for problematic substance use.	Harper J et al.	—	2019	→
The feedback-related negativity indexes prediction error in active but not observational learning.	Burnside R et al.	—	2019	→
Theta-Band Functional Connectivity and Single-Trial Cognitive Control in Sports-Related Concussion: Demonstration of Proof-of-Concept for a Potential Biomarker of Concussion.	Smith EE et al.	—	2019	→
Alignment of alpha-band desynchronization with syntactic structure predicts successful sentence comprehension.	Vassileiou B et al.	—	2018	→
Alterations of theta oscillation in executive control in temporal lobe epilepsy patients.	Li X et al.	—	2018	→
Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing.	Glazer JE et al.	—	2018	→
Brain reflections: A circuit-based framework for understanding information processing and cognitive control.	Gratton G	—	2018	→
Cognitive control and midline theta adjust across multiple timescales.	Chinn LK et al.	—	2018	→
Cognitive control involves theta power within trials and beta power across trials in the prefrontal-subthalamic network.	Zavala B et al.	—	2018	→
Conflict-related medial frontal theta as an endophenotype for alcohol use disorder.	Harper J et al.	—	2018	→
Dissociable functional activities of cortical theta and beta oscillations in the lateral prefrontal cortex during intertemporal choice.	Gui DY et al.	—	2018	→
Dynamics of cognitive control: Theoretical bases, paradigms, and a view for the future.	Gratton G et al.	—	2018	→
Frontal network dynamics reflect neurocomputational mechanisms for reducing maladaptive biases in motivated action.	Swart JC et al.	—	2018	→
How the Level of Reward Awareness Changes the Computational and Electrophysiological Signatures of Reinforcement Learning.	Correa CMC et al.	—	2018	→
It's the Other Way Around! Early Modulation of Sensory Distractor Processing Induced by Late Response Conflict.	Pastötter B et al.	—	2018	→
Moment-to-Moment Fluctuations in Neuronal Excitability Bias Subjective Perception Rather than Strategic Decision-Making.	Iemi L et al.	—	2018	→
Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior.	Bridwell DA et al.	—	2018	→
Non-motor Characterization of the Basal Ganglia: Evidence From Human and Non-human Primate Electrophysiology.	Eisinger RS et al.	—	2018	→
Novelty N2-P3a Complex and Theta Oscillations Reflect Improving Neural Coordination Within Frontal Brain Networks During Adolescence.	Wienke AS et al.	—	2018	→
Oscillatory Mechanisms of Response Conflict Elicited by Color and Motion Direction: An Individual Differences Approach.	Vissers ME et al.	—	2018	→
The Psychophysiology of Action: A Multidisciplinary Endeavor for Integrating Action and Cognition.	Hoffmann S et al.	—	2018	→
Trial-by-trial co-variation of pre-stimulus EEG alpha power and visuospatial bias reflects a mixture of stochastic and deterministic effects.	Benwell CSY et al.	—	2018	→
Utilizing time-frequency amplitude and phase synchrony measure to assess feedback processing in a gambling task.	Watts ATM et al.	—	2018	→
Distinct Oscillatory Frequencies Underlie Excitability of Human Occipital and Parietal Cortex.	Samaha J et al.	—	2017	→
Error-related oscillatory activity is modulated by novelty seeking in the reward condition.	Mojsa-Kaja J et al.	—	2017	→
Error-Specific Cognitive Control Alterations in Generalized Anxiety Disorder.	Cavanagh JF et al.	—	2017	→
Frontal Theta Dynamics during Response Conflict in Long-Term Mindfulness Meditators.	Jo HG et al.	—	2017	→
Frontoparietal theta oscillations during proactive control are associated with goal-updating and reduced behavioral variability.	Cooper PS et al.	—	2017	→
Human subthalamic nucleus activity during non-motor decision making.	Zavala BA et al.	—	2017	→
Human Subthalamic Nucleus Theta and Beta Oscillations Entrain Neuronal Firing During Sensorimotor Conflict.	Zavala B et al.	—	2017	→
Individual Alpha Peak Frequency Predicts 10 Hz Flicker Effects on Selective Attention.	Gulbinaite R et al.	—	2017	→
Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making.	Alavash M et al.	—	2017	→
Local and interregional alpha EEG dynamics dissociate between memory for search and memory for recognition.	van Driel J et al.	—	2017	→
Motor expertise modulates neural oscillations and temporal dynamics of cognitive control.	Wang CH et al.	—	2017	→
Neural Oscillation Reveals Deficits in Visuospatial Working Memory in Children With Developmental Coordination Disorder.	Wang CH et al.	—	2017	→
Prestimulus alpha-band power biases visual discrimination confidence, but not accuracy.	Samaha J et al.	—	2017	→
Proactive Control: Neural Oscillatory Correlates of Conflict Anticipation and Response Slowing.	Chang A et al.	—	2017	→
Spatiotemporal oscillatory dynamics of visual selective attention during a flanker task.	McDermott TJ et al.	—	2017	→
Sure I'm Sure: Prefrontal Oscillations Support Metacognitive Monitoring of Decision Making.	Wokke ME et al.	—	2017	→
Testing the effects of adolescent alcohol use on adult conflict-related theta dynamics.	Harper J et al.	—	2017	→
The role of temporal predictability for early attentional adjustments after conflict.	Bombeke K et al.	—	2017	→
Two Independent Frontal Midline Theta Oscillations during Conflict Detection and Adaptation in a Simon-Type Manual Reaching Task.	Töllner T et al.	—	2017	→
A functional classification of medial frontal negativity ERPs: Theta oscillations and single subject effects.	Van Noordt SJ et al.	—	2016	→
Decisions Made with Less Evidence Involve Higher Levels of Corticosubthalamic Nucleus Theta Band Synchrony.	Zavala B et al.	—	2016	→
Delta, theta, and alpha event-related oscillations in alcoholics during Go/NoGo task: Neurocognitive deficits in execution, inhibition, and attention processing.	Pandey AK et al.	—	2016	→
EEG Theta Dynamics within Frontal and Parietal Cortices for Error Processing during Reaching Movements in a Prism Adaptation Study Altering Visuo-Motor Predictive Planning.	Arrighi P et al.	—	2016	→
Emotional Modulation of Conflict Processing in the Affective Domain: Evidence from Event-related Potentials and Event-related Spectral Perturbation Analysis.	Ma J et al.	—	2016	→
Frontal Monitoring and Parietal Evidence: Mechanisms of Error Correction.	Navarro-Cebrian A et al.	—	2016	→
Frontosubthalamic Circuits for Control of Action and Cognition.	Aron AR et al.	—	2016	→
Human subthalamic nucleus-medial frontal cortex theta phase coherence is involved in conflict and error related cortical monitoring.	Zavala B et al.	—	2016	→
Patterns of alpha asymmetry in those with elevated worry, trait anxiety, and obsessive-compulsive symptoms: A test of the worry and avoidance models of alpha asymmetry.	Smith EE et al.	—	2016	→
Proactive, but Not Reactive, Distractor Filtering Relies on Local Modulation of Alpha Oscillatory Activity.	Vissers ME et al.	—	2016	→
Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients.	Albrecht MA et al.	—	2016	→
The ventral hippocampus, but not the dorsal hippocampus is critical for learned approach-avoidance decision making.	Schumacher A et al.	—	2016	→
Cortical delta activity reflects reward prediction error and related behavioral adjustments, but at different times.	Cavanagh JF	—	2015	→
Discriminant brain connectivity patterns of performance monitoring at average and single-trial levels.	Zhang H et al.	—	2015	→
EEG neural oscillatory dynamics reveal semantic and response conflict at difference levels of conflict awareness.	Jiang J et al.	—	2015	→
Electrophysiological dynamics reveal distinct processing of stimulus-stimulus and stimulus-response conflicts.	Li Q et al.	—	2015	→
Frequency Band-Specific Electrical Brain Stimulation Modulates Cognitive Control Processes.	van Driel J et al.	—	2015	→
Interactive effects of citalopram and serotonin transporter genotype on neural correlates of response inhibition and attentional orienting.	Fischer AG et al.	—	2015	→
Neurofeedback and its possible relevance for the treatment of Tourette syndrome.	Farkas A et al.	—	2015	→
(No) time for control: Frontal theta dynamics reveal the cost of temporally guided conflict anticipation.	van Driel J et al.	—	2015	→
Power-law dynamics in neuronal and behavioral data introduce spurious correlations.	Schaworonkow N et al.	—	2015	→
Serotonin reuptake inhibitors and serotonin transporter genotype modulate performance monitoring functions but not their electrophysiological correlates.	Fischer AG et al.	—	2015	→
Single trial beta oscillations index time estimation.	Kononowicz TW et al.	—	2015	→
The subthalamic nucleus, oscillations, and conflict.	Zavala B et al.	—	2015	→
Theta and Alpha Band Modulations Reflect Error-Related Adjustments in the Auditory Condensation Task.	Novikov NA et al.	—	2015	→
A long-range fronto-parietal 5- to 10-Hz network predicts "top-down" controlled guidance in a task-switch paradigm.	Phillips JM et al.	—	2014	→
Analytical methods and experimental approaches for electrophysiological studies of brain oscillations.	Gross J	—	2014	→
A neural microcircuit for cognitive conflict detection and signaling.	Cohen MX	—	2014	→
Concurrent working memory task decreases the Stroop interference effect as indexed by the decreased theta oscillations.	Zhao Y et al.	—	2014	→
Coordinated within-trial dynamics of low-frequency neural rhythms controls evidence accumulation.	Werkle-Bergner M et al.	—	2014	→
Dissociable mechanisms underlying individual differences in visual working memory capacity.	Gulbinaite R et al.	—	2014	→
Frontal theta activity is pronounced during illusory perception.	Mathes B et al.	—	2014	→
Frontal theta as a mechanism for cognitive control.	Cavanagh JF et al.	—	2014	→
Fronto-parietal network oscillations reveal relationship between working memory capacity and cognitive control.	Gulbinaite R et al.	—	2014	→
Genetic overlap between evoked frontocentral theta-band phase variability, reaction time variability, and attention-deficit/hyperactivity disorder symptoms in a twin study.	McLoughlin G et al.	—	2014	→
Inhibitory motor control based on complex stopping goals relies on the same brain network as simple stopping.	Wessel JR et al.	—	2014	→
Midline frontal cortex low-frequency activity drives subthalamic nucleus oscillations during conflict.	Zavala BA et al.	—	2014	→
Neural mechanisms and temporal dynamics of performance monitoring.	Ullsperger M et al.	—	2014	→
Performance monitoring during associative learning and its relation to obsessive-compulsive characteristics.	Doñamayor N et al.	—	2014	→
Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting.	Enriquez-Geppert S et al.	—	2014	→
Slow oscillations during sleep coordinate interregional communication in cortical networks.	Cox R et al.	—	2014	→
Subthreshold muscle twitches dissociate oscillatory neural signatures of conflicts from errors.	Cohen MX et al.	—	2014	→
The subthalamic nucleus contributes to post-error slowing.	Cavanagh JF et al.	—	2014	→
Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task.	Harper J et al.	—	2014	→
BOLD Frequency Power Indexes Working Memory Performance.	Balsters JH et al.	—	2013	→
Common medial frontal mechanisms of adaptive control in humans and rodents.	Narayanan NS et al.	—	2013	→
Dynamic adjustments of cognitive control: oscillatory correlates of the conflict adaptation effect.	Pastötter B et al.	—	2013	→
EEG source reconstruction reveals frontal-parietal dynamics of spatial conflict processing.	Cohen MX et al.	—	2013	→
Frontal midline theta and N200 amplitude reflect complementary information about expectancy and outcome evaluation.	Hajihosseini A et al.	—	2013	→
Lower theta inter-trial phase coherence during performance monitoring is related to higher reaction time variability: a lifespan study.	Papenberg G et al.	—	2013	→
Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior.	Cohen MX et al.	—	2013	→
Real and fictive outcomes are processed differently but converge on a common adaptive mechanism.	Fischer AG et al.	—	2013	→
Reply to "Higher response time increases theta energy, conflict increases response time".	Cohen MX et al.	—	2013	→
Subthalamic nucleus local field potential activity during the Eriksen flanker task reveals a novel role for theta phase during conflict monitoring.	Zavala B et al.	—	2013	→
Synchronization of medial temporal lobe and prefrontal rhythms in human decision making.	Guitart-Masip M et al.	—	2013	→
The congruency effect in the posterior medial frontal cortex is more consistent with time on task than with response conflict.	Weissman DH et al.	—	2013	→
The morphology of midcingulate cortex predicts frontal-midline theta neurofeedback success.	Enriquez-Geppert S et al.	—	2013	→
The neural oscillations of conflict adaptation in the human frontal region.	Tang D et al.	—	2013	→
The θ-γ neural code.	Lisman JE et al.	—	2013	→
An electrophysiological signal that precisely tracks the emergence of error awareness.	Murphy PR et al.	—	2012	→
Approach-bias predicts development of cannabis problem severity in heavy cannabis users: results from a prospective FMRI study.	Cousijn J et al.	—	2012	→
EEG oscillations reveal neural correlates of evidence accumulation.	van Vugt MK et al.	—	2012	→
Event-Related Oscillations in Alcoholism Research: A Review.	Pandey AK et al.	—	2012	→
Frontal theta reflects uncertainty and unexpectedness during exploration and exploitation.	Cavanagh JF et al.	—	2012	→
Not all errors are alike: theta and alpha EEG dynamics relate to differences in error-processing dynamics.	van Driel J et al.	—	2012	→
Theta dynamics reveal domain-specific control over stimulus and response conflict.	Nigbur R et al.	—	2012	→
Theta lingua franca: a common mid-frontal substrate for action monitoring processes.	Cavanagh JF et al.	—	2012	→
Hippocampal-prefrontal connectivity predicts midfrontal oscillations and long-term memory performance.	Cohen MX	—	2011	→
Single-trial analyses: why bother?	Pernet CR et al.	—	2011	→
Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold.	Cavanagh JF et al.	—	2011	→