The contribution of pre-stimulus neural oscillatory activity to spontaneous response time variability

Using occipital ⍺-bursts to modulate behavior in real-time

Pre-stimulus endogenous neural activity can influence the processing of upcoming sensory input and subsequent behavioral reactions. Despite it is known that spontaneous oscillatory activity mostly appears in stochastic bursts, typical approaches based on trial averaging fail to capture this. We aimed at relating spontaneous oscillatory bursts in the alpha band (8-13 Hz) to visual detection behavior, via an electroencephalography-based brain-computer interface (BCI) that allowed for burst-triggered stimulus presentation in real-time. According to alpha theories, we hypothesized that visual targets presented during alpha-bursts should lead to slower responses and higher miss rates, whereas targets presented in the absence of bursts (low alpha activity) should lead to faster responses and higher false alarm rates. Our findings support the role of bursts of alpha oscillations in visual perception and exemplify how real-time BCI systems can be used as a test bench for brain-behavioral theories.

Motor-induced oscillations in choice response performance

Recently, numerous studies have revealed 4-12 Hz fluctuations of behavioral performance in a multitude of tasks. The majority has utilized stimuli near detection threshold and observed related fluctuations in hit-rates, attributing these to perceptual or attentional processes. As neural oscillations in the 8-20 Hz range also feature prominently in cortical motor areas, they might cause fluctuations in the ability to induce responses, independent of attentional capabilities. Additionally, different effectors (e.g., the left versus right hand) might be cyclically prioritized in an alternating fashion, similar to the attentional sampling of distinct locations, objects, or memory templates. Here, we investigated these questions via a behavioral dense-sampling approach. Twenty-six participants performed a simple visual discrimination task using highly salient stimuli. We varied the interval between each motor response and the subsequent target from 330 to 1040 ms, and analyzed performance as a function of this interval. Our data show significant fluctuations of both RTs and sensitivity between 12.5 and 25 Hz, but no evidence for an alternating prioritization of left- versus right-hand responses. While our results suggest an impact of motor-related signals on performance oscillations, they might additionally be influenced by perceptual processes earlier in the processing hierarchy. In summary, we demonstrate that behavioral oscillations generalize to situations involving highly salient stimuli, closer to everyday life. Moreover, our work adds to the literature by showing fluctuations at a high speed, which might be a consequence of both low task difficulty and the involvement of sensorimotor rhythms.

The influence of temporal unpredictability on the electrophysiological mechanisms of neural entrainment

Neural entrainment, or the synchronization of endogenous oscillations to exogenous rhythmic events, has been postulated as a powerful mechanism underlying stimulus prediction. Nevertheless, studies that have explored the benefits of neural entrainment on attention, perception, and other cognitive functions have received criticism, which could compromise their theoretical and clinical value. Therefore, the aim of the present study was [1] to confirm the presence of entrainment using a set of pre-established criteria and [2] to establish whether the reported behavioral benefits of entrainment remain when temporal predictability related to target appearance is reduced. To address these points, we adapted a previous neural entrainment paradigm to include: a variable entrainer length and increased target-absent trials, and instructing participants to respond only if they had detected a target, to avoid guessing. Thirty-six right-handed women took part in this study. Our results indicated a significant alignment of neural activity to the external periodicity as well as a persistence of phase alignment beyond the offset of the driving signal. This would appear to indicate that neural entrainment triggers preexisting endogenous oscillations, which cannot simply be explained as a succession of event-related potentials associated with the stimuli, expectation and/or motor response. However, we found no behavioral benefit for targets in-phase with entrainers, which would suggest that the effect of neural entrainment on overt behavior may be more limited than expected. These results help to clarify the mechanistic processes underlying neural entrainment and provide new insights on its applications.

Rhythmic interactions between the mediodorsal thalamus and prefrontal cortex precede human visual perception

The thalamus is much more than a simple sensory relay. High-order thalamic nuclei, such as the mediodorsal thalamus, exert a profound influence over animal cognition. However, given the difficulty of directly recording from the thalamus in humans, next-to-nothing is known about thalamic and thalamocortical contributions to human cognition. To address this, we analysed simultaneously-recorded thalamic iEEG and whole-head MEG in six patients (plus MEG recordings from twelve healthy controls) as they completed a visual detection task. We observed that the phase of both ongoing mediodorsal thalamic and prefrontal low-frequency activity was predictive of perceptual performance. Critically however, mediodorsal thalamic activity mediated prefrontal contributions to perceptual performance. These results suggest that it is thalamocortical interactions, rather than cortical activity alone, that is predictive of upcoming perceptual performance and, more generally, highlights the importance of accounting for the thalamus when theorising about cortical contributions to human cognition.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Frequency-dependent effects of EEG phase resetting on reaction time

There is trial-to-trial variability in the reaction time to stimulus presentation. Since this variability exists even in an identical stimulus condition, it reflects the internal neural dynamics of the brain. To understand the neural dynamics that influence the reaction time, we conducted an electroencephalogram (EEG) experiment in which participants were asked to press a response button as quickly as possible when a stimulus was visually presented. Phase-locking factor analysis revealed that phase resetting in two frequency bands, which appeared 0.2 s after the stimulus presentation, characterized the reaction time. The combination of the theta band phase resetting in the left parietal region and the delta band phase resetting mainly in the posterior region was associated with the fastest reaction time, whereas delta band phase resetting without theta band phase resetting was associated with the faster reaction time. The results indicated that there were frequency-dependent effects in the relationships between the EEG phase resetting and reaction time.

Frontoparietal Networks Mediate the Behavioral Impact of Alpha Inhibition in Visual Cortex

Alpha oscillations are known to play a central role in the functional inhibition of visual cortices, but the mechanisms involved are poorly understood. One noninvasive method for modulating alpha activity experimentally is through the use of flickering visual stimuli that "entrain" visual cortices. Such alpha entrainment has been found to compromise visual perception and affect widespread cortical regions, but it remains unclear how the interference occurs and whether the widespread activity induced by alpha entrainment reflects a compensatory mechanism to mitigate the entrainment, or alternatively, a propagated interference signal that translates to impaired visual processing. Herein, we attempt to address these questions by integrating alpha entrainment into a modified Posner cueing paradigm, while measuring the underlying dynamics using magnetoencephalography. Our findings indicated that alpha entrainment is negatively related to task performance, such that as neural entrainment increases on the attended side (relative to the unattended side) accuracy decreases. Further, this attentional biasing is found to covary robustly with activity in the frontoparietal attention network. Critically, the observed negative entrainment effect on task accuracy was also fully mediated by activity in frontoparietal regions, signifying a propagation of the interfering alpha entrainment signal from bottom-up sensory to top-down regulatory networks.

The Weakened Relationship Between Prestimulus Alpha Oscillations and Response Time in Older Adults With Mild Cognitive Impairment

Background: Prestimulus alpha oscillations associated with preparatory attention have an impact on response time (RT). However, little is known about whether there is a deficit in the relationship between prestimulus alpha oscillations and RT in older adults with mild cognitive impairment (MCI).

Method: We collected electroencephalography (EEG) data from 28 older adults with MCI and 28 demographically matched healthy controls (HCs) when they were performing an Eriksen flanker task. For each participant, single-trial prestimulus alpha power was calculated for combinations of congruency (congruent vs. incongruent) and response speed (fast vs. slow).

Result: Statistical analysis indicated that prestimulus alpha power was significantly lower for fast trials than slow trials in HCs but not in older adults with MCI. The Fisher’s z scores of the within-subject correlation coefficients between single-trial prestimulus alpha power and RT were significantly larger in HCs than in older adults with MCI. In addition, machine learning analyses indicated that prestimulus alpha power and its correlation with RT could serve as features to distinguish older adults with MCI from HCs and to predict performance on some neuropsychological tests.

Conclusion: The reduced correlation between prestimulus alpha activity and RT suggests that older adults with MCI experience impaired preparatory attention.

Frequency and power of human alpha oscillations drift systematically with time-on-task

Oscillatory neural activity is a fundamental characteristic of the mammalian brain spanning multiple levels of spatial and temporal scale. Current theories of neural oscillations and analysis techniques employed to investigate their functional significance are based on an often implicit assumption: In the absence of experimental manipulation, the spectral content of any given EEG- or MEG-recorded neural oscillator remains approximately stationary over the course of a typical experimental session (∼1 h), spontaneously fluctuating only around its dominant frequency. Here, we examined this assumption for ongoing neural oscillations in the alpha-band (8-13 Hz). We found that alpha peak frequency systematically decreased over time, while alpha-power increased. Intriguingly, these systematic changes showed partial independence of each other: Statistical source separation (independent component analysis) revealed that while some alpha components displayed concomitant power increases and peak frequency decreases, other components showed either unique power increases or frequency decreases. Interestingly, we also found these components to differ in frequency. Components that showed mixed frequency/power changes oscillated primarily in the lower alpha-band (∼8-10 Hz), while components with unique changes oscillated primarily in the higher alpha-band (∼9-13 Hz). Our findings provide novel clues on the time-varying intrinsic properties of large-scale neural networks as measured by M/EEG, with implications for the analysis and interpretation of studies that aim at identifying functionally relevant oscillatory networks or at driving them through external stimulation.

Spontaneous pre-stimulus oscillatory activity shapes the way we look: A concurrent imaging and eye-movement study

Previous behavioural studies have accrued evidence that response time plays a critical role in determining whether selection is influenced by stimulus saliency or target template. In the present work, we investigated to what extent the variations in timing and consequent oculomotor controls are influenced by spontaneous variations in pre-stimulus alpha oscillations. We recorded simultaneously brain activity using magnetoencephalography (MEG) and eye movements while participants performed a visual search task. Our results show that slower saccadic reaction times were predicted by an overall stronger alpha power in the 500 ms time window preceding the stimulus onset, while weaker alpha power was a signature of faster responses. When looking separately at performance for fast and slow responses, we found evidence for two specific sources of alpha activity predicting correct versus incorrect responses. When saccades were quickly elicited, errors were predicted by stronger alpha activity in posterior areas, comprising the angular gyrus in the temporal-parietal junction (TPJ) and possibly the lateral intraparietal area (LIP). Instead, when participants were slower in responding, an increase of alpha power in frontal eye fields (FEF), supplementary eye fields (SEF) and dorsolateral pre-frontal cortex (DLPFC) predicted erroneous saccades. In other words, oculomotor accuracy in fast responses was predicted by alpha power differences in more posterior areas, while the accuracy in slow responses was predicted by alpha power differences in frontal areas, in line with the idea that these areas may be differentially related to stimulus-driven and goal-driven control of selection.

Low and variable correlation between reaction time costs and accuracy costs explained by accumulation models: Meta-analysis and simulations

The underpinning assumption of much research on cognitive individual differences (or group differences) is that task performance indexes cognitive ability in that domain. In many tasks performance is measured by differences (costs) between conditions, which are widely assumed to index a psychological process of interest rather than extraneous factors such as speed-accuracy trade-offs (e.g., Stroop, implicit association task, lexical decision, antisaccade, Simon, Navon, flanker, and task switching). Relatedly, reaction time (RT) costs or error costs are interpreted similarly and used interchangeably in the literature. All of this assumes a strong correlation between RT-costs and error-costs from the same psychological effect. We conducted a meta-analysis to test this, with 114 effects across a range of well-known tasks. Counterintuitively, we found a general pattern of weak, and often no, association between RT and error costs (mean r = .17, range -.45 to .78). This general problem is accounted for by the theoretical framework of evidence accumulation models, which capture individual differences in (at least) 2 distinct ways. Differences affecting accumulation rate produce positive correlation. But this is cancelled out if individuals also differ in response threshold, which produces negative correlations. In the models, subtractions between conditions do not isolate processing costs from caution. To demonstrate the explanatory power of synthesizing the traditional subtraction method within a broader decision model framework, we confirm 2 predictions with new data. Thus, using error costs or RT costs is more than a pragmatic choice; the decision carries theoretical consequence that can be understood through the accumulation model framework. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance

Prestimulus oscillatory neural activity has been linked to perceptual outcomes during performance of psychophysical detection and discrimination tasks. Specifically, the power and phase of low frequency oscillations have been found to predict whether an upcoming weak visual target will be detected or not. However, the mechanisms by which baseline oscillatory activity influences perception remain unclear. Recent studies suggest that the frequently reported negative relationship between α power and stimulus detection may be explained by changes in detection criterion (i.e., increased target present responses regardless of whether the target was present/absent) driven by the state of neural excitability, rather than changes in visual sensitivity (i.e., more veridical percepts). Here, we recorded EEG while human participants performed a luminance discrimination task on perithreshold stimuli in combination with single-trial ratings of perceptual awareness. Our aim was to investigate whether the power and/or phase of prestimulus oscillatory activity predict discrimination accuracy and/or perceptual awareness on a trial-by-trial basis. Prestimulus power (3-28 Hz) was inversely related to perceptual awareness ratings (i.e., higher ratings in states of low prestimulus power/high excitability) but did not predict discrimination accuracy. In contrast, prestimulus oscillatory phase did not predict awareness ratings or accuracy in any frequency band. These results provide evidence that prestimulus α power influences the level of subjective awareness of threshold visual stimuli but does not influence visual sensitivity when a decision has to be made regarding stimulus features. Hence, we find a clear dissociation between the influence of ongoing neural activity on conscious awareness and objective performance.

Trial-by-trial co-variation of pre-stimulus EEG alpha power and visuospatial bias reflects a mixture of stochastic and deterministic effects

Human perception of perithreshold stimuli critically depends on oscillatory EEG activity prior to stimulus onset. However, it remains unclear exactly which aspects of perception are shaped by this pre‐stimulus activity and what role stochastic (trial‐by‐trial) variability plays in driving these relationships. We employed a novel jackknife approach to link single‐trial variability in oscillatory activity to psychometric measures from a task that requires judgement of the relative length of two line segments (the landmark task). The results provide evidence that pre‐stimulus alpha fluctuations influence perceptual bias. Importantly, a mediation analysis showed that this relationship is partially driven by long‐term (deterministic) alpha changes over time, highlighting the need to account for sources of trial‐by‐trial variability when interpreting EEG predictors of perception. These results provide fundamental insight into the nature of the effects of ongoing oscillatory activity on perception. The jackknife approach we implemented may serve to identify and investigate neural signatures of perceptual relevance in more detail.

Activity of primate V1 neurons during the gap saccade task

When a saccadic eye movement is made toward a visual stimulus, the variability in accompanying primary visual cortex (V1) activity is related to saccade latency in both humans and simians. To understand the nature of this relationship, we examined the functional link between V1 activity and the initiation of visually guided saccades during the gap saccade task, in which a brief temporal gap is inserted between the turning off of a fixation stimulus and the appearance of a saccadic target. The insertion of such a gap robustly reduces saccade latency and facilitates the occurrence of extremely short-latency (express) saccades. Here we recorded single-cell activity from macaque V1 while monkeys performed the gap saccade task. In parallel with the gap effect on saccade latency the neural latency (time of first spike) of V1 response elicited by the saccade target became shorter, and the firing rate increased as the gap duration increased. Similarly, neural latency was shorter and firing rate was higher before express saccades relative to regular-latency saccades. In addition to these posttarget changes, the level of spontaneous spike activity during the pretarget period was negatively correlated with both neural and saccade latencies. These results demonstrate that V1 activity correlates with the gap effect and indicate that trial-to-trial variability in the state of V1 accompanies the variability of neural and behavioral latencies.NEW & NOTEWORTHY The link between neural activity in monkey primary visual cortex (V1) and visually guided behavioral response is confirmed with the gap saccade paradigm. Results indicated that the variability in neural latency of V1 spike activity correlates with the gap effect on saccade latency and that the trial-to-trial variability in the state of V1 before the onset of saccade target correlates with the variability in neural and behavioral latencies.

Slower resting alpha frequency is associated with superior localisation of moving targets

We examined the neurophysiological underpinnings of individual differences in the ability to maintain up-to-date representations of the positions of moving objects. In two experiments similar to the multiple object tracking (MOT) task, we asked observers to monitor continuously one or several targets as they moved unpredictably for a semi-random period. After all objects disappeared, observers were immediately prompted to report the perceived final position of one queried target. Precision of these position reports declined with attentional load, and reports tended to best resemble positions occupied by the queried target between 0 and 30ms in the past. Measurement of event-related potentials showed a contralateral delay activity over occipital scalp, maximal in the right hemisphere. The peak power-spectral frequency of observers' eyes-closed resting occipital alpha oscillations reliably predicted performance, such that lower-frequency alpha was associated with superior spatial localisation. Slower resting alpha might be associated with a cognitive style that depends less on memory-related processing and instead emphasises attention to changing stimuli.

Speeded saccadic and manual visuo-motor decisions: Distinct processes but same principles

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Core architecture of visuo-motor selection model generalises across effectors.

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Hand and eyes show very different response times, but similar decision times.

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Longer non-decision time for visuo-manual responses accounts for longer response times.

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Stronger faster transient visual inputs for saccades account for different selection dynamics.

Action decisions are considered an emergent property of competitive response activations. As such, decision mechanisms are embedded in, and therefore may differ between, different response modalities. Despite this, the saccadic eye movement system is often promoted as a model for all decisions, especially in the fields of electrophysiology and modelling. Other research traditions predominantly use manual button presses, which have different response distribution profiles and are initiated by different brain areas. Here we tested whether core concepts of action selection models (decision and non-decision times, integration of automatic and selective inputs to threshold, interference across response options, noise, etc.) generalise from saccadic to manual domains. Using two diagnostic phenomena, the remote distractor effect (RDE) and ‘saccadic inhibition', we find that manual responses are also sensitive to the interference of visual distractors but to a lesser extent than saccades and during a shorter time window. A biologically-inspired model (DINASAUR, based on non-linear input dynamics) can account for both saccadic and manual response distributions and accuracy by simply adjusting the balance and relative timings of transient and sustained inputs, and increasing the mean and variance of non-decisional delays for manual responses. This is consistent with known neurophysiological and anatomical differences between saccadic and manual networks. Thus core decision principles appear to generalise across effectors, consistent with previous work, but we also conclude that key quantitative differences underlie apparent qualitative differences in the literature, such as effects being robustly reported in one modality and unreliable in another.

A Role for Endogenous Brain States in Organizational Research: Moving Toward a Dynamic View of Cognitive Processes

The dominant view in neuroscience, including functional neuroimaging, is that the brain is an essentially reactive system, in which some sensory input causes some neural activity, which in turn results in some important response such as a motor activity or some hypothesized higher-level cognitive or affective process. This view has driven the rise of neuroscience methods in management and organizational research. However, the reactive view offers at best a partial understanding of how living organisms function in the real world. In fact, like any neural system, the human brain exhibits a constant ongoing activity. This intrinsic brain activity is produced internally, not in response to some environmental stimulus, and is thus termed endogenous brain activity (EBA). In the present article we introduce EBA to organizational research conceptually, explain its measurement, and go on to show that including EBA in management and organizational theory and empirical research has the potential to revolutionize how we think about human choice and behavior in organizations.

Noise and non-neuronal contributions to the BOLD signal: applications to and insights from animal studies

The BOLD signal reflects hemodynamic events within the brain, which in turn are driven by metabolic changes and neural activity. However, the link between BOLD changes and neural activity is indirect and can be influenced by a number of non-neuronal processes. Motion and physiological cycles have long been known to affect the BOLD signal and are present in both humans and animal models. Differences in physiological baseline can also contribute to intra- and inter-subject variability. The use of anesthesia, common in animal studies, alters neural activity, vascular tone, and neurovascular coupling. Most intriguing, perhaps, are the contributions from other processes that do not appear to be neural in origin but which may provide information about other aspects of neurophysiology. This review discusses different types of noise and non-neuronal contributors to the BOLD signal, sources of variability for animal studies, and insights to be gained from animal models.

How to Evaluate Phase Differences between Trial Groups in Ongoing Electrophysiological Signals

A growing number of studies endeavor to reveal periodicities in sensory and cognitive functions, by comparing the distribution of ongoing (pre-stimulus) oscillatory phases between two (or more) trial groups reflecting distinct experimental outcomes. A systematic relation between the phase of spontaneous electrophysiological signals, before a stimulus is even presented, and the eventual result of sensory or cognitive processing for that stimulus, would be indicative of an intrinsic periodicity in the underlying neural process. Prior studies of phase-dependent perception have used a variety of analytical methods to measure and evaluate phase differences, and there is currently no established standard practice in this field. The present report intends to remediate this need, by systematically comparing the statistical power of various measures of "phase opposition" between two trial groups, in a number of real and simulated experimental situations. Seven measures were evaluated: one parametric test (circular Watson-Williams test), and three distinct measures of phase opposition (phase bifurcation index, phase opposition sum, and phase opposition product) combined with two procedures for non-parametric statistical testing (permutation, or a combination of z-score and permutation). While these are obviously not the only existing or conceivable measures, they have all been used in recent studies. All tested methods performed adequately on a previously published dataset (Busch et al., 2009). On a variety of artificially constructed datasets, no single measure was found to surpass all others, but instead the suitability of each measure was contingent on several experimental factors: the time, frequency, and depth of oscillatory phase modulation; the absolute and relative amplitudes of post-stimulus event-related potentials for the two trial groups; the absolute and relative trial numbers for the two groups; and the number of permutations used for non-parametric testing. The concurrent use of two phase opposition measures, the parametric Watson-Williams test and a non-parametric test based on summing inter-trial coherence values for the two trial groups, appears to provide the most satisfactory outcome in all situations tested. Matlab code is provided to automatically compute these phase opposition measures.

Toward a model-based cognitive neuroscience of mind wandering

People often "mind wander" during everyday tasks, temporarily losing track of time, place, or current task goals. In laboratory-based tasks, mind wandering is often associated with performance decrements in behavioral variables and changes in neural recordings. Such empirical associations provide descriptive accounts of mind wandering - how it affects ongoing task performance - but fail to provide true explanatory accounts - why it affects task performance. In this perspectives paper, we consider mind wandering as a neural state or process that affects the parameters of quantitative cognitive process models, which in turn affect observed behavioral performance. Our approach thus uses cognitive process models to bridge the explanatory divide between neural and behavioral data. We provide an overview of two general frameworks for developing a model-based cognitive neuroscience of mind wandering. The first approach uses neural data to segment observed performance into a discrete mixture of latent task-related and task-unrelated states, and the second regresses single-trial measures of neural activity onto structured trial-by-trial variation in the parameters of cognitive process models. We discuss the relative merits of the two approaches, and the research questions they can answer, and highlight that both approaches allow neural data to provide additional constraint on the parameters of cognitive models, which will lead to a more precise account of the effect of mind wandering on brain and behavior. We conclude by summarizing prospects for mind wandering as conceived within a model-based cognitive neuroscience framework, highlighting the opportunities for its continued study and the benefits that arise from using well-developed quantitative techniques to study abstract theoretical constructs.

Power-law dynamics in neuronal and behavioral data introduce spurious correlations

Relating behavioral and neuroimaging measures is essential to understanding human brain function. Often, this is achieved by computing a correlation between behavioral measures, e.g., reaction times, and neurophysiological recordings, e.g., prestimulus EEG alpha-power, on a single-trial-basis. This approach treats individual trials as independent measurements and ignores the fact that data are acquired in a temporal order. It has already been shown that behavioral measures as well as neurophysiological recordings display power-law dynamics, which implies that trials are not in fact independent. Critically, computing the correlation coefficient between two measures exhibiting long-range temporal dependencies may introduce spurious correlations, thus leading to erroneous conclusions about the relationship between brain activity and behavioral measures. Here, we address data-analytic pitfalls which may arise when long-range temporal dependencies in neural as well as behavioral measures are ignored. We quantify the influence of temporal dependencies of neural and behavioral measures on the observed correlations through simulations. Results are further supported in analysis of real EEG data recorded in a simple reaction time task, where the aim is to predict the latency of responses on the basis of prestimulus alpha oscillations. We show that it is possible to "predict" reaction times from one subject on the basis of EEG activity recorded in another subject simply owing to the fact that both measures display power-law dynamics. The same is true when correlating EEG activity obtained from different subjects. A surrogate-data procedure is described which correctly tests for the presence of correlation while controlling for the effect of power-law dynamics.