The dot-probe task is often considered a gold standard in the field for investigating attentional bias to threat. However, serious issues with the task have been raised. Specifically, a number of studies have demonstrated that the traditional reaction time (RT) measure of attentional bias to threat in the dot-probe task has poor internal reliability and poor test-retest reliability. In addition, although threatening stimuli capture attention in other paradigms, attentional bias to threat has not usually been found in typical research participants in the dot-probe task. However, when attention is measured in the dot-probe task with the N2pc component of the event-related potential waveform, substantial attentional orienting to threat is observed, and the internal reliability is moderate. To provide a rigorous comparison of the reliability of this N2pc measure and the conventional behavioral measure, as well as to examine the relationship of these measures to anxiety, the present study examined the N2pc in conjunction with RT in the dot-probe task in a large sample of participants (N = 96). As in previous studies, RT showed no bias to threatening images across the sample and exhibited poor internal reliability. Moreover, this measure did not relate to trait anxiety. By contrast, the N2pc revealed a significant initial shift of attention to threat, and this measure was internally reliable. However, the N2pc was not correlated with trait anxiety, indicating that it does not provide a meaningful index of individual differences in anxiety in the dot-probe task. Together, these results indicate a serious need to develop new tasks and methods to more reliably investigate attentional bias to threat and its relationship to anxiety in both clinical and non-clinical populations.

In designing an ERP study, researchers must choose how many trials to include, balancing the desire to maximize statistical power and the need to minimize the length of the recording session. Recent studies have attempted to quantify the minimum number of trials needed to obtain reliable measures for a variety of ERP components. However, these studies have largely ignored other variables that affect statistical power in ERP studies, including sample size and effect magnitude. The goal of the present study was to determine whether and how the number of trials, number of participants, and effect magnitude interact to influence statistical power, thus providing a better guide for selecting an appropriate number of trials. We used a Monte Carlo approach to measure the probability of obtaining a statistically significant result when testing for (a) the presence of an ERP effect, (b) within-participant condition differences in an ERP effect, and (c) between-participants group differences in an ERP effect. Each of these issues was examined in the context of the error-related negativity and the lateralized readiness potential. We found that doubling the number of trials recommended by previous studies led to more than a doubling of statistical power under many conditions. Thus, when determining the number of trials that should be included in a given study, researchers must consider the sample size, the anticipated effect magnitude, and the noise level, rather than relying solely on general recommendations about the number of trials needed to obtain a "stable" ERP waveform.

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Event-related potentials (ERPs) are noninvasive measures of human brain activity that index a range of sensory, cognitive, affective, and motor processes. Despite their broad application across basic and clinical research, there is little standardization of ERP paradigms and analysis protocols across studies. To address this, we created ERP CORE (Compendium of Open Resources and Experiments), a set of optimized paradigms, experiment control scripts, data processing pipelines, and sample data (N = 40 neurotypical young adults) for seven widely used ERP components: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). This resource makes it possible for researchers to 1) employ standardized ERP paradigms in their research, 2) apply carefully designed analysis pipelines and use a priori selected parameters for data processing, 3) rigorously assess the quality of their data, and 4) test new analytic techniques with standardized data from a wide range of paradigms.

Increased attention to threat is considered a core feature of anxiety. However, there are multiple mechanisms of attention and multiple types of threat, and the relationships among attention, threat, and anxiety are poorly understood. In the present study, we used event-related potentials (ERPs) to separately isolate attentional selection (N2pc) and suppression (PD) of pictorial threats (photos of weapons, snakes, etc.) and conditioned threats (colored shapes paired with electric shock). In a sample of 48 young adults, both threat types were initially selected for increased attention (an N2pc), but only conditioned threats elicited subsequent suppression (a PD) and a reaction time (RT) bias. Levels of trait anxiety were unrelated to N2pc amplitude, but increased anxiety was associated with larger PDs (i.e., greater suppression) and reduced RT bias to conditioned threats. These results suggest that anxious individuals do not pay more attention to threats but rather engage more attentional suppression to overcome threats.

Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.