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ERPLAB toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB's EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB's tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user's guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations.

Researchers have long debated whether attentional capture is purely stimulus driven or purely goal driven. In the current study, we tested a hybrid account, called the signal-suppression hypothesis, which posits that stimuli automatically produce a bottom-up salience signal, but that this signal can be suppressed via top-down control processes. To test this account, we used a new capture-probe paradigm in which participants searched for a target shape while ignoring an irrelevant color singleton. On occasional probe trials, letters were briefly presented inside the search shapes, and participants attempted to report these letters. Under conditions that promoted capture by the irrelevant singleton, accuracy was greater for the letter inside the singleton distractor than for letters inside nonsingleton distractors. However, when the conditions were changed to avoid capture by the singleton, accuracy for the letter inside the irrelevant singleton was reduced below the level observed for letters inside nonsingleton distractors, an indication of active suppression of processing at the singleton location.

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.

How working memory works As well as holding a vast store of long-term memories, the human brain creates short-term memories that last only a few seconds and are essential for performing tasks such as adding two numbers or comparing the attractiveness of two faces. We know that only a limited amount of information can be stored in short-term memory, but whether we store high-quality representations of a small number of items, or a potentially infinite number of 'low resolution' items is the subject of much debate. A new study of visual working memory resolves the matter in favour of the 'high resolution' option: short-term information storage does not discard quality in favour of quantity, but stores a relatively small number of objects, as discrete fixed-resolution representations. Only a limited amount of information can be stored in short-term memory, but it is unclear whether we store high-quality representations of a small number of items or a larger number of items whose representation is of lower quality. Visual working memory is studied, particularly both the number of representations and the resolution of each representation, with the results favouring the idea that we store a smaller number of objects, with relatively discrete, fixed-resolution representations. Limits on the storage capacity of working memory significantly affect cognitive abilities in a wide range of domains 1 , but the nature of these capacity limits has been elusive 2 . Some researchers have proposed that working memory stores a limited set of discrete, fixed-resolution representations 3 , whereas others have proposed that working memory consists of a pool of resources that can be allocated flexibly to provide either a small number of high-resolution representations or a large number of low-resolution representations 4 . Here we resolve this controversy by providing independent measures of capacity and resolution. We show that, when presented with more than a few simple objects, human observers store a high-resolution representation of a subset of the objects and retain no information about the others. Memory resolution varied over a narrow range that cannot be explained in terms of a general resource pool but can be well explained by a small set of discrete, fixed-resolution representations.

For more than 2 decades, researchers have debated the nature of cognitive control in the guidance of visual attention. Stimulus-driven theories claim that salient stimuli automatically capture attention, whereas goal-driven theories propose that an individual’s attentional control settings determine whether salient stimuli capture attention. In the current study, we tested a hybrid account called the signal suppression hypothesis, which claims that all stimuli automatically generate a salience signal but that this signal can be actively suppressed by top-down attentional mechanisms. Previous behavioral and electrophysiological research has shown that participants can suppress covert shifts of attention to salient-but-irrelevant color singletons. In this study, we used eye-tracking methods to determine whether participants can also suppress overt shifts of attention to irrelevant singletons. We found that under conditions that promote active suppression of the irrelevant singletons, overt attention was less likely to be directed toward the salient distractors than toward nonsalient distractors. This result provides direct evidence that people can suppress salient-but-irrelevant singletons below baseline levels.

•We evaluated the impact of artifact correction and artifact rejection on EEG/ERP decoding performance.•We explored a wide range of experimental paradigms, including both easy and difficult decoding tasks, various subject populations, and differing electrode densities.•We found that the combination of artifact correction and rejection did not significantly enhance decoding performance in the vast majority of cases.•However, we strongly recommended using artifact correction prior to decoding analyses to reduce artifact-related confounds. Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminating these artifacts during preprocessing enhances the performance of multivariate pattern analysis (MVPA; decoding), especially given that artifact rejection reduces the number of trials available for training the decoder. This study aimed to evaluate the impact of artifact-minimization approaches on the decoding performance of support vector machines. Independent component analysis (ICA) was used to correct ocular artifacts, and artifact rejection was used to discard trials with large voltage deflections from other sources (e.g., muscle artifacts). We assessed decoding performance in relatively simple binary classification tasks using data from seven commonly-used event-related potential paradigms (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity), as well as more challenging multi-way decoding tasks, including stimulus location and stimulus orientation. The results indicated that the combination of artifact correction and rejection did not improve decoding performance in the vast majority of cases. However, artifact correction may still be essential to minimize artifact-related confounds that might artificially inflate decoding accuracy. Researchers who are using similar methods to decode EEG data from paradigms, populations, and recording setups that are similar to those examined here may benefit from our recommendations to optimize decoding performance and avoid incorrect conclusions.

There is considerable controversy about whether salient singletons capture attention in a bottom-up fashion, irrespective of top-down control settings. One possibility is that salient singletons always generate an attention capture signal, but this signal can be actively suppressed to avoid capture. In the present study, we investigated this issue by using event-related potential recordings, focusing on N2pc (N2-posterior-contralateral; a measure of attentional deployment) and Pd (distractor positivity; a measure of attentional suppression). Participants searched for a specific letter within one of two regions, and irrelevant color singletons were sometimes present. We found that the irrelevant singletons did not elicit N2pc but instead elicited Pd; this occurred equally within the attended and unattended regions. These findings suggest that salient singletons may automatically produce an attend-to-me signal, irrespective of top-down control settings, but this signal can be overridden by an active suppression process to prevent the actual capture of attention.

The present study sought to determine whether scalp electroencephalogram (EEG) signals contain decodable information about the direction of motion in random dot kinematograms (RDKs), in which the motion information is spatially distributed and mixed with random noise. Any direction of motion from 0 to 360° was possible, and observers reported the precise direction of motion at the end of a 1500-ms stimulus display. We decoded the direction of motion separately during the motion period (during which motion information was being accumulated) and the report period (during which a shift of attention was necessary to make a fine-tuned direction report). Machine learning was used to decode the precise direction of motion (within ±11.25°) from the scalp distribution of either alpha-band EEG activity or sustained event-related potentials (ERPs). We found that ERP-based decoding was above chance (1/16) during both the stimulus and the report periods, whereas alpha-based decoding was above chance only during the report period. Thus, sustained ERPs contain information about spatially distributed direction-of-motion, providing a new method for observing the accumulation of sensory information with high temporal resolution. By contrast, the scalp topography of alpha-band EEG activity appeared to mainly reflect spatially focused attentional processes rather than sensory information.

Recent experiences influence the processing of new information even when those experiences are irrelevant to the current task. Does this reflect the indirect effects of a passively maintained representation of the previous experience, or is this representation reactivated when a new event occurs? To answer this question, we attempted to decode the orientation of the stimulus on the previous trial from the electroencephalogram on the current trial in a working memory task. Behavioral data confirmed that the previous-trial stimulus orientation influenced the reported orientation on the current trial, even though the previous-trial orientation was now task irrelevant. In two independent experiments, we found that the previous-trial orientation could be decoded from the current-trial electroencephalogram, indicating that the current-trial stimulus reactivated or boosted the representation of the previous-trial orientation. These results suggest that the effects of recent experiences on behavior are driven, in part, by a reactivation of those experiences and not solely by the indirect effects of passive memory traces.

We investigated whether the representations of different objects are maintained independently in working memory or interact with each other. Observers were shown two sequentially presented orientations and required to reproduce each orientation after a delay. The sequential presentation minimized perceptual interactions so that we could isolate interactions between memory representations per se. We found that similar orientations were repelled from each other whereas dissimilar orientations were attracted to each other. In addition, when one of the items was given greater attentional priority by means of a cue, the representation of the high-priority item was not influenced very much by the orientation of the low-priority item, but the representation of the low-priority item was strongly influenced by the orientation of the high-priority item. This indicates that attention modulates the interactions between working memory representations. In addition, errors in the reported orientations of the two objects were positively correlated under some conditions, suggesting that representations of distinct objects may become grouped together in memory. Together, these results demonstrate that working-memory representations are not independent but instead interact with each other in a manner that depends on attentional priority.