Dataset Information

Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials.

ABSTRACT: EEG data, and specifically the ERP, provide psychologists with the power to examine quickly occurring cognitive processes at the native temporal resolution at which they occur. Despite the advantages conferred by ERPs to examine processes at different points in time, ERP researchers commonly ignore the trial-to-trial temporal dimension by collapsing across trials of similar types (i.e., the signal averaging approach) because of constraints imposed by repeated measures ANOVA. Here, we present the advantages of using multilevel modeling (MLM) to examine trial-level data to investigate change in neurocognitive processes across the course of an experiment. Two examples are presented to illustrate the usefulness of this technique. The first demonstrates decreasing differentiation in N170 amplitude to faces of different races across the course of a race categorization task. The second demonstrates attenuation of the ERN as participants commit more errors within a task designed to measure implicit racial bias. Although the examples presented here are within the realm of social psychology, the use of MLM to analyze trial-level EEG data has the potential to contribute to a number of different theoretical domains within psychology.

SUBMITTER: Volpert-Esmond HI

PROVIDER: S-EPMC5899682 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials.

Volpert-Esmond Hannah I HI Merkle Edgar C EC Levsen Meredith P MP Ito Tiffany A TA Bartholow Bruce D BD

Psychophysiology 20171211 5

EEG data, and specifically the ERP, provide psychologists with the power to examine quickly occurring cognitive processes at the native temporal resolution at which they occur. Despite the advantages conferred by ERPs to examine processes at different points in time, ERP researchers commonly ignore the trial-to-trial temporal dimension by collapsing across trials of similar types (i.e., the signal averaging approach) because of constraints imposed by repeated measures ANOVA. Here, we present the ...[more]

PMID: 29226966

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Project description:Event-related potentials (ERPs) represent neuronal activity in the brain elicited by external visual or auditory stimulation and are widely used in brain-computer interface (BCI) systems. The ERP responses are elicited a few milliseconds after attending to an oddball stimulus; target and non-target stimuli are repeatedly flashed, and the ERP trials are averaged over time in order to improve their decoding accuracy. To reduce this time-consuming process, previous studies have attempted to evoke stronger ERP responses by changing certain experimental parameters like color, size, or the use of a face image as a target symbol. Since these exogenous potentials can be naturally evoked by merely looking at a target symbol, the BCI system could generate unintended commands while subjects are gazing at one of the symbols in a non-intentional mental state. We approached this problem of unintended command generation by assuming that a greater effort by the user in a short-term imagery task would evoke a discriminative ERP response. Three tasks were defined: passive attention, counting, and pitch-imagery. Users were instructed to passively attend to a target symbol, or to perform a mental tally of the number of target presentations, or to perform the novel task of imagining a high-pitch tone when the target symbol was highlighted. The decoding accuracy were 71.4%, 83.5%, and 89.2% for passive attention, counting, and pitch-imagery, respectively, after the fourth averaging procedure. We found stronger deflections in the N500 component corresponding to the levels of mental effort (passive attention: -1.094 ±0.88 μV, counting: -2.226 ±0.97 μV, and pitch-imagery: -2.883 ±0.74 μV), which highly influenced the decoding accuracy. In addition, the rate of binary classification between passive attention and pitch-imagery tasks was 73.5%, which is an adequate classification rate that motivated us to propose a two-stage classification strategy wherein the target symbols are estimated in the first stage and the passive or active mental state is decoded in the second stage. In this study, we found that the ERP response and the decoding accuracy are highly influenced by the user's voluntary mental tasks. This could lead to a useful approach in practical ERP systems in two respects. Firstly, the user-voluntary tasks can be easily utilized in many different types of BCI systems, and performance enhancement is less dependent on the manipulation of the system's external, visual stimulus parameters. Secondly, we propose an ERP system that classifies the brain state as intended or unintended by considering the measurable differences between passively gazing and actively performing the pitch-imagery tasks in the EEG signal thus minimizing unintended commands to the BCI system.

Project description:Previous research has pointed out that the combination of orthographic and semantic-associative training is a more advantageous strategy for the lexicalization of novel written word-forms than their single orthographic training. However, paradigms used previously involve explicit stimuli categorization (lexical decision), which likely influence word learning. In the present study, we used a more automatic task (silent reading) to determine the advantage of the associative training, by comparing the brain electrical signals elicited in combined (orthographic and semantic) and single (only orthographic) training conditions. In addition, the learning effect (in terms of similar neurophysiological activity between novel and known words) was also tested under a categorization paradigm, enabling determination of the possible influence of the training task in the lexicalization process. Results indicated that novel words repeatedly associated with meaningful cues showed a higher attenuation of N400 responses than those trained in the single orthographic condition, confirming the higher facilitation in the lexico-semantic processing of these stimuli, as a consequence of semantic associations. Moreover, only when the combined training was carried out in the reading task did novel words show similar N400 responses to those elicited by known words, suggesting the achievement of a similar lexical processing to known words. Crucially, when the training is carried out under a demanding task context (lexical decision), known words exhibited positive enhancement within the N400 time window, contributing to maintaining N400 differences with novel trained words and confounding the outcome of the learning. Such deflection-compatible with the modulation of the categorization-related P300 component-suggests that novel word learning could be influenced by the activation of categorization-related processes. Thus, the use of low-demand tasks arises as a more appropriate approach to study novel word learning, enabling the build-up process of mental representations, which probably depends on pure lexical and semantic factors rather than being guided by categorization demands.

Dataset Information

Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials.

Publications

Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials.

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