Abstract
Although the N400 was originally discovered in a paradigm designed to elicit a P300 (Kutas and Hillyard, 1980), its relationship with the P300 and how both overlapping event-related potentials (ERPs) determine behavioral profiles is still elusive. Here we conducted an ERP (N = 20) and a multiple-response speed-accuracy tradeoff (SAT) experiment (N = 16) on distinct participant samples using an antonym paradigm (The opposite of black is white/nice/yellow with acceptability judgment). We hypothesized that SAT profiles incorporate processes of task-related decision-making (P300) and stimulus-related expectation violation (N400). We replicated previous ERP results (Roehm et al., 2007): in the correct condition (white), the expected target elicits a P300, while both expectation violations engender an N400 [reduced for related (yellow) vs. unrelated targets (nice)]. Using multivariate Bayesian mixed-effects models, we modeled the P300 and N400 responses simultaneously and found that correlation between residuals and subject-level random effects of each response window was minimal, suggesting that the components are largely independent. For the SAT data, we found that antonyms and unrelated targets had a similar slope (rate of increase in accuracy over time) and an asymptote at ceiling, while related targets showed both a lower slope and a lower asymptote, reaching only approximately 80% accuracy. Using a GLMM-based approach (Davidson and Martin, 2013), we modeled these dynamics using response time and condition as predictors. Replacing the predictor for condition with the averaged P300 and N400 amplitudes from the ERP experiment, we achieved identical model performance. We then examined the piecewise contribution of the P300 and N400 amplitudes with partial effects (see Hohenstein and Kliegl, 2015). Unsurprisingly, the P300 amplitude was the strongest contributor to the SAT-curve in the antonym condition and the N400 was the strongest contributor in the unrelated condition. In brief, this is the first demonstration of how overlapping ERP responses in one sample of participants predict behavioral SAT profiles of another sample. The P300 and N400 reflect two independent but interacting processes and the competition between these processes is reflected differently in behavioral parameters of speed and accuracy.
Highlights
Human cognition can be conceived of as a dynamic, hierarchically organized system of decision-making or categorization that accumulates evidence for categories as new incoming sensory information is processed across time, and translates the outcome of this categorization to appropriate action once a decision threshold has been reached (Gold and Shadlen, 2007; Kelly and O’Connell, 2015).Language is no exception to this: linguistic categorization is a dynamic process in which evidence from stimulus properties from lower to higher linguistic levels is accumulated across time, shaped by both stimulus-induced processes as well as decision-related processes
While aligning event-related potentials (ERPs) patterns with behavioral patterns descriptively via inspection of their respective effect directions and sizes is not uncommon, it clearly suffers from two methodological challenges, summarized in (i) and (ii) below: For the first issue, we propose that the speed-accuracy tradeoff (SAT) paradigm is better suited than standard reaction time (RT) measures to discover the time-course of decision-making during sentence categorization
For introducing our novel modeling approach and keeping model complexity reasonable, we focus on temporal overlap of the N400 and P300 occurring in the largely overlapping time windows, as the ERP methodology in sentence and word processing is still more often used to make inferences based on the temporal dimension, rather than on an integrated spatiotemporal profile
Summary
Human cognition can be conceived of as a dynamic, hierarchically organized system of decision-making or categorization that accumulates evidence for (alternative) categories as new incoming sensory information is processed across time, and translates the outcome of this categorization to appropriate action once a decision threshold has been reached (Gold and Shadlen, 2007; Kelly and O’Connell, 2015).Language is no exception to this: linguistic categorization is a dynamic process in which evidence from stimulus properties from lower to higher linguistic levels is accumulated across time, shaped by both stimulus-induced (exogeneous) processes as well as decision-related (endogenous) processes. Cross-method divergence results in part from two well-known complications, namely that N400 and P300 overlap in time and scalp topography despite their different cognitive functions, and that standard RT and/or ER measures rely on a single data point insensitive to the dynamics of categorization. This makes it difficult to unify, across electrophysiological and behavioral measures, effects of contextual predictability and semantic relatedness in signatures of stimulus processing and categorization at the word or sentence level
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
