Reconsidering electrophysiological markers of response inhibition in light of trigger failures in the stop‐signal task

Skippen, Patrick; Fulham, W. Ross; Michie, Patricia T.; Matzke, Dóra; Heathcote, Andrew; Karayanidis, Frini · 2020 · Psychophysiology

DOI: 10.1111/psyp.13619

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Summary

This study investigates the neural correlates of response inhibition by addressing potential biases in traditional stop-signal reaction time (SSRT) estimates. The authors argue that conventional nonparametric methods fail to account for "trigger failures"—instances where participants fail to initiate the inhibition process—leading to inflated SSRT values. To resolve this, the research employs a parametric ex-Gaussian model (EXG3) that incorporates both SSRT and trigger failure rates, allowing for a more accurate assessment of the cognitive processes underlying the stop-signal task. The study analyzed data from a large sample of 156 healthy participants who performed a stop-signal task while undergoing electroencephalogram (EEG) recording. Behavioral data were modeled using hierarchical Bayesian methods via the EXG3 framework, which estimates the distribution of finishing times for go and stop processes, including probabilities of go and trigger failures. Electrophysiological data were processed to isolate event-related potentials (ERPs), specifically the N1 and P3 components, using surface Laplacian transformations and ADJAR correction to remove overlapping activity from preceding go stimuli. The researchers compared ERP metrics (latency and amplitude) against both traditional SSRT estimates (SSRTint) and the parametric EXG3 estimates (SSRTEXG3). Results indicated that the EXG3-derived SSRT (SSRTEXG3) was approximately 65 ms faster than traditional estimates. Both the fronto-central N1 and P3 components peaked earlier and exhibited larger amplitudes on stop-success trials compared to stop-failure trials. Crucially, for stop-failure trials, N1 peak latency correlated with SSRTEXG3 and trigger failure rates, temporally coinciding with the parametric SSRT estimate but not the traditional one. In contrast, P3 peak and onset latencies showed no association with any behavioral estimates of inhibition. Furthermore, the typical N1 latency difference between success and failure trials was absent in poor performers characterized by high trigger failure rates. These findings suggest that attentional mechanisms, rather than pure inhibitory speed, play a critical role in the reliability and speed of response inhibition. The study challenges the prevailing interpretation of the P3 component as the primary neural marker of the inhibition process, given its lack of association with behavioral inhibition metrics when trigger failures are accounted for. Instead, the N1 component appears more closely linked to the initiation of the stop process and attentional engagement with the stop signal. The results imply that effective inhibitory control depends on the successful triggering of the inhibition process, which is modulated by attention, particularly in individuals with lower performance reliability.

Key finding

The N1 component's peak latency correlates with parametric stop-signal reaction time and trigger failure rates, while the P3 component shows no significant association with behavioral estimates of inhibition, suggesting attentional mechanisms underpin response inhibition speed and reliability.

Methodology

lab_experiment

Sample size: 156

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• executive function