Strategy and processing speed eclipse individual differences in control ability in conflict tasks.

Hedge, Craig; Powell, Georgina; Bompas, Aline; Sumner, Petroc · 2022 · Crossref

DOI: 10.1037/xlm0001028

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Summary

This study addresses the persistent debate regarding the existence of a common underlying construct for response control (inhibition) in cognitive psychology. While response control is central to theories of executive function, correlational studies using standard conflict tasks (e.g., Stroop, Flanker, Simon) have yielded inconsistent results, leading some to question the validity of inhibition as a psychometric construct. The authors argue that traditional behavioral measures, such as reaction time (RT) and error costs, are confounded by non-conflict processes like individual differences in strategy (response caution) and general processing speed. To resolve this, the paper aims to dissociate these processes using cognitive modeling to determine whether correlations between tasks reflect shared conflict mechanisms or other factors. The researchers employed the Diffusion Model for Conflict Tasks (DMC), an evidence accumulation model that decomposes performance into parameters representing conflict processing (amplitude and time-to-peak of automatic activation), general processing efficiency (drift rate), and response strategy (boundary separation). They conducted a meta-analysis of DMC fits applied to seven empirical datasets containing combinations of the Flanker, Simon, colour-word Stroop, and spatial Stroop tasks. This analysis examined correlations between model parameters across task pairs. Additionally, the authors performed simulation studies to evaluate whether behavioral correlations are diagnostic of shared conflict mechanisms. They simulated data with known correlations in conflict parameters versus known correlations in non-conflict parameters to observe how these underlying structures manifest in observable RT and error costs. The meta-analysis revealed weak, non-significant correlations (rho < .05) between tasks for the DMC parameters representing conflict processing, providing no support for a common inhibition mechanism. In contrast, the analysis showed consistent, moderate-to-strong positive correlations for non-conflict parameters: drift rate (general processing speed, r = .32) and boundary separation (strategy/caution, r = .54). Traditional behavioral measures showed small but significant correlations for RT costs (r = .14) and error costs (r = .13). The simulation results demonstrated that correlations in strategy and processing speed can produce behavioral correlations equal to or larger than those produced by correlated conflict mechanisms. Consequently, behavioral performance correlations are not diagnostic of shared conflict processing, as they can be driven entirely by shared strategic or speed factors. The study concludes that individual differences in conflict tasks are primarily driven by general processing speed and response strategy rather than specific inhibitory control abilities. The findings suggest that the lack of correlation between conflict tasks does not necessarily refute the existence of inhibition but indicates that standard behavioral metrics are insufficient for isolating it. The authors argue that researchers must control for strategy and processing speed, likely through cognitive modeling, to accurately assess individual differences in response control. This has significant implications for both theoretical models of executive function and clinical applications, where the validity of inhibition as a distinct construct has been increasingly questioned.

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• cognitive capacity variation