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

Craig Hedge,Georgina Powell,Aline Bompas,Petroc Sumner

doi:10.1037/xlm0001028

Abstract

Response control or inhibition is one of the cornerstones of modern cognitive psychology, featuring prominently in theories of executive functioning and impulsive behavior. However, repeated failures to observe correlations between commonly applied tasks have led some theorists to question whether common response conflict processes even exist. A challenge to answering this question is that behavior is multifaceted, with both conflict and nonconflict processes (e.g., strategy, processing speed) contributing to individual differences. Here, we use a cognitive model to dissociate these processes; the diffusion model for conflict tasks (Ulrich et al., 2015). In a meta-analysis of fits to seven empirical datasets containing combinations of the flanker, Simon, color-word Stroop, and spatial Stroop tasks, we observed weak (r < .05) zero-order correlations between tasks in parameters reflecting conflict processing, seemingly challenging a general control construct. However, our meta-analysis showed consistent positive correlations in parameters representing processing speed and strategy. We then use model simulations to evaluate whether correlations in behavioral costs are diagnostic of the presence or absence of common mechanisms of conflict processing. We use the model to impose known correlations for conflict mechanisms across tasks, and we compare the simulated behavior to simulations when there is no conflict correlation across tasks. We find that correlations in strategy and processing speed can produce behavioral correlations equal to, or larger than, those produced by correlated conflict mechanisms. We conclude that correlations between conflict tasks are only weakly informative about common conflict mechanisms if researchers do not control for strategy and processing speed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Highlights

We based our parameter ranges on a previous parameter recovery study (White et al, 2018), which themselves were based on previous studies that had applied the DMC (Servant et al, 2016; Ulrich et al, 2015)
The overarching questions we address here are: is there a common mechanism of conflict processing underlying performance across ‘inhibition’ tasks and, if there were, would we be able to detect it from RT and error costs? Our data and simulations suggest the presence or absence of correlations across conflict tasks is only weakly informative as to whether common conflict control mechanisms underlie performance
Summary and Conclusions In Part I of this article, a meta-analysis showed no evidence for correlated conflict mechanisms, and robust evidence for correlations in strategy and processing speed across tasks

Summary

Introduction

We based our parameter ranges on a previous parameter recovery study (White et al, 2018), which themselves were based on previous studies that had applied the DMC (Servant et al, 2016; Ulrich et al, 2015). White et al observed high correlations between simulated and recovered parameters .93 for all parameters when shape is held constant), so we can be confident that these ranges produce discriminable variation in behavior White et al observed high correlations between simulated and recovered parameters (r . .93 for all parameters when shape is held constant), so we can be confident that these ranges produce discriminable variation in behavior

Methods

Discussion

Conclusion