Related Experiment Video
Updated: Oct 16, 2025

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
Published on: July 5, 2015
Lessons unlearned: A conceptual review and meta-analysis of the relationship between the Attention Control Scale and
Patrick J F Clarke1, Jemma Todd2
1Affective, Behavioural, and Cognitive Neuroscience Group, Curtin University, Bentley, WA, Australia.
Abstract:
Attention control is central to many models of emotion. Among the most common measures of attention, control is the Attention Control Scale (ACS), which has exerted considerable influence in terms of the volume and breadth of research findings, with its use in cognitive-experimental research continuing to increase in recent years. However, there are growing concerns about whether the ACS genuinely indexes attention control. The present paper considers the context and development of the ACS, reviews and meta-analyses the available evidence regarding its association with objective measures of attention control. Meta-analytic results from nine studies (total n = 1274) indicated that the full-scale ACS was not significantly associated with behavioural measures of attentional control (r = .067, p = .093, N = 1274, 95% CI: -.011, .145). Findings indicated likely missing studies with lower correlations suggesting the true association may be smaller. Limited evidence of shared variance between subjective and objective measures of attention control contrasts with considerable evidence that the scale is closely correlated with dispositional traits (e.g. anxiety, agreeableness) that could plausibly influence responding. Thus, on the balance of current findings, we conclude that there is little compelling evidence that responding on the ACS reflects genuine attention control abilities.
More Related Videos
06:46Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
Published on: March 18, 2019
13:00Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017