A-193 Median- and Robust Statistics-based Attention Indices Outperform Mean-based Counterparts in Predicting Anxiety

Abstract

Abstract Objectives: Eye-tracking paradigms may measure attentional process with greater precision than parallel reaction-time-based methods by reducing construct-irrelevant systematic error (e.g., motor activity). However, the benefits of increased sensitivity offered through eye-tracking are offset by their potential susceptibility for eye-movement outliers due to instrumentation and related effects. Arithmetic means are commonly used to index attention in multi-trial tasks but are known to be biased in the presence of outliers. Median-based indices and robust mean estimators reduce outliers’ influence but are not commonly used in the literature. This study compares the value of mean-, median-, and robust-mean based eye-tracking attention shifting indices in predicting social and generalized anxiety symptoms among community-dwelling adults with various psychiatric histories. Methods: Participants (N=148) completed self-report measures (SIAS & PSWQ) and a visual attention shifting task via E-prime 3.0 and Tobii x3-120 eye-tracking system. Participants viewed 72 same-actor face pairs (sad-neutral & happy-neutral), and motivated attention shifting speed away from sad- or happy-valenced faces towards neutral faces served as predictors of interest. Arithmetic means, medians, and Tukey’s bisquare function-based robust means summarized attention shifting trials. Results. Arithmetic-mean-based indices did not predict anxiety, while median-based and robust-mean indices did: slow disengagement from sad faces predicted elevated social anxiety (b[Mdn]=.02, p=.02; b[robust-M] = .021, p =.004), and robust-mean-based indices reached trend-levels when predicting worry (ps=.055-.076), while arithmetic-mean- and median-based indices did not (ps=.43-98). Conclusion: Findings support the utility of quantifying attention processes via robust statistical approaches that attenuate outlier influence.