Late attention reflects the viewing pattern that occurs after the initial viewing pattern and is thought to reflect rumination or maintenance, which are important in theories of depression (Donaldson, Lam, & Mathews, 2007). Examples include location of the first fixation, first fixation latency, and duration of the first fixation to a salient stimulus. Early attention reflects the initial viewing pattern when stimuli are first presented and has been used to indicate initial vigilance, which may be important in threat detection (Armstrong & Olatunji, 2012). For example, if stimuli are presented for 4,000 ms, the total dwell time toward the salient stimulus is considered an indicator of overall attention. Overall attention combines early and late stage processing and reflects the viewing pattern across the total stimulus duration. Prespecified spatial (e.g., displacement) and temporal (e.g., velocity and acceleration) eye movement parameters are used to derive “fixations” and “saccades.” Fixation-based measures can be categorized according to the component of attention they are proposed to measure: overall, early, or late. To investigate such models, methods that can consistently distinguish between the temporal components of attentional processing are needed.Įyetracking continuously measures eye movements to stimuli presented on either a computer screen or mobile head centered video device. These models incorporate a temporal component of processing, broadly categorised into overall, early and late processing. For example, the vigilance-avoidance model posits that individuals may attend to a threat stimulus during initial exposure (vigilance) but then after detection, avoid the threat stimulus (avoidance) (Mogg et al., 2004). Models of attentional bias, such as the “vigilance–avoidance” model (Mogg, Bradley, Miles, & Dixon, 2004) and “threat interpretation” model (Todd et al., 2015), consider attentional bias to be dynamic attentional bias may shift toward or away from a stimulus during the stimulus exposure. Recently, attentional-bias modification training has been found to reduce symptoms of affective and pain disorders (Amir, Beard, Burns, & Bomyea, 2009 Amir, Weber, Beard, Bomyea, & Taylor, 2008 Sharpe et al., 2012). Attentional bias to threat stimuli has been identified in the development and maintenance of clinical conditions such as addiction, anxiety, depression and chronic pain (Sharpe, Haggman, Nicholas, Dear, & Refshauge, 2014 White, Suway, Pine, Bar-Haim, & Fox, 2011). Recommendations are discussed for improving the reliability of eyetracking tasks in future research.Īttentional bias describes the preferential allocation of cognitive resources to the detection of salient stimuli (Crombez, Van Ryckeghem, Eccleston, & Van Damme, 2013). All of the outcome measures, except second-run dwell time, demonstrated low measurement error (. A longer exposure time was associated with higher test–retest reliability. Sensory words had a lower mean ICC (.08) than either affective words (.32) or general threat words (.29). Reliability varied according to the outcome measure and threat word category. We used intraclass correlation coefficients (ICCs) to measure test–retest reliability (ICC >. Healthy participants completed a preferential-looking eyetracking task that involved the presentation of threatening (sensory words, general threat words, and affective words) and nonthreatening words. This study reports the test–retest reliability, measurement error, and internal consistency of 12 commonly used outcome measures thought to reflect the different components of attentional bias: overall attention, early attention, and late attention. Although some studies have investigated the internal consistency of eyetracking, data are scarce on the test–retest reliability and agreement of eyetracking to investigate attentional bias. Eyetracking is commonly used to investigate attentional bias.
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