Cueing Effects in the Attentional Network Test: a Spotlight Diffusion Model Analysis

Abstract

The attentional network test (ANT) uses flanker stimuli with different cue conditions to quantify differences in attentional processing. However, it is unclear precisely how the alerting and orienting cues in the task affect different decision processes. The present study leveraged computational modeling to identify the relationship between attentional cues and decision components. ANT data from a large sample of 156 participants were analyzed using the spotlight diffusion model, which quantifies decision components for response caution, motor/encoding time, perceptual processing, and attentional control. The spotlight analysis showed that the attentional cues had multiple effects on decision processing. Compared to the no cue condition, an alerting cue led to faster encoding/motor speed, improved perceptual processing, and increased attentional focusing. The orienting cue further led to a decrease in response caution and increased encoding/motor speed and attentional focusing to reduce interference from incompatible flankers. This analysis demonstrates that alerting and orienting cues have complex effects on decision processes that are not captured by simple differences in RTs, and that model-based analyses can delineate such effects to allow researchers to identify precisely how attentional processing varies across individuals or conditions in tasks like the ANT.

That’s me in the spotlight: Self-relevance modulates attentional breadth

“…To confirm the origin of the observed flanker effects, data (RT & accuracy) were submitted to an additional Shrinking Spotlight (SSP) Diffusion Model analysis (White et al, 2011;White & Curl, 2018). The SSP is an extension of the Drift Diffusion Model (DDM) of decision-making and was developed to elucidate the cognitive operations that underpin performance during flanker tasks (White et al, 2011).…”

“…To estimate the parameters of the SSP, data (i.e., RT quantiles and accuracy) were submitted to the fitting procedure adopted by White and Curl (2018). Apart from the spotlight width (sd a ), all parameters (a, p, Ter, s d ) varied as a function of Target Association (i.e., self vs. friend) and were fitted separately for each participant (see Supplemental Materials for a listing of the parameter estimates).…”

“…The spotlight width (sd a ) was fixed at a value of 1 (Servant & Evans, 2020). To simplify parameter estimation, following White and Curl (2018), data from neutral flanker trials were not included in the model fitting procedure. As such, the SSP parameters for each participant and Target Association reflect the best fitting estimates for both compatible and incompatible trials simultaneously (White et al, 2011).…”

Impact of Chronic Stress on Attention Control: Evidence from Behavioral and Event-Related Potential Analyses

“…The system has three functionally and anatomically separate networks with specific and separable forms of attention, namely alarm, orientation, and executive attention. Executive attention networks are also known as collision networks [25].…”

Computational modelling approaches to meditation research: why should we care?

“…They showed that meditators adapted their level of caution more to the task conditions than controls, although the effect of meditation practice was not very strong. A critical note here is that the drift diffusion model they used was not adapted specifically to the Attention Network Task [14], which may have resulted in inaccuracies in the parameter estimates.…”