Attentional guidance by working memory differs by paradigm: An individual-differences approach

Abstract

The contents of working memory (WM) have been repeatedly found to guide the allocation of visual attention; in a dual-task paradigm that combines WM and visual search, actively holding an item in WM biases visual attention towards memory-matching items during search (e.g., Soto et al., Journal of Experimental Psychology: Human Perception and Performance, 31(2), [248][249][250][251][252][253][254][255][256][257][258][259][260][261] 2005). A key debate is whether such memory-based attentional guidance is automatic or under strategic control. Generally, two distinct task paradigms have been employed to assess memory-based guidance, one demonstrating that attention is involuntarily captured by memorymatching stimuli even at a cost to search performance (Soto et al., 2005), and one demonstrating that participants can strategically avoid memory-matching distractors to facilitate search performance (Woodman & Luck, Journal of Experimental Psychology: Human Perception and Performance, 33(2), 363-377, 2007). The current study utilized an individualdifferences approach to examine why the different paradigms-which presumably tap into the same attentional construct-might support contrasting interpretations. Participants completed a battery of cognitive tasks, including two types of attentional guidance paradigms (see Soto et al., 2005;), a visual WM task, and an operation span task, as well as attention-related self-report assessments. Performance on the two attentional guidance paradigms did not correlate. Subsequent exploratory regression analyses revealed that memory-based guidance in each task was differentially predicted by visual WM capacity for one paradigm, and by attentionrelated assessment scores for the other paradigm. The current results suggest that these two paradigms-which have previously produced contrasting patterns of performancemay probe distinct aspects of attentional guidance.

Decoding working memory content from attentional biases

“…To test this, we examined the correlation between classifier performance and a coarse measure of the magnitude of attentional bias, as calculated from search RTs: (invalid – valid)/neutral. This normalized difference score reflects the absolute RT difference as a proportion of neutral RTs, such that positive values indicate greater attentional bias (Dowd et al, 2015b). Individual differences in classification accuracy for all three approaches were significantly predicted by the magnitude of attentional bias, with R 2 values ranging from .30 to .44, p s < .001 (Figure 4b).…”

“…Given the variation in standard univariate validity effects across individuals (see Dowd et al, 2015b), the generalization of decoding WM contents from attention bias was unknown. Thus, the current study explicitly tested whether a classifier trained on data from a group of individuals could predict single-trial WM content in another, new individual.…”

“…Mean group effects do not necessarily translate into successful classification of categorical data at the single-trial level (see Franz & von Luxburg, 2015), as aggregate effects can be driven by a subset of trials or subjects, and/or the signal-to-noise ratio of a given effect measurement might be too small for reliable single-trial inference. Previous work has also demonstrated high variability in WM-based attentional effects across individuals, even within the same task (e.g., Dowd, Kiyonaga, Egner, & Mitroff, 2015b). Thus, in the present study, we asked whether the effects of WM maintenance on attentional orienting are robust enough to be used diagnostically for inferring a person’s mental content.…”

Fear generalization gradients in visuospatial attention.

“…To better assess fear generalization gradients in attentional bias across the tested color range, we calculated a singular measure of attentional bias for each individual: (invalid RT – valid RT)/(neutral RT). This normalized difference score reflects the absolute RT difference as a proportion of neutral RT, such that positive values indicate greater attentional bias (see Dowd, Kiyonaga, Egner, & Mitroff, 2015). A repeated-measures ANOVA again revealed a significant quadratic trend, F (1, 39) = 5.37, p = .026, η p 2 = .12, indicating a continuous and graded inverse -shaped curve in attentional bias (Figure 2D).…”

If at first you don’t succeed, try, try again: Second chances reveal more information in working memory

“…The first response was a continuous report-a commonly employed method for assessing the existence and quality of working memory representations (e.g. Bae et al, 2014;Bays et al, 2009;Bays et al, 2011;Brady & Alvarez, 2015;Dowd et al, 2015;Fougnie & Alvarez, 2011;Fougnie et al, 2012;Wilken & Ma, 2004;Williams et al, 2013;Van den Berg et al, 2012;Zhang & Luck, 2008). The probed location was highlighted by a solid white circle (non-probed locations had a white circle outline).…”