Reward learning and statistical learning independently influence attentional priority of salient distractors in visual search

Mike E Le Pelley,Daniel Pearson,Rhonda Ung,Jan Theeuwes,Poppy Watson,Chisato Mine,Steven B Most

doi:10.3758/s13414-021-02426-7

Abstract

Existing research demonstrates different ways in which attentional prioritization of salient nontarget stimuli is shaped by prior experience: Reward learning renders signals of high-value outcomes more likely to capture attention than signals of low-value outcomes, whereas statistical learning can produce attentional suppression of the location in which salient distractor items are likely to appear. The current study combined manipulations of the value and location associated with salient distractors in visual search to investigate whether these different effects of selection history operate independently or interact to determine overall attentional prioritization of salient distractors. In Experiment 1, high-value and low-value distractors most frequently appeared in the same location; in Experiment 2, high-value and low-value distractors typically appeared in distinct locations. In both experiments, effects of distractor value and location were additive, suggesting that attention-promoting effects of value and attention-suppressing effects of statistical location-learning independently modulate overall attentional priority. Our findings are consistent with a view that sees attention as mediated by a common priority map that receives and integrates separate signals relating to physical salience and value, with signal suppression based on statistical learning determined by physical salience, but not incentive salience.

Highlights

Attention refers to the set of cognitive mechanisms that act to prioritize certain aspects of incoming sensory information for further analysis and action and suppress other aspects that might otherwise interfere with our ongoing information processing
Attention is influenced by learning about the value associated with search targets: If responding to a particular stimulus has previously yielded high reward, attention will prioritize that stimulus in future, relative to a target paired with low reward (e.g., Kiss et al, 2009; Kristjansson et al, 2010; O’Brien & Raymond, 2012; Seitz et al, 2009)
Consistent with previous findings (e.g., Le Pelley et al, 2015; Watson, Pearson, Most, et al, 2019a), responding to the target in Experiment 1 was slower when the search display contained a high-value distractor versus a low-value distractor. This implies that participants were more likely to attend to the high-value distractor, interfering with search for the target. This effect of distractor value was counterproductive, since response times influenced the points earned in the search task: By responding more slowly on trials in which higher rewards were available, participants lost out disproportionately

Summary

Introduction

Attention refers to the set of cognitive mechanisms that act to prioritize certain aspects of incoming sensory information for further analysis and action and suppress other aspects that might otherwise interfere with our ongoing information processing. This suggests the high-value distractor was more likely to receive attentional priority, interfering with search for the target—even though this behaviour was counterproductive, as it meant participants earned less on high-value trials than would otherwise have been the case Such findings demonstrate a qualitatively similar pattern of attentional bias to reward-signalling nontarget stimuli as has previously been shown for targets: In both cases, stimuli associated with high value are prioritized over those signalling low value. Influences of value and location have been shown to combine additively to determine attentional priority for targets (Garner et al, 2021; Stankevich & Geng, 2014); given the somewhat different way in which these factors modulate attention with regard to distractors—and the possibility that use of distractors may implicate a distinct system of attentional control based on selection history—a natural question to ask is whether value and location operate independently in this case. An alternative possibility is that learned suppression is a reactive process, such that high-value (and high salience) distractors elicit greater suppression than low-value distractors, which would manifest as an interaction between effects of value and location on overall priority (cf. Failing & Theeuwes, 2020)

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