Reward-driven distraction: A meta-analysis.

Rusz, Dorottya; Pelley, Mike E. Le; Kompier, Michiel A. J.; Mait, Leon; Bijleveld, Erik

doi:10.1037/bul0000296

Cited by 39 publications

(47 citation statements)

References 188 publications

(360 reference statements)

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“…Cues become important in part because of their past association with rewards. Research has shown that stimuli that were rewarding in the past attract attention even when irrelevant to a current task (Rusz et al., 2020). For example, when colored shapes in an online task had previously been associated with rewards, the shapes continued to attract participants’ attention despite being irrelevant to what they were currently doing (Anderson, 2016).…”

Section: Forming Habits: Rewards For Social Media Usementioning

confidence: 99%

Habits and the electronic herd: The psychology behind social media’s successes and failures

Anderson

Wood

2020

Consumer Psychology Review

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If platforms such as Facebook, Instagram, and Twitter are the engines of social media use, what is the gasoline? The answer can be found in the psychological dynamics behind consumer habit formation and performance. In fact, the financial success of different social media sites is closely tied to the daily‐use habits they create among users. We explain how the rewards of social media sites motivate user habit formation, how social media design provides cues that automatically activate habits and nudge continued use, and how strong habits hinder quitting social media. Demonstrating that use habits are tied to cues, we report a novel test of a 2008 change in Facebook design, showing that it impeded posting only of frequent, habitual users, suggesting that the change disrupted habit automaticity. Finally, we offer predictions about the future of social media sites, highlighting the features most likely to promote user habits.

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Section: Forming Habits: Rewards For Social Media Usementioning

confidence: 99%

Habits and the electronic herd: The psychology behind social media’s successes and failures

Anderson

Wood

2020

Consumer Psychology Review

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“…There now is converging empirical evidence that attention can be captured automatically by non-salient and task-irrelevant stimuli if these stimuli are associated with either reward or punishment (Anderson 2015;Anderson & Kim, 2018;Anderson, Laurent & Yantis, 2011a, 2011bAnderson & Yantis, 2012;Gluth et al, 2020;Le Pelley et al, 2016;Rusz et al, 2020;Schmidt, Belopolsky, & Theeuwes, 2015a, 2015bWang, Yu, & Zhou, 2013;Wentura, Mueller, & Rothermund, 2014). Most of these experiments have concluded that the potential of an irrelevant distractor to capture attention is linked to the strength of its association with reward.…”

Section: Introductionmentioning

confidence: 99%

Value-driven interference in visual search: Attention to reward-associated distractors.

Koenig¹,

Torrents-Rodas²,

Üngör³

et al. 2021

Preprint

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We used an implicit learning paradigm to examine the acquisition of color-reward associations when colors were task-irrelevant and attention to color was detrimental to performance. Our task required a manual classification response to a shape target and a correct response was rewarded with either 1 or 10 cent. The amount of reward was contingent on the color of a simultaneous color distractor and different colors were associated with low reward (always 1 Cent), partial reward (randomly either 1 or 10 Cent), and high reward (always 10 Cent). Attention to color was nonstrategic for maximizing reward because it interfered with the response to the target. We examined the potential of reward-associated colors to capture and hold overt attention automatically. Reward expectancy increased with the average amount of associated reward (low < partial < high). Reward uncertainty was highest for the partially reward distractor color (low < partial > high). Results revealed that capture frequency was linked to reward expectancy, while capture duration additionally seemed to be influenced by uncertainty, complementing previous findings of such a dissociation in appetitive and aversive learning (Koenig, Kadel, Uengoer, Schubö, & Lachnit, 2017; Koenig, Uengoer, & Lachnit, 2017).

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“…A growing body of research indicates that prior experience can exert varied effects on attentional prioritisation of visual stimuli. Specifically, it has been shown learning about the value of rewards and punishments associated with a stimulus can increase the priority of that stimulus (for reviews, see Anderson, 2016;Le Pelley et al, 2016;Rusz et al, 2020;. A second stream of research has shown that statistical learning can result in attentional suppression of locations in which salient distractor stimuli are likely to appear (e.g., Failing, Feldmann-Wüstefeld, et al, 2019;Wang et al, 2019;Wang & Theeuwes, 2018a, 2018b.…”

Section: Discussionmentioning

confidence: 99%

“…One example of this influence of prior experience on attention is seen in a recent body of work demonstrating that the likelihood that a stimulus will receive attentional priority is modulated by previous learning about the relationship between that stimulus and events of motivational significance: rewards and punishments (for reviews, see Anderson, 2016;Le Pelley et al, 2016;Rusz et al, 2020;. This effect has been demonstrated across a range of procedures; here we focus on a procedure developed by Le Pelley, Pearson, Griffiths and Beesley (2015;see also Watson, Pearson, Most, et al, 2019), which was a variant of the additional singleton task (Theeuwes, 1992).…”

Section: Introductionmentioning

confidence: 99%

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

Pelley¹,

Watson²,

Theeuwes³

et al. 2020

Preprint

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Existing research demonstrates different ways in which attentional prioritisation of visual 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 distractors in visual search to investigate whether these different effects of selection history operate independently, or interact to determine overall attentional prioritisation of salient distractors. In Experiment 1, high- and low-value distractors most frequently appeared in the same location; in Experiment 2, high- 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 physical salience 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.

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Reward-driven distraction: A meta-analysis.

Cited by 39 publications

References 188 publications

Habits and the electronic herd: The psychology behind social media’s successes and failures

Habits and the electronic herd: The psychology behind social media’s successes and failures

Value-driven interference in visual search: Attention to reward-associated distractors.

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

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