Reward-priming impacts visual working memory maintenance: Evidence from human electrophysiology

Infanti, Elisa; Hickey, Clayton; Menghi, Nicholas; Turatto, Massimo

doi:10.1080/13506285.2017.1365790

Cited by 8 publications

(14 citation statements)

References 70 publications

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“…Limited evidence suggests that implicit learning of the features associated with a reward increases the likelihood that they are encoded into WM rather than increasing representational quality. In contrast, neither the strategic use of reward (Thomas et al, 2016) nor the priming of reward associations (Infanti et al, 2017) seems to have a strong effect on creating an encoding bias. The modulation of attention from reward learning may be due to dopaminergic processes that are important for habit formation and valuebased decision making.…”

Section: Reward Learningmentioning

confidence: 84%

“…Thomas et al (2016) found that eye gaze during WM encoding was not contingent upon reward value, but instead was driven by physical salience. Similarly, explicit reward feedback from the previous trial had no effect on attentional selection at encoding (measured using N2pc ERPs), but better representation in WM, as indexed by higher contralateral delay activity (Infanti et al, 2017). As reward associations were available to consciousness, it is possible that participants used strategic attention to mitigate the influence of reward when it was irrelevant to the WM task.…”

Section: Reward Learningmentioning

confidence: 95%

“…As reward associations were available to consciousness, it is possible that participants used strategic attention to mitigate the influence of reward when it was irrelevant to the WM task. Furthermore, the reward priming effects demonstrated by Infanti et al (2017) may directly enhance memory because of recent occupation in WM from the previous trial rather than forming an attentional bias from repeated experiences with stimulus-reward pairings. Thus, the limited evidence to date supports a benefit to selection rather than more representational benefits when implicit learning and strategic attention are dissociable.…”

Section: Reward Learningmentioning

confidence: 99%

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Gotcha: Working memory prioritization from automatic attentional biases

Ravizza

Conn

2021

Psychon Bull Rev

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Attention is an important resource for prioritizing information in working memory (WM), and it can be deployed both strategically and automatically. Most research investigating the relationship between WM and attention has focused on strategic efforts to deploy attentional resources toward remembering relevant information. However, such voluntary attentional control represents a mere subset of the attentional processes that select information to be encoded and maintained in WM (Theeuwes, Journal of Cognition, 1[1]: 29, 1-15, 2018). Here, we discuss three ways in which information becomes prioritized automatically in WMphysical salience, statistical learning, and reward learning. This review integrates findings from perception and working memory studies to propose a more sophisticated understanding of the relationship between attention and working memory.

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Section: Reward Learningmentioning

confidence: 84%

Section: Reward Learningmentioning

confidence: 95%

Section: Reward Learningmentioning

confidence: 99%

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Gotcha: Working memory prioritization from automatic attentional biases

Ravizza

Conn

2021

Psychon Bull Rev

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“…This approach is particularly useful when some items in the visual environment are associated with higher reward values. Such an ability has already been reported in the context of prelearning manipulations, in which certain stimuli are preassociated before the memory experiment with different monetary values (Gong & Li, 2014;Infanti, Hickey, Menghi, & Turatto, 2017;Thomas, FitzGibbon, & Raymond, 2016;Wallis, Stokes, Arnold, & Nobre, 2015). It has recently been extended to more online processing and explored experimentally in a series of studies in which participants were instructed to remember a sequence of visual stimuli, but to prioritize certain items from within this sequence based on associated reward values (Atkinson et al, 2018;Hitch, Hu, Allen, & Baddeley, 2018;Hu, Allen, Baddeley, & Hitch, 2016;Hu, Hitch, Baddeley, Zhang, & Allen, 2014).…”

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confidence: 93%

Multiple high-reward items can be prioritized in working memory but with greater vulnerability to interference

Allen

Ueno

2018

Atten Percept Psychophys

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Emerging literature indicates that working memory and attention interact in determining what is retained over time, though the nature of this relationship and the impacts on performance across different task contexts remain to be mapped. In the present study, four experiments examined whether participants can prioritize one or more high-reward items within a four-item target array for the purposes of an immediate cued recall task, and the extent to which this mediates the disruptive impact of a postdisplay to-be-ignored suffix. All four experiments indicated that endogenous direction of attention toward high-reward items results in their improved recall. Furthermore, increasing the number of high-reward items from one to three (Experiments 1-3) produces no decline in recall performance for those items, while associating each item in an array with a different reward value results in correspondingly graded levels of recall performance (Experiment 4). These results suggest the ability to exert precise voluntary control in the prioritization of multiple targets. However, in line with recent outcomes drawn from serial visual memory, this endogenously driven focus on high-reward items results in greater susceptibility to exogenous suffix interference, relative to low-reward items. This contrasts with outcomes from cueing paradigms, indicating that different methods of attentional direction may not always result in equivalent outcomes on working memory performance.

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“…However, empirical results have revealed a complex relationship between reward and working memory. Under some circumstances, reward did improve working memory performance (Gilbert and Fiez, 2004;Taylor et al, 2004;Rowe et al, 2008;Beck et al, 2010;Savine et al, 2010;Marquand et al, 2011;Kawasaki and Yamaguchi, 2013;Sandry et al, 2014;Choi et al, 2015;Hammer et al, 2015;Fairclough and Ewing, 2017;Hefer and Dreisbach, 2017;Heritage et al, 2017;Klink et al, 2017;Allen and Ueno, 2018;Anna and Anna, 2018;Thurm et al, 2018;Gaillard et al, 2019;Magis-Weinberg et al, 2019;Manga et al, 2020;Sandry and Ricker, 2020), while other studies did not find the reward effect on working memory accuracy (Pochon et al, 2002;Krawczyk et al, 2007;Beck et al, 2010;Hager et al, 2015;Infanti et al, 2017;Smith et al, 2017;Fairclough et al, 2018;Di Rosa et al, 2019). A potential factor mediating the reward effect is working memory load.…”

Section: Introductionmentioning

confidence: 93%

Reward Expectation Differentially Modulates Global and Local Spatial Working Memory Accuracy

2021

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Although it has been suggested that reward expectation affects the performance of spatial working memory tasks, controversial results have been found in previous experiments. Hence, it is still unclear to what extent reward expectation has an effect on working memory. To clarify this question, a memory-guided saccade task was applied, in which participants were instructed to retain and reconstruct a temporospatial sequence of four locations by moving their eyes in each trial. The global- and local-level spatial working memory accuracies were calculated to determine the reward effect on the global and local level of processing in spatial working memory tasks. Although high reward expectation enhanced the encoding of spatial information, the percentage of trials in which the cued location was correctly fixated decreased with increment of reward expectation. The reconstruction of the global temporospatial sequence was enhanced by reward expectation, whereas the local reconstruction performance was not affected by reward. Furthermore, the improvements in local representations of uncued locations and local sequences were at the cost of the representation of cued locations. The results suggest that the reward effect on spatial working memory is modulated by the level of processing, which supports the flexible resource theory during maintenance.

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Reward-priming impacts visual working memory maintenance: Evidence from human electrophysiology

Cited by 8 publications

References 70 publications

Gotcha: Working memory prioritization from automatic attentional biases

Gotcha: Working memory prioritization from automatic attentional biases

Multiple high-reward items can be prioritized in working memory but with greater vulnerability to interference

Reward Expectation Differentially Modulates Global and Local Spatial Working Memory Accuracy

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