Capacity-aligned feedback enhances visual working memory performance

Because Visual Working Memory (VWM) capacity is limited, previous research has aimed to improve VWM with feedback and monetary incentives. However, the effects of feedback and incentives on VWM have been mixed. Here, we propose an “optimal strategy” account to explain when feedback will be effective versus ineffective at improving VWM performance. We hypothesized that incentivizing individuals with performance goals that are aligned to their capacity would yield higher average performance and fewer performance failures (“lapses”). Participants completed a whole-report VWM task and we varied the performance goal needed to earn bonus money on each trial (e.g., “Remember 3!”). In Experiments 1a and 1b, performance goals varied from 1 to 5 items, and we manipulated whether goals were blocked (Experiment 1a) or intermixed (Experiment 1b). In Experiment 2 (pre-registered), we replicated Experiment 1a with a larger sample size to examine individual differences in optimal VWM performance. Across all three experiments, group-averaged optimal VWM performance corresponded to goals that aligned to typical group-level capacity (~3 items). In contrast, supra-capacity goals (e.g., “remember 5 items”) tended to harm VWM performance by increasing lapses, consistent with an overload effect. In Experiment 2, we found that participants performed best when goals corresponded to their individual capacity estimate, and replicated overload effects at supra-capacity goals. However, we found a trade-off between metacognitive accuracy and optimal VWM performance. Altogether, our results suggest that VWM performance improvements rely on feedback that encourages the allocation of typical capacity limits, rather than encouraging maximizing performance.

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Publication Details

Published
2025-09-22
DOI
https://doi.org/10.31234/osf.io/tayck_v1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Capacity-aligned feedback enhances visual working memory performance

Olga Kozlova, Kirsten Adam
Advanced Memory and Neural Computing
article

Capacity-aligned feedback enhances visual working memory performance

Olga Kozlova, Kirsten Adam
article en

Abstract

Because Visual Working Memory (VWM) capacity is limited, previous research has aimed to improve VWM with feedback and monetary incentives. However, the effects of feedback and incentives on VWM have been mixed. Here, we propose an “optimal strategy” account to explain when feedback will be effective versus ineffective at improving VWM performance. We hypothesized that incentivizing individuals with performance goals that are aligned to their capacity would yield higher average performance and fewer performance failures (“lapses”). Participants completed a whole-report VWM task and we varied the performance goal needed to earn bonus money on each trial (e.g., “Remember 3!”). In Experiments 1a and 1b, performance goals varied from 1 to 5 items, and we manipulated whether goals were blocked (Experiment 1a) or intermixed (Experiment 1b). In Experiment 2 (pre-registered), we replicated Experiment 1a with a larger sample size to examine individual differences in optimal VWM performance. Across all three experiments, group-averaged optimal VWM performance corresponded to goals that aligned to typical group-level capacity (~3 items). In contrast, supra-capacity goals (e.g., “remember 5 items”) tended to harm VWM performance by increasing lapses, consistent with an overload effect. In Experiment 2, we found that participants performed best when goals corresponded to their individual capacity estimate, and replicated overload effects at supra-capacity goals. However, we found a trade-off between metacognitive accuracy and optimal VWM performance. Altogether, our results suggest that VWM performance improvements rely on feedback that encourages the allocation of typical capacity limits, rather than encouraging maximizing performance.

Arcadia Fund
Openalex Percentile: Top 75%
Advanced Memory and Neural Computing
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