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激励动机能提高数字辨别能力:瞳孔测量与漂移扩散建模的结合带来的新见解。

Incentive motivation improves numerosity discrimination: Insights from pupillometry combined with drift-diffusion modelling.

机构信息

Faculty of Psychology, Chair of Lifespan Developmental Neuroscience, Technische Universität Dresden, 01062, Dresden, Germany.

Centre for Tactile Internet with Human-in-the-Loop (CeTI), Technische Universität Dresden, 01062, Dresden, Germany.

出版信息

Sci Rep. 2020 Feb 13;10(1):2608. doi: 10.1038/s41598-020-59415-3.

DOI:10.1038/s41598-020-59415-3
PMID:32054923
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7018719/
Abstract

Recent studies show that training the approximate number system (ANS) holds promise for improving symbolic math abilities. Extending this line of research, the present study aims to shed light on incentive motivation of numerosity discrimination and the underlying mechanisms. Thirty-two young adults performed a novel incentivized dot comparison task, that we developed, to discern the larger of two numerosities. An EZ-diffusion model was applied to decompose motivational effects on component processes of perceptual decision-making. Furthermore, phasic pupil dilation served as an indicator of the involvement of the salience network. The results of improved accuracy and a higher information accumulation rate under the reward condition suggest that incentive motivation boosts the precision of the ANS. These novel findings extend earlier evidence on reward-related enhancements of perceptual discrimination to the domain of numerosity perception. In light of the Adaptive Gain Theory, we interpret the results in terms of two processes of gain modulation driven by the locus coeruleus-norepinephrine system. Specifically, the reward-induced increase in pupil dilation may reflect incentive modulation of (i) salience attention during reward anticipation towards incentivized stimuli to upregulate stimulus processing that results in a larger drift rate; and (ii) response caution that leads to an increased decision threshold.

摘要

最近的研究表明,训练近似数量系统(ANS)有望提高符号数学能力。为了进一步研究,本研究旨在揭示数量区分的激励动机和潜在机制。32 名年轻人完成了一项新颖的激励点比较任务,我们开发了这项任务来辨别两个数量中的较大者。应用 EZ 扩散模型来分解激励对感知决策的各个过程的影响。此外,相位瞳孔扩张作为突显网络参与的指标。在奖励条件下提高准确性和更高的信息积累率的结果表明,激励动机提高了 ANS 的精度。这些新发现将与奖励相关的感知辨别增强的早期证据扩展到数量感知领域。根据适应性增益理论,我们从蓝斑-去甲肾上腺素系统驱动的两种增益调制过程的角度来解释结果。具体来说,瞳孔扩张的奖励诱导增加可能反映了(i)在奖励预期期间对激励刺激的注意的激励调节,以增强导致更大漂移率的刺激处理;和(ii)反应谨慎,导致决策阈值增加。

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Can monetary incentives overturn fairness-based decisions?金钱激励能否推翻基于公平的决策?
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Incentive motivation improves numerosity discrimination in children and adolescents.激励动机能提高儿童和青少年的数量辨别能力。

本文引用的文献

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The role of reward and task demand in value-based strategic allocation of auditory comprehension effort.奖励和任务需求在基于价值的听觉理解努力的策略分配中的作用。
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瞳孔反应如何在强化学习期间和之后跟踪基于价值的决策。
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Non-symbolic approximate arithmetic training improves math performance in preschoolers.非符号近似算术训练可提高学龄前儿童的数学成绩。
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The Approximate Number System Acuity Redefined: A Diffusion Model Approach.重新定义的近似数字系统敏锐度:一种扩散模型方法。
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