Suppr超能文献

奖励历史调节知觉负载效应。

Reward history modulates perceptual load effects.

机构信息

Université Clermont Auvergne, CNRS, LAPSCO, F-63000 Clermont-Ferrand, France.

Université Grenoble Alpes, CNRS, LJK, F-38000 Grenoble, France.

出版信息

Acta Psychol (Amst). 2021 Jan;212:103217. doi: 10.1016/j.actpsy.2020.103217. Epub 2020 Dec 10.

Abstract

The reward history of a stimulus can yield strong attentional selection biases. Indeed, attentional capture can be triggered by previously rewarded items which are neither salient nor relevant for the ongoing task, even when selection is clearly counter-productive to actually obtain the reward outcome. Therefore, value-driven attentional capture (VDAC) has been argued to be an automatic attention mechanism. Our study aimed at putting the VDAC automaticity directly to the test. For this purpose, the Load Theory offers a comprehensive framework where distraction is observed under low but not high perceptual load condition. Nevertheless, if VDAC is indeed automatic, distraction by reward-stimuli should be observed on both perceptual load conditions. We used a feature vs. conjunction discrimination of a go/no-go cue to manipulate perceptual load. As expected, our results revealed that perceptual load decreased interference produced by low-reward distractor. However, this effect was not significant for high-reward distractor, giving support to VDAC automaticity. We discussed our results in light of the Load Theory literature and we strongly encourage to consider reward history along with perceptual load in determining attentional capture.

摘要

刺激的奖励历史可以产生强烈的注意力选择偏差。事实上,即使选择明显不利于实际获得奖励结果,先前奖励的项目也会在既不显著也与当前任务无关的情况下引发注意力捕获,即使选择明显不利于实际获得奖励结果。因此,有人认为价值驱动的注意力捕获(VDAC)是一种自动注意力机制。我们的研究旨在直接检验 VDAC 的自动性。为此,负载理论提供了一个全面的框架,其中在低但不是高知觉负载条件下观察到分心。然而,如果 VDAC 确实是自动的,那么在两种知觉负载条件下都应该观察到奖励刺激引起的分心。我们使用了 Go/No-Go 线索的特征与联合辨别来操纵知觉负载。正如预期的那样,我们的结果表明,知觉负载降低了低奖励分心物产生的干扰。然而,对于高奖励分心物,这种效果并不显著,这支持了 VDAC 的自动性。我们根据负载理论文献讨论了我们的结果,并强烈鼓励在确定注意力捕获时将奖励历史与知觉负载结合起来考虑。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验