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使用眼动追踪技术测试对有吸引力面孔的注意力的个体差异。

Using eye tracking to test for individual differences in attention to attractive faces.

作者信息

Valuch Christian, Pflüger Lena S, Wallner Bernard, Laeng Bruno, Ansorge Ulrich

机构信息

Cognitive Science Research Platform, University of Vienna Vienna, Austria ; Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna Vienna, Austria.

Cognitive Science Research Platform, University of Vienna Vienna, Austria ; Department of Anthropology, Faculty of Life Sciences, University of Vienna Vienna, Austria.

出版信息

Front Psychol. 2015 Feb 2;6:42. doi: 10.3389/fpsyg.2015.00042. eCollection 2015.

Abstract

We assessed individual differences in visual attention toward faces in relation to their attractiveness via saccadic reaction times. Motivated by the aim to understand individual differences in attention to faces, we tested three hypotheses: (a) Attractive faces hold or capture attention more effectively than less attractive faces; (b) men show a stronger bias toward attractive opposite-sex faces than women; and (c) blue-eyed men show a stronger bias toward blue-eyed than brown-eyed feminine faces. The latter test was included because prior research suggested a high effect size. Our data supported hypotheses (a) and (b) but not (c). By conducting separate tests for disengagement of attention and attention capture, we found that individual differences exist at distinct stages of attentional processing but these differences are of varying robustness and importance. In our conclusion, we also advocate the use of linear mixed effects models as the most appropriate statistical approach for studying inter-individual differences in visual attention with naturalistic stimuli.

摘要

我们通过眼跳反应时间评估了个体对不同吸引力面孔的视觉注意力差异。出于理解对面孔注意力个体差异的目的,我们检验了三个假设:(a) 有吸引力的面孔比吸引力较低的面孔更能有效地吸引或抓住注意力;(b) 男性比女性对有吸引力的异性面孔表现出更强的偏好;(c) 蓝眼睛男性对蓝眼睛女性面孔比对棕色眼睛女性面孔表现出更强的偏好。纳入后一项检验是因为先前的研究表明效应量较大。我们的数据支持假设(a)和(b),但不支持(c)。通过分别进行注意力脱离和注意力捕获的测试,我们发现个体差异存在于注意力加工的不同阶段,但这些差异的稳健性和重要性各不相同。在结论中,我们还提倡使用线性混合效应模型作为研究自然刺激下视觉注意力个体差异的最合适统计方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/457b/4313586/6dce38649e90/fpsyg-06-00042-g001.jpg

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