ABSTRACT

Everyday social interactions hinge on our ability to resolve uncertainty in nonverbal cues. For example, although some facial expressions (e.g. happy, angry) convey a clear affective meaning, others (e.g. surprise) are ambiguous, in that their meaning is determined by the context. Here, we used mouse-tracking to examine the underlying process of resolving uncertainty. Previous work has suggested an initial negativity, in part via faster response times for negative than positive ratings of surprise. We examined valence categorizations of filtered images in order to compare faster (low spatial frequencies; LSF) versus more deliberate processing (high spatial frequencies; HSF). When participants categorised faces as “positive”, they first exhibited a partial attraction toward the competing (“negative”) response option, and this effect was exacerbated for HSF than LSF faces. Thus, the effect of response conflict due to an initial negativity bias was exaggerated for HSF faces, likely because these images allow for greater deliberation than the LSFs. These results are consistent with the notion that more positive categorizations are characterised by an initial attraction to a default, negative response.

摘要

日常社交互动取决于我们解决非语言暗示不确定性的能力。例如，虽然一些面部表情（如高兴、愤怒）传达了明确的情感含义，但其他面部表情（如惊讶）是模棱两可的，因为它们的含义是由上下文决定的。在这里，我们使用鼠标跟踪来检查解决不确定性的基本过程。以前的工作表明了最初的消极情绪，部分原因是对惊喜的消极评级比积极评级的响应时间更快。我们检查了过滤图像的价分类，以便比较更快（低空间频率；LSF）与更深思熟虑的处理（高空间频率；HSF）。当参与者将面孔归类为“正面”时，他们首先表现出对竞争（“负面”）响应选项的部分吸引力，HSF 的这种影响比 LSF 面更严重。因此，对于 HSF 人脸，由于最初的消极偏见导致的反应冲突的影响被夸大了，可能是因为这些图像比 LSFs 允许更多的审议。这些结果与更积极的分类的特征是对默认的消极反应的初始吸引力这一概念是一致的。