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Emotion and language: Valence and arousal affect word recognition

机译:情感和语言:价和唤醒会影响单词识别

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摘要

Emotion influences most aspects of cognition and behavior, but emotional factors are conspicuously absent from current models of word recognition. The influence of emotion on word recognition has mostly been reported in prior studies on the automatic vigilance for negative stimuli, but the precise nature of this relationship is unclear. Various models of automatic vigilance have claimed that the effect of valence on response times is categorical, an inverted-U, or interactive with arousal. The present study used a sample of 12,658 words, and included many lexical and semantic control factors, to determine the precise nature of the effects of arousal and valence on word recognition. Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects: Negative words are recognized more slowly than positive words, and arousing words are recognized more slowly than calming words. Valence explained about 2% of the variance in word recognition latencies, whereas the effect of arousal was smaller. Valence and arousal do not interact, but both interact with word frequency, such that valence and arousal exert larger effects among low-frequency words than among high-frequency words. These results necessitate a new model of affective word processing whereby the degree of negativity monotonically and independently predicts the speed of responding. This research also demonstrates that incorporating emotional factors, especially valence, improves the performance of models of word recognition.
机译:情感会影响认知和行为的大多数方面,但是目前的单词识别模型显然缺少情感因素。在先前对消极刺激的自动警惕的研究中,情绪对单词识别的影响大多已被报道,但是这种关系的确切性质尚不清楚。各种自动警戒模型声称,价数对响应时间的影响是绝对的,倒U形的或唤醒的。本研究使用了一个12658个单词的样本,并包括许多词汇和语义控制因素,以确定唤醒和效价对单词识别的精确性质。词汇判断和命名在词级和试验级数据中观察到的经验模式的融合表明,价和激发起独立的单调作用:否定词的识别速度比肯定词慢,而唤醒词的识别速度比平静词慢。价位解释了单词识别潜伏期中方差的2%,而唤醒的影响较小。价和唤醒不相互作用,但是两者都与词频相互作用,因此,价和唤醒在低频词中的作用要大于高频词。这些结果需要一种情感词处理的新模型,由此否定度可以单调且独立地预测响应速度。这项研究还表明,结合情绪因素,尤其是价数,可以改善单词识别模型的性能。

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