Emotion, Etmnooi, or Emitoon? – Faster lexical access to emotional than to neutral words during reading

Kißler, Johanna; Herbert, Cornelia

doi:10.1016/j.biopsycho.2012.09.004

Cited by 138 publications

(126 citation statements)

References 66 publications

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“…Recent evidence suggests not only that positive words are processed faster than negative and neutral words but also that lexical access (i.e., the time it takes until a word is identified) is fastest in positive words (Kissler & Herbert, 2013). This facilitatory effect is also reflected in accuracy data where fewest errors are observed for positive words (Briesemeister et al, 2011a;Kousta et al, 2009;Kuchinke & Lux, 2012).…”

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confidence: 55%

“…Ratcliff et al (2004) called this an effect of wordness, that is, words with higher frequency appear more word-like and thus evidence in favor of a BWORD^response is accumulated faster. Similarly, it has been discussed based on models of word recognition that positive words are identified fastest (Kissler & Herbert, 2013). Interestingly, the only computational model of the LDT that also incorporates emotion effects, the MROMe (extended multiple read-out model; Kuchinke, 2007), successfully simulates faster RTs of emotional words by enhanced lexical activity at a proposed word level based on an affective mechanism operating on associated affective information.…”

Section: Discussionmentioning

confidence: 99%

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Individual differences in emotion word processing: A diffusion model analysis

Mueller

Kuchinke

2016

Cogn Affect Behav Neurosci

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The exploratory study investigated individual differences in implicit processing of emotional words in a lexical decision task. A processing advantage for positive words was observed, and differences between happy and fear-related words in response times were predicted by individual differences in specific variables of emotion processing: Whereas more pronounced goal-directed behavior was related to a specific slowdown in processing of fear-related words, the rate of spontaneous eye blinks (indexing brain dopamine levels) was associated with a processing advantage of happy words. Estimating diffusion model parameters revealed that the drift rate (rate of information accumulation) captures unique variance of processing differences between happy and fear-related words, with highest drift rates observed for happy words. Overall emotion recognition ability predicted individual differences in drift rates between happy and fear-related words. The findings emphasize that a significant amount of variance in emotion processing is explained by individual differences in behavioral data.

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confidence: 55%

Section: Discussionmentioning

confidence: 99%

Individual differences in emotion word processing: A diffusion model analysis

Mueller

Kuchinke

2016

Cogn Affect Behav Neurosci

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“…Even most word recognition and reading models do not consider affective influences, although the extended multiple read-out model (MROMe) and the neurocognitive poetics models are notable exceptions (Kuchinke, 2007;Jacobs, 2011Jacobs, , 2014; see also Hofmann & Jacobs 2014). The MROMe predicts that affective information will facilitate LDRTs at a prelexical level (see also Kissler & Herbert, 2013, for first empirical evidence), although it does not make a distinction between discrete emotions and affective dimensions, and therefore cannot account for possible interactions. Moreover, the MROMe is not a neurophysiological model.…”

Section: Discussionmentioning

confidence: 99%

Emotions in reading: Dissociation of happiness and positivity

Briesemeister

Kuchinke

Jacobs

et al. 2014

Cogn Affect Behav Neurosci

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The hierarchical emotion model proposed by Panksepp (1998) predicts that affective processing will rely on three functionally and neuroanatomically distinct levels, engaging subcortical networks (primary level), the limbic system (secondary level), and the neocortex (tertiary level). In the present fMRI study, we manipulated happiness and positivity, which are assumed to rely on secondary-and tertiary-level processes, respectively, to test these assumptions in a word recognition task. In accordance with the model predictions, evidence for a double dissociation was found in the brain activation patterns: Secondary-level processes engaged parts of the limbic system-specifically, the right hemispheric amygdala. Tertiary-level processes, in contrast, relied predominantly on frontal neocortical structures such as the left inferior frontal and medial frontal gyri. These results are interpreted as support for Panksepp's (1998) model and as an indicator of a semantic foundation of affective dimensions.

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“…In the literature, the typical studies concerning opinion mining concentrate on the (very) positive and (very) negative emotion words detected in texts [21], the so-called "positive versus negative" problem. This kind of approach has several weakness concerning not only these two contrasting types of data, but also by neglecting the so-called "neutral words".…”

Section: Related Workmentioning

confidence: 99%

How Objective a Neutral Word Is? A Neutrosophic Approach for the Objectivity Degrees of Neutral Words

2017

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Abstract:In the latest studies concerning the sentiment polarity of words, the authors mostly consider the positive and negative constructions, without paying too much attention to the neutral words, which can have, in fact, significant sentiment degrees. More precisely, not all the neutral words have zero positivity or negativity scores, some of them having quite important nonzero scores for these polarities. At this moment, in the literature, a word is considered neutral if its positive and negative scores are equal, which implies two possibilities: (1) zero positive and negative scores; (2) nonzero, but equal positive and negative scores. It is obvious that these cases represent two different categories of neutral words that must be treated separately by a sentiment analysis task. In this paper, we present a comprehensive study about the neutral words applied to English as is developed with the aid of SentiWordNet 3.0: the publicly available lexical resource for opinion mining. We designed our study in order to provide an accurate classification of the so-called "neutral words" described in terms of sentiment scores and using measures from neutrosophy theory. The intended scope is to fill the gap concerning the neutrality aspect by giving precise measurements for the words' objectivity.

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Emotion, Etmnooi, or Emitoon? – Faster lexical access to emotional than to neutral words during reading

Cited by 138 publications

References 66 publications

Individual differences in emotion word processing: A diffusion model analysis

Individual differences in emotion word processing: A diffusion model analysis

Emotions in reading: Dissociation of happiness and positivity

How Objective a Neutral Word Is? A Neutrosophic Approach for the Objectivity Degrees of Neutral Words

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