The role of subjective significance, valence and arousal in the explicit processing of emotion-laden words is a research paper published in PeerJ (2023). On theSindex it has a DataRank of 0.250. It has been cited 4 times, with 3 citing works in its 1-hop citation network.
Emotional categorisation (deciding whether a word is emotional or not) is a task that employs the explicit analysis of the emotional meaning of words. Therefore, it allows for assessing the role of emotional factors, i.e., valence, arousal, and subjective significance, in emotional word processing. The aim of the current experiment was to investigate the role of subjective significance, a reflective form of activation that is similar to arousal (the automatic form), in the processing of emotional meaning. We applied the orthogonal manipulation of three emotional factors. Thus, we were able to precisely differentiate the effects of each factor and search for interactions between them. We expected valence to shape the late positive complex LPC component, while subjective significance and arousal were expected to shape the P300 and N400 components. We observed the effects of subjective significance throughout the whole span of processing, while the arousal effect was present only in the LPC component. We also observed that amplitudes for N400 and LPC discriminated negative from positive valence. The results showed that all factors included in the analysis should be taken into account while explaining the processing of emotion-laden words; especially interesting is the subjective significance, which was shown to shape processing individually, as well as to come into interaction with valence and arousal.
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Base Score Contribution
0.241
From this paper's citation signal
Citation Network Contribution
9.06 × 10⁻³
From 1 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 96% comes from its base citations and 4% from the citation network (1 citing paper contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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