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. 2016 Nov 24:7:1836.
doi: 10.3389/fpsyg.2016.01836. eCollection 2016.

On Elementary Affective Decisions: To Like Or Not to Like, That Is the Question

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On Elementary Affective Decisions: To Like Or Not to Like, That Is the Question

Arthur Jacobs et al. Front Psychol. .

Abstract

Perhaps the most ubiquitous and basic affective decision of daily life is deciding whether we like or dislike something/somebody, or, in terms of psychological emotion theories, whether the object/subject has positive or negative valence. Indeed, people constantly make such liking decisions within a glimpse and, importantly, often without expecting any obvious benefit or knowing the exact reasons for their judgment. In this paper, we review research on such elementary affective decisions (EADs) that entail no direct overt reward with a special focus on Neurocognitive Poetics and discuss methods and models for investigating the neuronal and cognitive-affective bases of EADs to verbal materials with differing degrees of complexity. In line with evolutionary and appraisal theories of (aesthetic) emotions and data from recent neurocognitive studies, the results of a decision tree modeling approach simulating EADs to single words suggest that a main driving force behind EADs is the extent to which such high-dimensional stimuli are associated with the "basic" emotions joy/happiness and disgust.

Keywords: basic affective tone; beauty; decision tree modeling; elementary affective decisions; liking; ludic reading; neuroaesthetics; neurocognitive poetics.

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Figures

FIGURE 1
FIGURE 1
Upper panel. Partitioning results and decision tree report for 46 negative (red) and 45 positive (blue) words as implemented in Model 3. Resp, response (positive vs. negative; see text for details). Lower panel. Detailed decision tree for Model 3 with number of candidates, G2 values indicating the likelihood ratio χ2 for the best split, and LogWorth statistics [defined as – log10(p-value)]. The optimal split is the one that maximizes the LogWorth.

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References

    1. Altmann U., Bohrn I. C., Lubrich O., Menninghaus W., Jacobs A. M. (2012). The power of emotional valence-from cognitive to affective processes in reading. Front. Hum. Neurosci. 6:192 10.3389/fnhum.2012.00192 - DOI - PMC - PubMed
    1. Altmann U., Bohrn I. C., Lubrich O., Menninghaus W., Jacobs A. M. (2014). Fact vs fiction-how paratextual information shapes our reading processes. Soc. Cogn. Affect. Neurosci. 9 22–29. 10.1093/scan/nss098 - DOI - PMC - PubMed
    1. Andrews M., Vigliocco G., Vinson D. (2009). Integrating experiential and distributional data to learn semantic representations. Psychol. Rev. 116 463–498. 10.1037/a0016261 - DOI - PubMed
    1. Anz T. (1998). Literatur und Lust: Glück und Unglück beim Lesen. Munich: C.H. Beck.
    1. Aryani A., Jacobs A. M., Conrad M. (2013). Extracting salient sublexical units from written texts: “Emophon,” a corpus-based approach to phonological iconicity. Front. Psychol. 4:654 10.3389/fpsyg.2013.00654 - DOI - PMC - PubMed

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