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Experimental Linguistics Talks | Li Kloostra: Affective Language Processing: Language-driven Evaluation of Character Affect in Morally Loaded Narratives

Experimental Linguistics Talks

Agenda

16 January 2023
11:00 - 12:00
Trans 10, 0.19

Li Kloostra: Affective Language Processing: Language-driven Evaluation of Character Affect in Morally Loaded Narratives

Affective Language Processing: Language-driven Evaluation of Character Affect in Morally Loaded Narratives

Reading negative affect adjectives, e.g.,’angry’, generally elicits increased activation of the corrugator supercilii (‘frowning muscle’), while positive adjectives, e.g., ‘happy’, can cause relaxation. This is presumably a result of mental simulation. In a moral context, the corrugator may also be affected by evaluation, e.g., how we feel about morally good or bad characters experiencing this emotion. A prior study found support for a multiple-drivers account: “simulation and evaluation both ‘drive’ the corrugator response”. Interestingly, they may counteract in immoral contexts, for example when we evaluate immoral characters being ‘angry’ as fair. The current study looked at corrugator activation while participants read short narratives, divided into segments. Each narrative included an introduction describing a character’s moral or immoral behavior, an embedded behavior-judgement task, and a phrase describing the character as ‘happy’ or ’sad’. With the task we aimed to boost fairness-based evaluation which should consequently lead to more corrugator activation at positive than negative adjectives in immoral conditions. While this is not what we found, we offer some interesting new insights into language-driven simulation and evaluation of emotion.

 

(Internship and thesis project with Drs. Marijke Beulen, Dr. Marijn Struiksma, Prof. Jos van Berkum)

Watch the talk via Teams:

https://teams.microsoft.com/l/meetup-join/19%3ameeting_NTIxNjUwNGYtMDRjOC00MmFlLTg0NTEtYTA2MDU3NGU5ZmNm%40thread.v2/0?context=%7b%22Tid%22%3a%22d72758a0-a446-4e0f-a0aa-4bf95a4a10e7%22%2c%22Oid%22%3a%2283c475af-f9dc-4c5e-80f5-e3f3e2e17adc%22%7d

Meeting ID: 399 242 382 517
Passcode: dy2YA8