Electrophysiological correlates of conventional metaphor, irony, and literal language processing – An event-related potentials and eLORETA study

Deckert, Matthias; Schmoeger, Michaela; Geist, Max; Wertgen, Sarah; Willinger, Ulrike

doi:10.1016/j.bandl.2021.104930

Cited by 6 publications

(4 citation statements)

References 91 publications

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“…98 papers report studies on the first, and 18 are concerned with the latter. Eleven papers contrast metaphors with another figurative subtype in their paradigms, i.e., the stimuli included figurative subtypes besides metaphor: idiom (Desai et al, 2013;Romero Lauro et al, 2013;Lorusso et al, 2015), irony (Eviatar and Just, 2006;Prat et al, 2012;Deckert et al, 2021), metonymy (Weiland et al, 2014;Yurchenko et al, 2020), sarcasm (Uchiyama et al, 2012), and simile (Shibata et al, 2012;Lai and Curran, 2013).…”

Section: Definitions Of Figurative Languagementioning

confidence: 99%

“…Five studies used sentence pairs (Bambini et al, 2011(Bambini et al, , 2016Lai and Curran, 2013;Romero Lauro et al, 2013). A final eight studies (Eviatar and Just, 2006;Prat et al, 2012;Samur et al, 2015;Citron et al, 2016b;Hartung et al, 2020;Adamczyk et al, 2021;Deckert et al, 2021) worked with entire short stories. Consequently, 28 (24%) studies tested with isolated expressions while the remaining 76% presented context in varying degrees.…”

Section: Structural Stimulus Designmentioning

confidence: 99%

“…The stimuli are usually rated using scales of varying size: scales may present a binary choice (e.g., Kircher et al, 2007;Rüschemeyer et al, 2007;Lai et al, 2009;Bambini et al, 2016;Canal et al, 2017), more common are scales ranging from three to seven points. Few studies (n = 4) explicitly use expert ratings (Rüschemeyer et al, 2007;Lai et al, 2009;Forgács et al, 2012;Deckert et al, 2021).…”

Section: Stimuli Sources and Rating Proceduresmentioning

confidence: 99%

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The Elephant in the Room: A Systematic Review of Stimulus Control in Neuro-Measurement Studies on Figurative Language Processing

Koller

Müller

Kauschke

2022

Front. Hum. Neurosci.

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The processing of metaphors and idioms has been the subject of neuroscientific research for several decades. However, results are often contradictory, which can be traced back to inconsistent terminology and stimulus control. In this systematic review of research methods, we analyse linguistic aspects of 116 research papers which used EEG, fMRI, PET, MEG, or NIRS to investigate the neural processing of the two figurative subtypes metaphor and idiom. We critically examine the theoretical foundations as well as stimulus control by performing a systematic literature synthesis according to the PRISMA guidelines. We explicitly do not analyse the findings of the studies but instead focus on four primary aspects: definitions of figurative language and its subtypes, linguistic theory behind the studies, control for factors influencing figurative language processing, and the relationship between theoretical and operational definitions. We found both a lack and a broad variety in existing definitions and operationalisation, especially in regard to familiarity and conventionality. We identify severe obstacles in the comparability and validation potential of the results of the papers in our review corpus. We propose the development of a consensus in fundamental terminology and more transparency in the reporting of stimulus design in the research on figurative language processing.

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Section: Definitions Of Figurative Languagementioning

confidence: 99%

Section: Structural Stimulus Designmentioning

confidence: 99%

Section: Stimuli Sources and Rating Proceduresmentioning

confidence: 99%

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The Elephant in the Room: A Systematic Review of Stimulus Control in Neuro-Measurement Studies on Figurative Language Processing

Koller

Müller

Kauschke

2022

Front. Hum. Neurosci.

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“…Irony has indeed been demonstrated to be ubiquitous in social media, offering major challenge to sentiment analysis field [1]. It is a cognitive phenomenon in which affect-related features play a significant role.…”

Section: Introductionmentioning

confidence: 99%

Fine grained irony classification through transfer learning approach

Shirahatti

Rajpurohit²,

Sannakki³

2023

Comput. Sci. Inf. Technol.

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Nowadays irony appears to be pervasive in all social media discussion forums and chats, offering further obstacles to sentiment analysis efforts. The aim of the present research work is to detect irony and its types in English tweets We employed a new system for irony detection in English tweets, and we propose a distilled bidirectional encoder representations from transformers (DistilBERT)light transformer model based on the bidirectional encoder representations from transformers (BERT) architecture, this is further strengthened by the use and design of bidirectional long-short term memory (Bi-LSTM) network this configuration minimizes data preprocessing tasks proposed model tests on a SemEval-2018 task 3, 3,834 samples were provided. Experiment results show the proposed system has achieved a precision of 81% for not irony class and 66% for irony class, recall of 77% for not irony and 72% for irony, and F1 score of 79% for not irony and 69% for irony class since researchers have come up with a binary classification model, in this study we have extended our work for multiclass classification of irony. It is significant and will serve as a foundation for future research on different types of irony in tweets.

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