Context matters: Novel metaphors in supportive and non-supportive contexts

Hartung, Franziska; Kenett, Yoed N.; Cardillo, Eileen R.; Humphries, Stacey; Klooster, Nathaniel; Chatterjee, Anjan

doi:10.1016/j.neuroimage.2020.116645

Cited by 16 publications

(14 citation statements)

References 64 publications

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“…The actual realisation of the term "novel" in the relevant studies marks a stark contrast to the initial definitions given by the papers. In the majority, "novel" stimuli are operationalised as stimuli with low familiarity scores (Mashal et al, 2005(Mashal et al, , 2007(Mashal et al, , 2013(Mashal et al, , 2014Ahrens et al, 2007;Lai et al, 2009;Yang et al, 2009Yang et al, , 2010Cardillo et al, 2012;Lai and Curran, 2013;Zeev-Wolf et al, 2015;Hartung et al, 2020). Only four studies can be classified as having worked with conventionality in this respect (Goldstein et al, 2012;Subramaniam et al, 2012Subramaniam et al, , 2013Jończyk et al, 2020).…”

Section: Psycholinguistic Influence Factorsmentioning

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%

“…Eight studies did not specify their participants' gender; of the remaining participants, 49.3% were male and 50.7 female. One paper (Hartung et al, 2020) mentions a nonbinary person as part of the participant group. In all, there were seven studies which tested with male participants only (Bottini et al, 1994;Stringaris et al, 2006Stringaris et al, , 2007Ahrens et al, 2007;Rüschemeyer et al, 2007;Kircher et al, 2009;Straube et al, 2011) and none that tested with female participants exclusively.…”

Section: Stimuli Sources and Rating Proceduresmentioning

confidence: 99%

“…Given the great variation in composition, associated length, the examined parts of speech, and the position of figurative expressions within a stimulus, the stimuli are characterised by varying degrees of structural complexity. Contrast, for example, stimuli consisting of single metaphorical words such as "Stuhlbein" (chair leg, Forgács et al, 2012) and "begreifen" (grasp, to understand, Rüschemeyer et al, 2007) with the multisentence short stories used by Eviatar and Just (2006), Prat et al (2012), Uchiyama et al (2012), Samur et al (2015), Citron et al (2016b), andHartung et al (2020). The frequently used pattern of "A is B" hides a range in complexity, as well: Shibata et al (2007) tested with stimuli such as "Difficulty is a wall, " while Bottini et al (1994) represent the opposite end of the spectrum with sentences such as "The man who won the pools was a dog with the biggest bone.…”

Section: Structural Stimulus Designmentioning

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: Psycholinguistic Influence Factorsmentioning

confidence: 99%

Section: Structural Stimulus Designmentioning

confidence: 99%

Section: Stimuli Sources and Rating Proceduresmentioning

confidence: 99%

Section: Structural Stimulus Designmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Techniques leveraged from natural language processing (NLP) and machine learning have recently facilitated more widespread use of connected discourse (e.g., storybooks, podcasts, corpora) in studies of semantic processing (Baldassano et al, 2017;Deniz et al, 2019;Günther et al, 2019;Hartung et al, 2020;Huth et al, 2016;Jain & Huth, 2018;Kumar, 2021;Mandera et al, 2015Mandera et al, , 2017Nastase et al, 2020;Popham et al, 2021a). A key advantage of such approaches is their capacity to extract distributional statistics from vast numbers of tokens appearing in real world corpora (Baldassano et al, 2017;de Heer et al, 2017;Hartung et al, 2020;Huth et al, 2012;Jain & Huth, 2018;Naselaris et al, 2011;Popham et al, 2021b;Simony et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

What is Semantic Distance? A Review and Proposed Method for Modeling Conceptual Transitions in Natural Language

Reilly¹,

Zuckerman

Finley

et al. 2022

Preprint

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Cognitive science has seen a rapid evolution of the tools available for studying conceptual knowledge. Historically, much of our understanding of semantic memory has been informed through studies using language. Natural language processing (NLP) has offered groundbreaking techniques for elucidating relationships between concepts and language at unprecedented scales. One of the most popular applications has involved mathematically representing concepts using high dimensional feature spaces. Here we describe the nature of such semantic spaces and review ways in which human- and machine-generated semantic distance metrics differ in capturing taxonomic (e.g., dog-wolf) versus thematic (e.g., dog-leash) semantic relationships. We propose a novel method and open-source algorithm for deriving semantic distances between adjacent content words in connected language samples. This R package transforms a user-specified language transcript into a vector of pairwise semantic distances spanning all adjacent bigrams (e.g., The cat drank the milk → cat-drink, drink-milk, etc.). These distances constitute a continuous time series reflecting word-by-word level changes in meaning across a language sample of any length. We derive semantic distance norms and apply the proposed technique to a classic work of short fiction, To Build a Fire (Jack London, 1908). We discuss extensions of this time series approach, including the potential for forecasting, causal modeling, topic cohesion, and as an implicit measure of semantic impairment in spoken and/or written narratives.

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The Impact of Metaphors on Academic Text Comprehension: The Case of Students With Dyslexia

Cersosimo,

Domaneschi,

Cancer

2024

Dyslexia

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Metaphor has long been used by scholars to make concepts more comprehensible. However, this use of figurative language has never been investigated in relation to the way students with dyslexia learn from academic texts. This study aims to assess the impact of metaphors on overall academic text comprehension in university students with and without dyslexia. Excerpts from scientific articles containing metaphors were adapted to be presented in two comparable conditions: metaphorical and literal. Legibility indexes were controlled for to guarantee the same difficulty between conditions. Twenty‐eight students with dyslexia and thirty students without dyslexia underwent a multiple‐choice comprehension task for two texts, one for each condition, and provided evaluations of text perception. A standardised assessment of general reading comprehension and cognitive abilities, such as reading speed, reading accuracy, and working memory was conducted. Data revealed that, when reading ability was controlled for, students with dyslexia presented significantly lower comprehension scores only in texts containing metaphors, despite comparable perceived complexity and increased interest in metaphorical texts. These findings suggest that metaphors used in educational discourse may not be entirely inclusive, as they seem to impose additional cognitive demands on students with dyslexia.

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Context matters: Novel metaphors in supportive and non-supportive contexts

Cited by 16 publications

References 64 publications

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

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

What is Semantic Distance? A Review and Proposed Method for Modeling Conceptual Transitions in Natural Language

The Impact of Metaphors on Academic Text Comprehension: The Case of Students With Dyslexia

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