Language statistics and individual differences in processing primary metaphors

Hutchinson, Sterling; Louwerse, Max M.

doi:10.1515/cog-2013-0023

Cited by 24 publications

(6 citation statements)

References 39 publications

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“…The findings presented here are in line with what we have claimed for first-order (Hutchinson & Louwerse, 2013 ) and higher-order (Recchia & Louwerse, 2015 ) co-occurrences. Language users translate prelinguistic conceptual knowledge into linguistic conceptualizations, so that as a function of language use, language statistical patterns are encoded in language (Louwerse, 2008 ).…”

Section: Discussionsupporting

confidence: 93%

“…The effect sizes may seem small and therefore these findings may seem negligible. However, let us assume the general linguistic context of a word can predict the valence of a word (Recchia & Louwerse, 2015 ) and so can the immediately preceding words (Hutchinson & Louwerse, 2013 ). The current study has shown that nasals in the first position of the word predict the valence only with approximately 55% accuracy (Event A).…”

Section: Discussionmentioning

confidence: 99%

“…Recent research in language statistics, however, has shown that words are not as arbitrary as they may seem. Even though word pairs, such as attic and basement , could occur in any order, a high-low order is significantly more common (Louwerse, 2008 ), as is the order of happy and sad in valence words (Hutchinson & Louwerse, 2013 ) or magnitude (Hutchinson & Louwerse, 2014 ). We have shown that language users rely on these language statistical patterns in their conceptual processing to bootstrap meaning (Louwerse, Hutchinson, Tillman & Recchia, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Estimating valence from the sound of a word: Computational, experimental, and cross-linguistic evidence

Louwerse

2016

Psychon Bull Rev

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It is assumed linguistic symbols must be grounded in perceptual information to attain meaning, because the sound of a word in a language has an arbitrary relation with its referent. This paper demonstrates that a strong arbitrariness claim should be reconsidered. In a computational study, we showed that one phonological feature (nasals in the beginning of a word) predicted negative valence in three European languages (English, Dutch, and German) and positive valence in Chinese. In three experiments, we tested whether participants used this feature in estimating the valence of a word. In Experiment 1, Chinese and Dutch participants rated the valence of written valence-neutral words, with Chinese participants rating the nasal-first neutral-valence words more positive and the Dutch participants rating nasal-first neutral-valence words more negative. In Experiment 2, Chinese (and Dutch) participants rated the valence of Dutch (and Chinese) written valence-neutral words without being able to understand the meaning of these words. The patterns replicated the valence patterns from Experiment 1. When the written words from Experiment 2 were transformed into spoken words, results in Experiment 3 again showed that participants estimated the valence of words on the basis of the sound of the word. The computational study and psycholinguistic experiments indicated that language users can bootstrap meaning from the sound of a word.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating valence from the sound of a word: Computational, experimental, and cross-linguistic evidence

Louwerse

2016

Psychon Bull Rev

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“…Second, the Conceptual Metaphor Theory account hypothesizes that LMs are an outgrowth of metaphorical thought, which is in turn an outgrowth of embodied experiences that conflate source and target domains-experience structures thought, and thought structures language (Lakoff, 1993). However, recent critics have argued for the opposite causal direction: Linguistic regularities may drive the mental mapping between source and target domains (Hutchinson and Louwerse, 2013;Casasanto, 2014;Hutchinson and Louwerse, 2014). Our results show that, at least for AN pairs, the semantic structure of a source domain and its mapping to a metaphorical target domain are available in the distributional statistics of language itself.…”

Section: Discussionmentioning

confidence: 99%

Literal and Metaphorical Senses in Compositional Distributional Semantic Models

Gutiérrez¹,

Shutova²,

Marghetis³

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Metaphorical expressions are pervasive in natural language and pose a substantial challenge for computational semantics. The inherent compositionality of metaphor makes it an important test case for compositional distributional semantic models (CDSMs). This paper is the first to investigate whether metaphorical composition warrants a distinct treatment in the CDSM framework. We propose a method to learn metaphors as linear transformations in a vector space and find that, across a variety of semantic domains, explicitly modeling metaphor improves the resulting semantic representations. We then use these representations in a metaphor identification task, achieving a high performance of 0.82 in terms of F-score.

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“…A limitation of previous work on metaphor is that, with the exception of a small number of papers, few attempts have been made to investigate the extent to which the reaction time behavior reflects distributions within language corpora. One exception to this is Hutchinson and Louwerse's (2013) study in which they compared the ways in which male and female participants responded to primary metaphors in a reaction time study. They showed that vertical associations in line with the HAPPY IS UP metaphor were more driven by corpus-based frequency differences (e.g., how often 'happy-sad' was mentioned as opposed to 'sad-happy') for women than for men.…”

Section: Introductionmentioning

confidence: 99%

Power, Gender, and Individual Differences in Spatial Metaphor: The Role of Perceptual Stereotypes and Language Statistics

Winter

Duffy

Littlemore

2020

Metaphor and Symbol

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English speakers use vertical language to talk about power, such as when speaking of people being "at the bottom of the social hierarchy" or "rising to the top".Experimental research has shown that people automatically associate higher spatial positions with more powerful social groups, such as doctors and army generals, compared to lower spatial positions, which are associated with relatively less powerful groups, such as nurses and soldiers. However, power as a social dimension is also associated with gender. Here, by means of a reaction-time study and a corpus study, we show that professions that display greater gender asymmetries, such as doctor/nurse, exhibit stronger vertical associations. Moreover, we show that people's perception of vertical metaphors for power depends on their own gender, with male participants having stronger vertical biases than female participants, which we propose is due to the fact that men are more prone to thinking about power in bodily terms, as being associated with physical dominance.Our results provide clear evidence for individual differences in metaphor comprehension, thus demonstrating empirically that the same metaphor is understood differently by different people.

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Language statistics and individual differences in processing primary metaphors

Cited by 24 publications

References 39 publications

Estimating valence from the sound of a word: Computational, experimental, and cross-linguistic evidence

Estimating valence from the sound of a word: Computational, experimental, and cross-linguistic evidence

Literal and Metaphorical Senses in Compositional Distributional Semantic Models

Power, Gender, and Individual Differences in Spatial Metaphor: The Role of Perceptual Stereotypes and Language Statistics

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