The aesthetic paradox in processing conventional and non-conventional metaphors

Christmann, Ursula; Wimmer, Lena; Groeben, Norbert

doi:10.1075/ssol.1.2.03chr

Cited by 21 publications

(2 citation statements)

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“…This also opens the field for the application of the model to other psycholinguistic research questions. In this field, often decision tasks with trial durations beyond 1.5 s are used (e.g., Christmann, Wimmer, & Groeben, 2011; Johnson, 1996; Spotorno & Noveck, 2014). The diffusion model can provide a more fine-grained analysis of the processes involved in the execution of these tasks.…”

Section: Methodsmentioning

confidence: 99%

Impact of Context Information on Metaphor Elaboration

Lerche

Christmann

Voss

2018

Experimental Psychology

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Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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Section: Methodsmentioning

confidence: 99%

Impact of Context Information on Metaphor Elaboration

Lerche

Christmann

Voss

2018

Experimental Psychology

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“…We name this intent as persuasive. In addition, inspired by a variety of arousing, humorous, or aesthetic effects of metaphors (Christmann et al, 2011), the expressive is included in our intent annotation within the enlarged definition: expressing attitude, thought, emotion, feeling, attachment, etc. Based on these factors as well as investigation of the samples in our datasets, we generalized their taxonomy and listed the categories of the author intent in metaphor as descriptive,persuasive, expressive, and others.…”

Section: Intent and Sentiment Annotationmentioning

confidence: 99%

MultiMET: A Multimodal Dataset for Metaphor Understanding

Zhang¹,

Zhang²,

Zhang³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging. As a means of cognition, metaphor is rendered by more than texts alone, and multimodal information in which vision/audio content is integrated with the text can play an important role in expressing and understanding metaphor. However, previous metaphor processing and understanding has focused on texts, partly due to the unavailability of large-scale datasets with ground truth labels of multimodal metaphor. In this paper, we introduce MultiMET, a novel multimodal metaphor dataset to facilitate understanding metaphorical information from multimodal text and image. It contains 10,437 text-image pairs from a range of sources with multimodal annotations of the occurrence of metaphors, domain relations, sentiments metaphors convey, and author intents. MultiMET opens the door to automatic metaphor understanding by investigating multimodal cues and their interplay. Moreover, we propose a range of strong baselines and show the importance of combining multimodal cues for metaphor understanding. MultiMET will be released publicly for research.

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