“The Brain Is the Prisoner of Thought” : A Machine-Learning Assisted Quantitative Narrative Analysis of Literary Metaphors for Use in Neurocognitive Poetics.
Two main goals of the emerging field ofneurocognitive poeticsare (a) the use of more natural and ecologically valid stimuli, tasks and contexts and (b) providing methods and models allowing to quantify distinctive features of verbal materials used in such tasks and contexts and their effects on read...
| Published in: | Metaphor & Symbol Vol. 32; no. 3; pp. 139 - 161 |
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| Main Authors: | , |
| Format: | Article |
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Taylor & Francis Ltd
Jul-Sep2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=124435041&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 124435041 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10926488 7MO jtl: Metaphor & Symbol issn: 10926488 maglogo: N pubinfo: dt: Jul-Sep2017 vid: 32 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 124435041 10.1080/10926488.2017.1338015 ppf: 139 ppct: 22 formats: fmt: – @attributes: type: T db: hlh ui: 124435041 – @attributes: type: P db: hlh ui: 124435041 tig: atl: “The Brain Is the Prisoner of Thought” : A Machine-Learning Assisted Quantitative Narrative Analysis of Literary Metaphors for Use in Neurocognitive Poetics. aug: au: Jacobs, Arthur M. Kinder, Annette affil: Freie Universität Berlin su: Shakespeare, William, 1564-1616 Thomas, Dylan, 1914-1953 Metaphor Poets Narrative inquiry (Research method) Machine learning Verbal learning sug: subj: Metaphor Independent Artists, Writers, and Performers Independent writers and authors Poets Narrative inquiry (Research method) Machine learning Verbal learning Shakespeare, William, 1564-1616 Thomas, Dylan, 1914-1953 ab: Two main goals of the emerging field ofneurocognitive poeticsare (a) the use of more natural and ecologically valid stimuli, tasks and contexts and (b) providing methods and models allowing to quantify distinctive features of verbal materials used in such tasks and contexts and their effects on readers responses. A natural key element of poetic language, metaphor, still is understudied insofar as relatively little empirical research looked at literary or poetic metaphors. An exception is Katz et al.’s corpus of 204 literary metaphors by authors such as Shakespeare or Dylan Thomas, for which various rating data are available. We reanalyzed their corpus using a combination of quantitative narrative analysis, latent semantic analysis, and machine learning in order to identify relevant features of the metaphors that influenced the ratings. The combined application of computational tools sheds light on surface and affective-semantic features that co-determine the reception of poetic metaphors and successfully predicted the period of origin (i.e., early vs. late), authorship (e.g., Byron vs. Donne) and goodness ratings of the metaphors. The present results can be used for generating quantitative hypotheses or selecting and matching verbal stimuli in empirical studies of literature and neurocognitive poetics. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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