Poly-Temporal, Multi-Layered: A Techno-Cognitive Theory of Narrative Experience in Literature.
Published In: International Journal of Humanities & Arts Computing: A Journal of Digital Humanities, 2025, v. 19, n. 1. P. 33 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Kurzynski, Maciej 3 of 3
Abstract
This article draws upon recent developments in cognitive neuroscience and natural language processing to contribute a techno-cognitive perspective into the 'deep reading' versus 'surface reading' debate in literary studies. Research at the intersection of humanities and sciences suggests that narrative experience, including both production (decoding) and reception (encoding) of stories, constitutes a sequentially and hierarchically complex process shaped simultaneously by socio-cultural contexts, sensory-emotional dynamics, and cognitive integration across multiple levels of complexity. This interdisciplinary view contrasts with traditional humanities methodologies such as area studies, which privileges identity-based accounts of literary phenomena, or Marxist genealogy, which neglects extra-political sources of meaning. The article surveys relevant research findings across multiple domains and discusses the hermeneutic implications of the techno-cognitive approach for literary studies, exemplified in a reading of Zhang Xianliang's 1985 novel Half of Man is Woman. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Humanities & Arts Computing: A Journal of Digital Humanities. 2025/03, Vol. 19, Issue 1, p33
- Document Type:Article
- Subject Area:Literature and Writing
- Publication Date:2025
- ISSN:1753-8548
- DOI:10.3366/ijhac.2025.0343
- Accession Number:183293253
- Copyright Statement:Copyright of International Journal of Humanities & Arts Computing: A Journal of Digital Humanities is the property of Edinburgh University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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