In:Experiencing Fictional Worlds:
Edited by Benedict Neurohr and Lizzie Stewart-Shaw
[Linguistic Approaches to Literature 32] 2019
► pp. 33–52
Chapter 3A predictive coding approach to Text World Theory
Published online: 21 February 2019
https://doi.org/10.1075/lal.32.03neu
https://doi.org/10.1075/lal.32.03neu
Abstract
Text World Theory offers a beneficial tool for the analysis of texts and the structures of fictional worlds. A significant and fascinating topic is the cognitive and psychological reality behind the text-world structures identified by an expert researcher of literature and if they are experienced when read by a given individual for pleasure. Using a neurological model of perceptual processing called Predictive Coding, this chapter sets out to examine how a text might be processed in real time by a reader. This process must also be sensitive to readers’ prior knowledge in keeping with Text World Theory, and whether it is the first time the text is read or not. It will be shown that while the analyses provided by Text World Theory are valid and useful for describing fictional constructions, it is possible to be even more precise when describing the formation of text-worlds in a real-time reading process.
Article outline
- 3.1Introduction
- 3.2Predictive Coding
- 3.3Predictive Coding, texts and text-worlds
- 3.4A text-world analysis
- 3.5A Predictive Coding analysis
- 3.6Conclusion
References
References (25)
Clark, A. 2013. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Science 36(3): 181–204.
Egner, T., Monti, J. M., and Summerfield, C. 2010. Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream. Journal of Neuroscience 30(49): 16601–16608.
Friston, K. 2005. A theory of cortical responses. Philosophical Transactions of the Royal Society B
Biological Sciences 360(1521): 815–836.
2002. Beyond Phrenology: What Can Neuroimaging Tell Us About Distributed Circuitry? Annual Review of Neuroscience 25(1): 221–250.
Garrod, S., Gambi, C. and Pickering, M. J. 2014. Prediction at all levels: forward model predictions can enhance comprehension. Language, Cognition and Neuroscience 29(1): 46–48.
Halliday, M. A. K. and Matthiessen, C. M. I. M. 2013. Halliday’s Introduction to Functional Grammar. 4th edn. London: Routledge.
von Helmholtz, H. 1962. Helmholtz’s Treatise on Physiological Optics, Volume 3, J. P. C. James P. C. Southall (ed). New York: Dover.
Hohwy, J., Roepstorff, A., and Friston, K. 2008. Predictive coding explains binocular rivalry: An epistemological review. Cognition 108(3): 687–701.
Jack, B. N. and Hacker, G. 2014. Predictive Coding Explains Auditory and Tactile Influences on Vision during Binocular Rivalry. Journal of Neuroscience. 34(19): 6423–6424.
Lahey, E. 2004. All the World’s a Subworld: Direct Speech and Subworld Creation in ‘After’ by Norman MacCaig. Nottingham Linguistic Circular 18: 21–28.
Park, H.-J. and Friston, K. 2013. Structural and Functional Brain Networks: From Connections to Cognition. Science 342: 1238411.
Rahnev, D., Lau, H. and de Lange, F. P. 2011. Prior Expectation Modulates the Interaction between Sensory and Prefrontal Regions in the Human Brain. Journal of Neuroscience 31(29): 10741–10748.
Rescher, N. 1999. How Many Possible Worlds Are There? Philosophy and Phenomenological Research 59: 403–420.
Sanford, A. J. and Emmott, C. 2012. Mind, Brain and Narrative. Cambridge: Cambridge University Press.
Squire, L. R. (ed). 2008. Fundamental Neuroscience. 3rd edn. Burlington, MA; London: Academic Press.
Cited by (2)
Cited by two other publications
Norledge, Jessica
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