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Wong R, Reichle ED, Veldre A. Prediction in reading: A review of predictability effects, their theoretical implications, and beyond. Psychon Bull Rev 2024:10.3758/s13423-024-02588-z. [PMID: 39482486 DOI: 10.3758/s13423-024-02588-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/13/2024] [Indexed: 11/03/2024]
Abstract
Historically, prediction during reading has been considered an inefficient and cognitively expensive processing mechanism given the inherently generative nature of language, which allows upcoming text to unfold in an infinite number of possible ways. This article provides an accessible and comprehensive review of the psycholinguistic research that, over the past 40 or so years, has investigated whether readers are capable of generating predictions during reading, typically via experiments on the effects of predictability (i.e., how well a word can be predicted from its prior context). Five theoretically important issues are addressed: What is the best measure of predictability? What is the functional relationship between predictability and processing difficulty? What stage(s) of processing does predictability affect? Are predictability effects ubiquitous? What processes do predictability effects actually reflect? Insights from computational models of reading about how predictability manifests itself to facilitate the reading of text are also discussed. This review concludes by arguing that effects of predictability can, to a certain extent, be taken as demonstrating evidence that prediction is an important but flexible component of real-time language comprehension, in line with broader predictive accounts of cognitive functioning. However, converging evidence, especially from concurrent eye-tracking and brain-imaging methods, is necessary to refine theories of prediction.
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Affiliation(s)
- Roslyn Wong
- School of Psychological Sciences, Macquarie University, Sydney, Australia.
| | - Erik D Reichle
- School of Psychological Sciences, Macquarie University, Sydney, Australia
| | - Aaron Veldre
- School of Psychological Sciences, Macquarie University, Sydney, Australia
- Graduate School of Health, University of Technology Sydney, Sydney, Australia
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Vin R, Blauch NM, Plaut DC, Behrmann M. Visual word processing engages a hierarchical, distributed, and bilateral cortical network. iScience 2024; 27:108809. [PMID: 38303718 PMCID: PMC10831251 DOI: 10.1016/j.isci.2024.108809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 02/03/2024] Open
Abstract
Although the Visual Word Form Area (VWFA) in left temporal cortex is considered the pre-eminent region in visual word processing, other regions are also implicated. We examined the entire text-selective circuit, using functional MRI. Ten regions of interest (ROIs) per hemisphere were defined, which, based on clustering, grouped into early vision, high-level vision, and language clusters. We analyzed the responses of the ROIs and clusters to words, inverted words, and consonant strings using univariate, multivariate, and functional connectivity measures. Bilateral modulation by stimulus condition was evident, with a stronger effect in left hemisphere regions. Last, using graph theory, we observed that the VWFA was equivalently connected with early visual and language clusters in both hemispheres, reflecting its role as a mediator in the circuit. Although the individual ROIs and clusters bilaterally were flexibly altered by the nature of the input, stability held at the level of global circuit connectivity, reflecting the complex hierarchical distributed system serving visual text perception.
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Affiliation(s)
- Raina Vin
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
| | - Nicholas M. Blauch
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - David C. Plaut
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Marlene Behrmann
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15219, USA
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