Evidence map›Paper›PMID 39482486›Full record

ReviewPsychonomic bulletin & review2025

Prediction in reading: A review of predictability effects, their theoretical implications, and beyond.

Roslyn Wong, Erik D Reichle, Aaron Veldre

Abstract readReview
In one paragraph

Review in Psychonomic bulletin & review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Roslyn WongSchool of Psychological Sciences, Macquarie University, Sydney, Australia. roslyn.wong@mq.edu.au.ORCID http://orcid.org/0000-0002-7250-4844
Erik D ReichleSchool of Psychological Sciences, Macquarie University, Sydney, Australia.
Aaron VeldreSchool of Psychological Sciences, Macquarie University, Sydney, Australia.

Funding

Australian Research Council DP18102705Australian Research Council DP190100719
6 · The paper itself

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.

Indexed as

ComprehensionPsycholinguisticsReadingHumansLanguageBrain imagingEye movementsPredictability effectsPredictionReadingReading models

Identifiers

PMID39482486
PMCPMC12092549

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.