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Related Experiment Video

Updated: May 12, 2026

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

The unexplained nature of reading.

James S Adelman¹, Suzanne J Marquis¹, Maura G Sabatos-DeVito¹

¹Department of Psychology.

Journal of Experimental Psychology. Learning, Memory, and Cognition

|April 10, 2013

Summary

This study precisely measured word reading aloud speed, finding that word properties explain only a fraction of the variation. Current reading models may need revision to account for unexplained variance in word reading times.

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Area of Science:

Cognitive Psychology
Psycholinguistics
Computational Neuroscience

Background:

Word properties significantly influence reading aloud response times (RTs), offering insights into the reading process.
Accurately predicting RTs is crucial for understanding reading, but individual differences often obscure this precision.

Purpose of the Study:

To precisely estimate the predictability of word reading aloud RTs by word properties, free from individual differences.
To evaluate the explanatory power of current cognitive models of reading.

Main Methods:

Collected precise RT data for 2,820 words read 50 times each by 4 participants.
Estimated unexplained variance at the individual level to isolate the effects of word properties.
Compared individual precision estimates with a 31-variable regression model.

Main Results:

A sophisticated regression model, outperforming existing cognitive models, still left approximately two-thirds of meaningful word-level variance unexplained.
Most systematic variance in word reading times could not be accounted for by known word properties.

Conclusions:

Contemporary accounts of reading mechanisms at the word level may be incomplete.
The substantial unexplained variance highlights limitations in current theoretical and computational models of reading.
A precise, participant-level database is now available for future model assessments.