Volume 148, April 2026, 104746Author links open overlay panel, , Highlights•

We use a computational model to study how text shapes learning of derivational affixes.

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Our model accounts for the quasiregular nature of affix meanings and misleading forms.

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Model’s acquired affix knowledge aligns with patterns seen in human lexical processing.

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We conclude that natural text provides enough structure for learning important aspects of core affix semantics.

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Our work adds a new dimension to a psychologically grounded account of affix learning.

Abstract

Morphological knowledge serves as a powerful heuristic for vocabulary growth and contributes significantly to the speed and efficiency of reading. While research has long sought to explain how the knowledge of derivational morphology is acquired, previous approaches have struggled to capture the nuanced and complex ways in which derivational morphemes are used in written language, particularly that these morphemes contribute to meaning in a graded manner and that noise introduced by misleading forms (e.g., deliver) can impede learning. Our approach builds on earlier insights but moves beyond them by combining a large-scale analysis of vocabulary used in 1,200 popular books with computational modelling to explore how learning of derivational affixes may occur from text containing naturally occurring noise. We use a compositional distributional semantic model to investigate what can be learned about the meanings of individual English prefixes and suffixes through reading and evaluate the model’s performance against data from 120 adults in a lexical processing task. Our findings demonstrate that, despite the presence of noise, natural text contains sufficient structure to support the extraction of core affix semantics, and that readers are attuned to the complex patterns that shape affix use in the wild. This work contributes a new dimension to a more principled and psychologically grounded account of morpheme learning, and we discuss both this contribution and the broader insights it offers for language research.

Keywords

Morphology

Learning

Reading

Popular books

Lexical statistics

Computational modelling

Distributional semantics

Data availabilityAll data, code, and materials associated with this article are available on this project’s page on the Open Science Framework: https://osf.io/sf2bh/.

© 2026 The Authors. Published by Elsevier Inc.