Knowledge of Statistics or Statistical Learning? Readers Prioritize the Statistics of their Native Language Over the Learning of Local Regularities

Jarosław R Lelonkiewicz; Michael T Ullman; Davide Crepaldi

doi:10.5334/joc.209

Knowledge of Statistics or Statistical Learning? Readers Prioritize the Statistics of their Native Language Over the Learning of Local Regularities

J Cogn. 2022 Feb 21;5(1):18. doi: 10.5334/joc.209. eCollection 2022.

Authors

Jarosław R Lelonkiewicz¹, Michael T Ullman², Davide Crepaldi¹

Affiliations

¹ Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy.
² Georgetown University, Washington D.C., USA.

PMID: 36072100
PMCID: PMC9400655
DOI: 10.5334/joc.209

Abstract

A large body of evidence suggests that people spontaneously and implicitly learn about regularities present in the visual input. Although theorized as critical for reading, this ability has been demonstrated mostly with pseudo-fonts or highly atypical artificial words. We tested whether local statistical regularities are extracted from materials that more closely resemble one's native language. In two experiments, Italian speakers saw a set of letter strings modelled on the Italian lexicon and guessed which of these strings were words in a fictitious language and which were foils. Unknown to participants, words could be distinguished from foils based on their average bigram frequency. Surprisingly, in both experiments, we found no evidence that participants relied on this regularity. Instead, lexical decisions were guided by minimal bigram frequency, a cue rooted in participants' native language. We discuss the implications of these findings for accounts of statistical learning and visual word processing.

Keywords: reading; statistical learning; visual word processing.

Grants and funding

This work received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme Grant Agreement No 679010 STATLEARN ERC-2015-STG, awarded to Davide Crepaldi. We would like to thank Simona Amenta for sharing the Italian adaptation of the Mill Hill Vocabulary Scale with us, and Noam Siegelman for sharing the Visual Statistical Learning task. We also thank Anna D’Urso, Alessia Zampieri, and Chiara Zanin for help with data collection, and Ivana Bačanek for assistance with Figure 2.