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.

Highlights

  • One of the most striking features of the human mind is its ability to rapidly learn about regularities that are present in the environment

  • We observed a main effect of Seen (p < .001), which was further qualified by a significant interaction with Type (p < .001), indicating that the odds of making a “word” response were overall higher for word strings than foils, this effect was greater in strings that participants had already seen compared to previously unseen strings

  • This tendency was unlikely to have been acquired during the task, as we found no evidence that the effect of the minimal bigram frequency (minBF) statistics increased over time

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Summary

METHODS

The stimuli and experimental scripts used in the main task, as well as raw data and commented analysis scripts can be found at the project’s Open Science Framework page: https://osf.io/vc6rw/. Apart from investigating our main hypothesis, we wanted to explore whether the effects of statistical cues on lexical judgment would increase over time, indicating that participants were learning to rely on such cues as they progressed through the task (prior to this analysis we excluded 1 further participant who did not produce responses in the last two blocks of the experiment; leaving n = 42) To this end, we re-fit the ABF + minBF model including experimental block as a predictor (scaled): “word” response ~ (ABF * Type * Seen * Block) + (minBF * Type * Seen * Block) + (ABF * Type * Seen * Block | Participant) + (minBF * Type * Seen * Block | Participant) + (Seen | Item). The models followed the structure: “word” response ~ (ABF * Type * Seen * MHVS/VSL) + (minBF * Type * Seen * MHVS/VSL) + (ABF * Type * Seen * Block | Participant) + (minBF * Type * Seen * Block | Participant) + (Seen | Item)

RESULTS
DISCUSSION
Participants
GENERAL DISCUSSION
DATA ACCESSIBILITY STATEMENTS
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