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. 2023 Mar 23;18(3):e0281788.
doi: 10.1371/journal.pone.0281788. eCollection 2023.

Reading fluency and statistical learning across modalities and domains: Online and offline measures

Ágnes Lukács  1   2 , Dorottya Dobó  1   2 , Ágnes Szőllősi  3   4 , Kornél Németh  1 , Krisztina Sára Lukics  1   2
Affiliations

Reading fluency and statistical learning across modalities and domains: Online and offline measures

Ágnes Lukács et al. PLoS One. .

Abstract

The vulnerability of statistical learning has been demonstrated in reading difficulties in both the visual and acoustic modalities. We examined segmentation abilities of Hungarian speaking adolescents with different levels of reading fluency in the acoustic verbal and visual nonverbal domains. We applied online target detection tasks, where the extent of learning is reflected in differences between reaction times to predictable versus unpredictable targets. Explicit judgments of well-formedness were also elicited in an offline two-alternative forced choice (2AFC) task. Learning was evident in both the acoustic verbal and visual nonverbal tasks, both in online and offline measures, but learning effects were larger both in online and offline tasks in the verbal acoustic condition. We haven't found evidence for a significant relationship between statistical learning and reading fluency in adolescents in either modality. Together with earlier findings, these results suggest that the relationship between reading and statistical learning is dependent on the domain, modality and nature of the statistical learning task, on the reading task, on the age of participants, and on the specific language. The online target detection task is a promising tool which can be adapted to a wider set of tasks to further explore the contribution of statistical learning to reading acquisition in participants from different populations.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Distribution of the OMRT z-score in the sample.
Fig 2
Fig 2. Stimuli in the nonverbal visual task.
Fig 3
Fig 3. Change of median reaction times through blocks in the verbal acoustic condition.
Boxes indicate reaction times between the first and third quartiles, horizontal lines indicate medians, whiskers illustrate the range of data, and individual dots outside the range represent outliers.
Fig 4
Fig 4. Accuracy rates across blocks in the verbal acoustic condition.
Boxes indicate accuracy data between the first and third quartiles, horizontal lines indicate medians, and whiskers illustrate the range of data, and individual dots outside the range represent outliers.
Fig 5
Fig 5. Change of median reaction times across blocks in the nonverbal visual condition.
Boxes indicate RT data between the first and third quartiles, horizontal lines indicate medians, and whiskers illustrate the range of data, and individual dots outside the range represent outliers.
Fig 6
Fig 6. Accuracy rates across blocks in the nonverbal visual condition.
Boxes indicate accuracy data between the first and third quartiles, horizontal lines indicate medians, and whiskers illustrate the range of data, and individual dots outside the range represent outliers.
Fig 7
Fig 7. Correlations between age, IQ, reading fluency, background abilities and statistical learning measures.
From the statistical learning measures, VA 2AFC was significantly related to the OMRT z-score on the level of p < .05.
See this image and copyright information in PMC

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