Individual differences in artificial and natural language statistical learning

Isbilen, Erin S.; McCauley, Stewart M.; Christiansen, Morten H.

doi:10.1016/j.cognition.2022.105123

Cited by 23 publications

(28 citation statements)

References 43 publications

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“…First, many hypotheses surrounding statistical learning may be predicated upon conditions where syllable co‐occurrence information is perfectly uniform. This may also tie into why many studies fail to find reliable individual differences between statistical learning of artificial languages and language learning in the real world, where natural languages are infinitely more varied in terms of their statistical properties (Isbilen et al., 2022). It is thus imperative for future research to expand the diversity of the transitional probabilities that participants are trained on.…”

Section: Discussionmentioning

confidence: 99%

“…This ability can even predict individual differences in language proficiency across development (e.g., R. L. A. Frost et al., 2020; Gabay, Thiessen, & Holt, 2015; Isbilen, McCauley, & Christiansen, 2022; Mirman, Magnuson, Estes, & Dixon, 2008; see Mirman, Graf Estes, & Magnuson, 2010, for computational modeling of this effect), suggesting that these simple, laboratory‐based experiments may capture fundamental aspects of language learning in the real world.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Learning of Language: A Meta‐Analysis Into 25 Years of Research

Isbilen

Christiansen

2022

Cognitive Science

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Statistical learning is a key concept in our understanding of language acquisition. Ample work has highlighted its role in numerous linguistic functions—yet statistical learning is not a unitary construct, and its consistency across different language properties remains unclear. In a meta‐analysis of auditory‐linguistic statistical learning research spanning the last 25 years, we evaluated how learning varies across different language properties in infants, children, and adults and surveyed the methodological trends in the literature. We found robust learning across stimuli (syllables, words, etc.) in infants, and across stimuli and structures (adjacent dependencies, non‐adjacent dependencies, etc.) in adults, with larger effect sizes when multiple cues were present. However, the analysis also showed significant publication bias and revealed a tendency toward using a narrow range of simplified language properties, including in the strength of the transitional probabilities used during training. Bayes factor analyses revealed prevalent data insensitivity of moderators commonly hypothesized to impact learning, such as the amount of exposure and transitional probability strength, which contradict core theoretical assumptions in the field. Methodological factors, such as the tasks used at test, also significantly impacted effect sizes in adults and children, suggesting that choice of task may critically constrain current theories of how statistical learning operates. Collectively, our results suggest that auditory‐linguistic statistical learning has the kind of robustness needed to play a foundational role in language acquisition, but that more research is warranted to reveal its full potential.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Learning of Language: A Meta‐Analysis Into 25 Years of Research

Isbilen

Christiansen

2022

Cognitive Science

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“…While the simple correlations between sequence recall and PPVT or SRT were significant, we chose the latter measure as our individual differences variable because the recall task and the SRT both involve sequencing linguistic units in the same modality, but with different units (syllables versus morphemes). Finding that the two are systematically related would provide evidence in support of the idea that sequencing of syllables and morphemes share an underlying common and statistically sensitive mechanism (Isbilen et al, 2022). Thus, we next analyzed whether SRT performance influenced children's performance on the experiment.…”

Section: Relationship Between Language Proficiency and Slmentioning

confidence: 83%

The effect of children’s prior knowledge and language abilities on their statistical learning

Stärk

Kidd

Frost

2022

Applied Psycholinguistics

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Statistical learning (SL) is assumed to lead to long-term memory representations. However, the way that those representations influence future learning remains largely unknown. We studied how children’s existing distributional linguistic knowledge influences their subsequent SL on a serial recall task, in which 49 German-speaking seven- to nine-year-old children repeated a series of six-syllable sequences. These contained either (i) bisyllabic words based on frequently occurring German syllable transitions (naturalistic sequences), (ii) bisyllabic words created from unattested syllable transitions (non-naturalistic sequences), or (iii) random syllable combinations (unstructured foils). Children demonstrated learning from naturalistic sequences from the beginning of the experiment, indicating that their implicit memory traces derived from their input language informed learning from the very early stages onward. Exploratory analyses indicated that children with a higher language proficiency were more accurate in repeating the sequences and improved most throughout the study compared to children with lower proficiency.

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“…Second, implementing a task that requires processing statistical regularities in natural language (Isbilen, McCauley, & Christiansen, 2022) will offer a better opportunity for us to discover language-SL functional similarities in the brain. Similarly, altering the language localizer task utilized may uncover additional functional similarities between language-SL, as the language parcels identified in the current study have been shown to be biased towards semantic, as opposed to syntactic, processing (Fedorenko et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Limited but specific engagement of the mature language network during linguistic statistical learning

Schneider¹,

Scott²,

Legault³

et al. 2022

Preprint

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Statistical learning (SL) refers to the ability to detect and learn regularities from input and is foundational to language acquisition. Recent behavioral research has demonstrated a reciprocal relationship between prior language experiences and performance on linguistic measures of SL. Despite the dominant role of SL as a theoretical construct for language development, there has been a lack of direct evidence supporting shared neural substrates underlying language and SL. The current study tests whether the brain regions involved in language processing are similarly recruited during a speech segmentation SL task by probing the brain activation patterns of auditory SL within each learner’s language network. Twenty-two adults performed an auditory SL task and a language localizer task as their brain activities were monitored with functional magnetic resonance imaging. Within the subject-specific language network, the bilateral superior temporal gyri (STG) and middle temporal gyri (MTG) showed greater hemodynamic responses while participants were processing structured versus random syllable sequences. However, due to substantial spatial heterogeneity of brain regions engaged during SL, there was no convergence across participants in the conjunction of SL and language tasks. We further ask whether neural representation of the SL and language tasks is similar within language regions and outside of the core language network by using a multi-voxel pattern similarity analysis approach. We found no evidence of task similarity either within the language regions or outside of the classic language network. Our findings suggest that while parts of the language network are recruited during auditory SL, the specific subregions vary substantially across individuals. Despite the close relationship between language processing and SL, they do not share a similar neurocomputational basis within the mature language network.

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Individual differences in artificial and natural language statistical learning

Cited by 23 publications

References 43 publications

Statistical Learning of Language: A Meta‐Analysis Into 25 Years of Research

Statistical Learning of Language: A Meta‐Analysis Into 25 Years of Research

The effect of children’s prior knowledge and language abilities on their statistical learning

Limited but specific engagement of the mature language network during linguistic statistical learning

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