Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning is an elementary skill supporting the acquisition of rule-governed combinations in language inputs¹. Successful statistical learning ability in infants predicts later language learning success^2,3. Variability in statistical learning skills in school-aged children has also been associated with vocabulary⁴ and reading^5,6. Difficulty in statistical learning has been proposed as one etiological mechanism underlying language impairment⁷. Despite the association between statistical learning and language outcomes in both neurotypical and atypical populations, the cognitive and the neural mechanisms underlying statistical learning remain poorly understood. In addition, previous literature has revealed that, within an individual, statistical learning ability is not uniform but independent across domains and modalities^6,8,9. The developmental trajectory of statistical learning abilities may further vary across domains and modalities¹⁰. These findings emphasize the importance of assessing individual differences in statistical learning across multiple tasks throughout the course of development. However, the field first requires a more systematic investigation of the relationship between statistical learning and first language development. To address these questions, we apply innovative methods including a web-based testing platform¹¹ that reaches a large number of children, and laboratory-based neuroimaging techniques (functional magnetic resonance imaging, or fMRI) that examine the real-time encoding of statistical information.

Standard measures of statistical learning begin with a familiarization phase and are followed by a two-alternative forced choice (2-AFC) task^12,13. The familiarization phase introduces a continuous stream of stimuli embedded with statistical regularities, where some stimuli are more likely to co-occur than others. The presentation of these co-occurring stimuli follows a fixed temporal order. Participants are passively exposed to the stream during the familiarization phase, followed by a 2-AFC task that tests whether the participant successfully extracted the patterns. The 2-AFC accuracy task presents two consecutive sequences: one sequence has been presented to the participant during the familiarization phase, while the other is a novel sequence, or contains part of the sequence. Above-chance accuracy on the 2-AFC would indicate successful learning at the group level. Traditional behavioral tasks assessing statistical learning generally rely upon accuracy as the outcome measure of learning. However, accuracy fails to account for the natural learning of information as it unfolds in time. A measure of real-time learning is necessary to tap into the implicit learning process of statistical learning during which children are still encoding the regularities from the inputs^14,15,16. Various adaptations across paradigms have been developed in an effort to move away from the 2-AFC measure, towards measures of on-line learning through behavioral responses during the exposure¹⁶. Studies utilizing these adaptations which measure reaction time during the exposure phase found they were related to post-learning accuracy¹⁷ with better test-retest reliability compared to that of the accuracy in adult learners¹⁸.

Neural measures are also foundational to our understanding of how learning unfolds over time, as the implicit process by which language learning occurs likely recruits different neural resources from those used once language is learned¹⁹. Neural measures also provide insights into differences in cognitive specializations underlying language ability across special populations²⁰. How the condition contrast is designed in an fMRI study is crucial for how we interpret patterns of neural activation during learning. One common practice is to compare brain responses during the familiarization phase between sequences containing regular patterns versus those containing the same stimuli which are ordered randomly. However, previous research implementing such a random control condition found no evidence for learning in behavior, despite neural differences between structured and random sequences. This might be due to the interference of random sequences on learning of structured sequences, as both were constructed from the same stimuli^21,22. Other fMRI studies which utilized backward speech or earlier learning blocks as the control condition confirmed learning took place behaviorally^19,23. However, each of these paradigms introduced its own confounding factor, such as the effect of language processing for the former case and the effect of the experimental order for the latter case. Our paradigm uses the random sequence as the control condition but mitigates their interference on participants’ learning of the structured sequences. Our fMRI paradigm also implements a mixed block/event-related design, which allows for the simultaneous modeling of transient trial-related and sustained task-related BOLD signals²⁴. Lastly, and more broadly, neural measures allow for the measurement of learning in populations where eliciting an explicit behavioral response may be difficult (e.g., developmental and special populations)²⁵.

The current protocol adopts a response time measure, in addition to traditional accuracy measures, and examines brain activation during the familiarization phase. The combination of these methods aims to provide a rich dataset for the investigation of real-time learning processes. The web-based platform offers a set of learning measures by including both response time during the exposure phase and accuracy of the 2-AFC task during the test phase. The neuroimaging protocol allows for the investigation of the underlying neural mechanisms supporting statistical learning across domains and modalities. While it is optimal to measure statistical learning within an individual using both the web-based and fMRI protocols, the tasks are designed so that they may be disseminated independently, and therefore, as two independent measures of statistical learning. The fMRI experiments included in the current protocol can help clarify how stimulus encoding, pattern extraction, and other constituent components of statistical learning are represented by particular brain regions and networks.