We thank Yoel Sanchez Araujo and Wendy Georgan for their contribution in the initial design of the web-based platform. We thank An Nguyen and Violet Kozloff for their work on improving the web-based statistical learning tasks, implementing the fMRI tasks, and piloting the tasks in adult participants. We thank Violet Kozloff and Parker Robbins for their contribution in assisting data collection in children. We thank Ibrahim Malik, John Christopher, Trevor Wigal, and Keith Schneider at the Center for Biological and Brain Imaging at the University of Delaware for their assistance in neuroimaging data collection. This work is funded in part by the National Institute on Deafness and other Communication Disorders (PI: Qi; NIH 1R21DC017576) and the National Science Foundation Directorate for Social, Behavioral &#38; Economic Sciences (PI: Schneider, Co-PI: Qi &#38; Golinkoff; NSF 1911462).

The authors have nothing to disclose.

The methods presented in the current protocol provide a multimodal paradigm for understanding the behavioral and neural indices of statistical learning across the course of development. The current design allows for the identification of individual differences in statistical learning ability across modalities and domains, which can be used for future investigation of the relationship between statistical learning and language development. Since an individuals&#8217; statistical learning ability is found to vary across dom...

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 success2,3. Variability in statistical learning skills in school-aged children has also been associated with vocabulary4 and reading5,6. 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 modalities6,8,9. 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) task12,13. 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 inputs14,15,16. 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 stimuli21,22. Other fMRI studies which utilized backward speech or earlier learning blocks as the control condition confirmed learning took place behaviorally19,23. 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&#8217; 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.

<table>
	<tbody>
		<tr>
			<td>4 Button Inline Response Device</td>
			<td>Cambridge Research Systems</td>
			<td>SKU: N1348</td>
			<td>An fMRI reponse pad used for measuring in-scanner response time</td>
		</tr>
		<tr>
			<td>Short/Slim Canal Tips</td>
			<td>Comply Foam</td>
			<td>SKU: 40-15028-11</td>
			<td>Short &#38; slim in-ear canal tips are recommended for children to protect hearing and allow for them to hear the stimuli while in the scanner.</td>
		</tr>
		<tr>
			<td>jsPsych</td>
			<td>jsPsych</td>
			<td>https://www.jspsych.org/</td>
			<td>jsPsych is a JavaScript library for running behavioral experiments in a web browser.</td>
		</tr>
		<tr>
			<td>Speech Synthesizer</td>
			<td>Praat</td>
			<td>Version 6.1.14</td>
			<td>This program is an artificial speech synthesizer which was used to create the syllable stimuli.</td>
		</tr>
		<tr>
			<td>Web-based statistical learning tasks</td>
			<td>Zenodo</td>
			<td>http://doi.org/10.5281/zenodo.3820620 (2020).</td>
			<td>All web-based statistical learning tasks are available for free access on Zenodo.</td>
		</tr>
	</tbody>
</table>

measuring statistical learning across modalities and domains in school-aged children via an online platform and neuroimaging techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Web-based Behavioral Results 
Given the current protocol is designed for easy dissemination with developmental populations, we have included preliminary web-based results based on data from 22 developing school-aged children (Mean (M) age = 9.3 years, Standard Deviation (SD) age = 2.04 years, range = 6.2-12.6 years, 13 girls). In the web-based statistical learning task, children performed significantly better than 0.5 chance-level on all conditions, indicating successful statistical learning at the ...

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

Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children&#8217;s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first ...

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7.3K Views.  University of Delaware. Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children’s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.