Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

Abstract

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.

Individual Differences in Language Acquisition and Processing

“…Overall, the research to date suggests that SL tasks are capturing cognitive skills important for language, but it is still unclear exactly what they capture [133]. Fortunately, new methods are emerging that may support further advancement in this area [137,138]. Therefore, future research will be better able to estimate the relative contribution that SL makes to language.…”

Statistical learning abilities and their relation to language

“…For starters, the vast majority of previous SL studies assumes that learners come to the learning task tabula rasa. Real-world learning, however, constantly involves the updating of existing knowledge (see, e.g., Karuza et al, 2016;Kóbor, Horváth, Kardos, Nemeth, & Janacsek, 2019;Siegelman, Bogaerts, Elazar, et al, 2018;Siegelman, Bogaerts, Kronenfeld, & Frost, 2018;Weiss, Gerfen, & Mitchel, 2009). Thus, mapping individual differences in SL cannot focus only on how individuals differ in their ability to learn a novel set of regularities from scratch, but should also consider variance in individuals' capacity to learn novel information given existing assimilated regularities.…”

“…In general, more research is needed to fully understand how both task-related factors (e.g., number of trials, input modality, type of embedded regularities, and measurement domain) and sample-related properties (in particular-participants' age) influence task reliability, and how to maximize the reliability of a given task for a given population. To this aim, future studies can combine data from more than one form of measurement, for example by using composite offlineonline behavioral metrics (Siegelman, Bogaerts, Kronenfeld, et al, 2018), or by combining behavioral measures with neural indices from electroencephalography recordings (e.g., Batterink & Paller, 2017;Vasuki, Sharma, Ibrahim, & Arciuli, 2017). In addition, note that when estimating the strength of the association between a SL task and a linguistic outcome, one should also consider the reliability of the linguistic task, as it similarly constrains the magnitude of the observed correlation between the two.…”

Initial and Later Procedural Learning Rates of Implicit Sequences in Good and Poor Readers of English

“…However, reliability of SRT tasks have been criticized (Siegelman, Bogaerts, Kronenfeld, & Frost, 2017;West, Vadillo, Shanks, & Hulme, 2018). Our task, therefore, runs the risk of being unsuitable to be used in correlational approaches (Hedge, Powell, & Sumner, 2017).…”