The Neuroscience of Implicit Learning

Abstract

Over the past decades, research employing artificial grammar, sequence learning, and statistical learning paradigms has flourished, not least because these methods appear to offer a window, albeit with a restricted view, on implicit learning processes underlying natural language learning. But these paradigms usually provide relatively little exposure, use meaningless stimuli, and do not even necessarily target natural language structures. So the question arises whether they engage the same brain regions as natural language. The aim of this review is to use data from brain imaging, brain stimulation, and the effects of brain damage to identify the main brain regions that show sensitivity to structural regularities in implicit learning paradigms and to consider their relationship to natural language processing and learning. This article is not a meta-analysis of studies in the field, nor will it advance a particular theoretical perspective, or systematically lay out an agenda for future research (though indications of outstanding questions and possible avenues for research emerge along the way). Rather it is an attempt to lay out the state of the art in the field for the nonspecialist, hoping to highlight, if nothing else, the complexities and inconsistencies in the evidence base, how these might be related to methodological variations, and to emphasize the problems inherent in making generalizations about localization of brain function on the basis of the evidence reviewed. With a view to the latter, the following section briefly lays out some of the ongoing debates over the function of core brain regions in relation to language processing and nonimplicit learning of natural languagelike systems.

Distributional Learning of Speech Sounds: An Exploratory Study Into the Effects of Prior Language Experience

“…In contrast, without the availability of any underlying categorical structures, distributional training might have rather unexpected effects: Exposure to probability distributions could result in entrainment to the most frequently presented physical properties of stimuli. Such effects of prior abstract knowledge on adults’ ability to learn implicitly from input have been reported for linguistic levels other than phonology, such as word order or mapping between a grammatical category and meaning (Leung & Williams, 2014; Onnis & Thiessen, 2013; Williams, 2020).…”

The Contributions of Implicit-Statistical Learning Aptitude to Implicit Second-Language Knowledge

“…Because TOL measures the effects of practice, it can be regarded as a measure of skill acquisition (Ouellet et al, 2004) and is assumed to reflect procedural learning (Ouellet et al, 2004) and provide a measure of individual differences in procedural memory ability (e.g., Antoniou et al, 2016;Buffington & Morgan-Short, 2018;Buffington et al, 2021;Ettlinger et al, 2014;Morgan-Short et al, 2014). The contributions of procedural memory to implicit-statistical learning are complex (Batterink et al, 2019;Williams, 2020). Batterink et al (2019) reported that "a common theme that emerges across implicit learning and statistical learning paradigms is that there is frequently interaction or competition between the declarative and nondeclarative [e.g., procedural] memory systems of the brain….…”

“…2 Although knowledge represented in procedural memory is implicit (inaccessible to awareness), both declarative and procedural memory underlie implicit knowledge, suggesting procedural memory and implicit knowledge are related but not isomorphic (Batterink et al, 2019;Ullman, 2020;Williams, 2020). 3 The full covariance matrix with error covariances for each figure is available from the authors upon request.…”

Cognitive Predictors of Child Second Language Comprehension and Syntactic Learning

“…The first strand has considered declarative and procedural learning ability as behavioral correlates of declarative and procedural long‐term memory (for a meta‐analysis of these associations, see Hamrick, Lum, & Ullman, 2018). The second strand has focused on differences in implicit statistical learning, broadly characterized as the largely implicit 1 ability to track and learn co‐occurrence patterns from repeated exposure to sensory input in different modalities (Perruchet & Pachton, 2006; for reviews, see Kidd, Donnelly, & Christiansen, 2018, and Williams, 2020). The present study mainly considered the relationship between child second language (L2) learning and cognitive individual differences from the perspective of the declarative/procedural model of learning and memory, but relevant child studies within the implicit statistical learning framework were also considered in the literature review and in the discussion.…”