How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning

Abstract

Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

Keywords: Artificial grammar learning; Implicit learning; Sequential learning; Statistical learning.

Fig. 1.

Three orthogonal dimensions outlining a proposed task space for statistical learning.

Fig. 2.

Three candidate architectures for how input modality and domain interacts with statistical learning. (Top left): A domain-general (DG) account posits a single, unitary mechanism that implements statistical learning for all input modalities and domains. Here, the frontal lobe of the brain (prefrontal cortex) is offered as one potential domain-general brain region, though others areas are also likely candidates. (Top right): A modality/domain-specific account posits multiple, relatively independent mechanisms, each handling a specific type of input such as auditory (A), visual (V), tactile (T), and motor (M) patterns. For simplicity, only these four modality/domain-specific regions are illustrated though others would be posited to exist as well. (Bottom): Finally, perhaps the most viable account is one which combines domain-general and domain-specific architectures, here shown with connections between modality-specific brain regions and the prefrontal cortex.

Fig. 3.

Depiction of two distinct learning mechanisms, “implicit” and “explicit”, in relation to the factors of input modality, input complexity, and attention.

Fig. 4.

A multicomponent model of statistical learning. The first set of (“implicit”) mechanisms is based on the principle of cortical plasticity, which allows networks to adaptively change through experience. Posterior modality-specific networks allow for improved perceptual processing of input patterns spanning short timescales (A: auditory; V: visual; T: tactile; M: motor). More anterior networks can handle patterns spanning across perceptual modalities and over larger timescales. The second “executive” (or “explicit”) mechanism is rooted in frontal lobe (prefrontal cortex, PFC) and frontoparietal networks that mediate top-down control of attention and working memory to modulate learning and allow for the learning of more complex patterns such as nonadjacent dependencies. Not only does the executive system modulate plasticity-based learning; but through the principle of plasticity, as learning occurs, attention itself can be affected, drawing resources toward certain environmental events or stimuli, thus affecting the operation of the executive system.