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How Does the Brain Learn Environmental Structure? Ten Core Principles for Understanding the Neurocognitive Mechanisms of Statistical Learning T

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How Does the Brain Learn Environmental Structure? Ten Core Principles for Understanding the Neurocognitive Mechanisms of Statistical Learning T Neuroscience and Biobehavioral Reviews 112 (2020) 279–299 Contents lists available at ScienceDirect Neuroscience and Biobehavioral Reviews journal homepage: www.elsevier.com/locate/neubiorev How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning T Christopher M. Conway* Center for Childhood Deafness, Language, and Learning, Boys Town National Research Hospital, Omaha, NE, United States ARTICLE INFO ABSTRACT Keywords: Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is Statistical learning still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor Implicit learning what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a Sequential learning review of research examining the roles of input modality and domain, input structure and complexity, attention, Artificial grammar learning 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, auto- matic, 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-depen- dent 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. 1. Introduction across different perceptual modalities (e.g., vision vs. audition) or domains (e.g., language vs. music)? Does the learning of patterns in Many events in our daily existence occur not completely randomly one modality or domain involve the same neurocognitive mechan- or haphazardly, but with a certain amount of structure, regularity, and isms as learning in a different modality or domain? predictability. Because of the ubiquitous presence of structured patterns 2) Input structure and complexity: What mechanisms underpin the in human action, perception, and cognition, the ability to process and learning of different types of input patterns, such as associations represent these patterns is of paramount importance. This type of between adjacent or co-occurring elements to more complex structured pattern learning – which is likely a crucial foundational “global” patterns that require integration of information over longer ability of all higher-level organisms, and possibly of many lower-level time-scales? ones as well – has been studied under the guise of different terms for 3) Role of attention. To what extent are attention and related cognitive what arguably tap into aspects of the same underlying construct, in- processes necessary for statistical learning to occur? In turn, does cluding “implicit learning” (A.S. Reber, 1967), “sequence learning” the outcome of learning modulate attention? (Nissen and Bullemer, 1987), “sequential learning” (Conway and 4) Neural bases. What is the underlying neuroanatomy of statistical Christiansen, 2001), and “statistical learning” (Saffran et al., 1996). learning? Is there a single, common learning and processing net- Despite gains made in understanding how humans and other or- work? Or are there different sets of regions or networks that are used ganisms learn patterned input, we are still far from an understanding of for different types of learning situations? the neurocognitive mechanisms underlying learning and what factors 5) Ontogenetic constraints. How does statistical learning emerge and constrain its emergence across individuals, species, and learning si- change across the lifespan? Do different aspects of learning have tuations. What is needed is an integration of research findings across six different developmental trajectories? key areas that have generally been treated in isolation: 6) Phylogenetic constraints. Which aspects of statistical learning are shared versus unique across different animal species? What drives 1) Input modality and domain: How does learning proceed for inputs variation or differences across species? ⁎ Corresponding author at: Brain, Learning, and Language Lab, Center for Childhood Deafness, Language, and Learning, Boys Town National Research Hospital, Lied Learning and Technology Center, 555N. 30th St., Omaha, NE 68131, United States. E-mail address: [email protected]. https://doi.org/10.1016/j.neubiorev.2020.01.032 Received 8 August 2019; Received in revised form 22 January 2020; Accepted 25 January 2020 Available online 01 February 2020 0149-7634/ © 2020 Elsevier Ltd. All rights reserved. C.M. Conway Neuroscience and Biobehavioral Reviews 112 (2020) 279–299 Although a number of theoretical perspectives exist (e.g., Arciuli, Of course, even the early research on implicit learning did not occur 2017; Aslin and Newport, 2012; Daltrozzo and Conway, 2014; in a vacuum. Behaviorist approaches to associative learning and con- Forkstam and Petersson, 2005; Frost et al., 2015; Janacsek and Nemeth, ditioning provided an important historical context for the implicit 2012; Keele et al., 2003; Perruchet and Pacton, 2006; Pothos, 2007; P.J. learning work (e.g., see Pearce and Bouton, 2001; Rescorla and Wagner, Reber, 2013; A.S. Reber, 2003; Savalia et al., 2016; Seger, 1994; 1972). Gureckis and Love (2007) in fact argued that much of what the Thiessen and Erickson, 2013), currently none of them sufficiently ad- field has been studying under the guise of statistical learning is embo- dress all of these questions. In this paper, we begin by defining in more died by behaviorist principles of conditioning and associative learning detail what is meant by “statistical learning” and how it relates to other (c.f., Goddard, 2018). Additional neurophysiological antecedents of similarly-used terms. A lack of clarity and consensus in regards to ter- statistical learning include Hebb’s principles of learning and plasticity minology has proven to be a barrier for integrating findings across (i.e., the “Hebbian learning rule”; see Cooper, 2005; Hebb, 1949) and different research areas; furthermore, the use of certain terms denotes the demonstration that the development of primary visual cortex de- premature assumptions about what the underlying mechanisms are that pends on environmental experience (e.g., Blakemore and Cooper, characterize the construct of interest. Following this discussion, we 1970). While acknowledging these important precursors, this review provide a selective review and synthesis of research related to the six focuses primarily on the findings from the implicit learning and sta- areas described above. Then, based on this review, we outline ten core tistical learning literatures per se. principles that arise from an integration of the reviewed research and For simplicity the term “statistical learning” is used in the re- provide the beginnings of what could be construed as a unified theory mainder of this paper to refer to incidental learning of structured pat- of statistical learning. To preview, we propose there exist two primary terns encountered in the environment. To constrain and operationalize sets of neurocognitive mechanisms – one based on the general principle the definition of statistical learning and provide added focus to this of cortical plasticity and the other a specialized neural system that can review, we delineate the task or situational characteristics of interest. provide top-down modulation of learning – with each affected and Specifically, we propose three orthogonal dimensions that can help constrained by different factors in different ways. Only by taking into clarify the construct of statistical learning. These dimensions include: account the operation of these two mechanisms will we understand the the level of structure present in input (i.e., random versus heavily neurocognitive bases of statistical learning and how they are con- structured sequences); the amount of exposure that is involved (i.e., a strained by factors such as input modality, complexity, ontogeny, and single exposure versus multiple instances); and the extent to which task phylogeny. situations provide explicit instruction or overt feedback (i.e., incidental versus intentional learning situations). These three dimensions are de- 2. Preliminary considerations picted graphically in Fig. 1. They create a “task space” containing a continuum of distributed points in which tasks (or situations) that are Statistical learning research began in earnest with the seminal study closer to the zero-point (0,0,0) can be thought of as being more char- by Saffran et al. (1996), who showed that 8-month-old infants were acteristic of statistical learning compared to tasks at the periphery (note sensitive to the statistical structure inherent in a short (2-minute) au- though, that technically speaking there is no actual zero-point as there ditory nonword speech stream. In this study, statistical structure was could always be a situation with more exposures, or more structure, etc. operationalized as the strength of transitional probabilities between and in that case the zero-point might be more appropriately regarded as adjacent syllables (i.e., the
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