Behavioral and Imaging Studies of Infant Artificial Grammar Learning

Brain responses to repetition-based rule-learning do not exhibit sex differences: an aggregated analysis of infant fNIRS studies

Studies have repeatedly shown sex differences in some areas of language development, typically with an advantage for female over male children. However, the tested samples are typically small and the effects do not always replicate. Here, we used a meta-analytic approach to address this issue in a larger sample, combining seven fNIRS studies on the neural correlates of repetition- and non-repetition-based rule learning in newborns and 6-month-old infants. The ability to extract structural regularities from the speech input is fundamental for language development, it is therefore highly relevant to understand whether this ability shows sex differences. The meta-analysis tested the effect of Sex, as well as of other moderators on infants' hemodynamic responses to repetition-based (e.g. ABB: "mubaba") and non-repetition-based (e.g. ABC: "mubage") sequences in both anatomically and functionally defined regions of interests. Our analyses did not reveal any sex differences at birth or at 6 months, suggesting that the ability to encode these regularities is robust across sexes. Interestingly, the meta-analysis revealed other moderator effects. Thus in newborns, we found a greater involvement of the bilateral temporal areas compared to the frontal areas for both repetition and non-repetition sequences. Further, non-repetition sequences elicited greater responses in 6-month-olds than in newborns, especially in the bilateral frontal areas. When analyzing functional clusters of HbR timetraces, we found that a larger right-left asymmetry for newborn boys in brain responses compared to girls, which may be interpreted in terms of a larger right-left asymmetry in cerebral blood flow in boys than in girls early in life. We conclude that extracting repetition-based regularities from speech is a robust ability with a well-defined neural substrate present from birth and it does not exhibit sex differences.

Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross-Species, and Computational Approaches

Human languages all have a grammar, that is, rules that determine how symbols in a language can be combined to create complex meaningful expressions. Despite decades of research, the evolutionary, developmental, cognitive, and computational bases of grammatical abilities are still not fully understood. "Artificial Grammar Learning" (AGL) studies provide important insights into how rules and structured sequences are learned, the relevance of these processes to language in humans, and whether the cognitive systems involved are shared with other animals. AGL tasks can be used to study how human adults, infants, animals, or machines learn artificial grammars of various sorts, consisting of rules defined typically over syllables, sounds, or visual items. In this introduction, we distill some lessons from the nine other papers in this special issue, which review the advances made from this growing body of literature. We provide a critical synthesis, identify the questions that remain open, and recognize the challenges that lie ahead. A key observation across the disciplines is that the limits of human, animal, and machine capabilities have yet to be found. Thus, this interdisciplinary area of research firmly rooted in the cognitive sciences has unearthed exciting new questions and venues for research, along the way fostering impactful collaborations between traditionally disconnected disciplines that are breaking scientific ground.

Structured Sequence Learning: Animal Abilities, Cognitive Operations, and Language Evolution

Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in species ranging from mammals (primates and rats) to birds (pigeons, songbirds, and parrots) considering also cross-species comparisons. The findings are contrasted with seminal studies in human infants that motivated the work in nonhuman animals. This synopsis identifies advances in knowledge and where uncertainty remains regarding the various strategies that nonhuman animals can adopt for processing sequencing dependencies. The paucity of evidence in the few species studied to date and the need for follow-up experiments indicate that we do not yet understand the limits of animal sequence processing capacities and thereby the evolutionary pattern. This vibrant, yet still budding, field of research carries substantial promise for advancing knowledge on animal abilities, cognitive substrates, and language evolution.

Capacities and neural mechanisms for auditory statistical learning across species

Statistical learning has been proposed as a possible mechanism by which individuals can become sensitive to the structures of language fundamental for speech perception. Since its description in human infants, statistical learning has been described in human adults and several non-human species as a general process by which animals learn about stimulus-relevant statistics. The neurobiology of statistical learning is beginning to be understood, but many questions remain about the underlying mechanisms. Why is the developing brain particularly sensitive to stimulus and environmental statistics, and what neural processes are engaged in the adult brain to enable learning from statistical regularities in the absence of external reward or instruction? This review will survey the statistical learning abilities of humans and non-human animals with a particular focus on communicative vocalizations. We discuss the neurobiological basis of statistical learning, and specifically what can be learned by exploring this process in both humans and laboratory animals. Finally, we describe advantages of studying vocal communication in rodents as a means to further our understanding of the cortical plasticity mechanisms engaged during statistical learning. We examine the use of rodents in the context of pup retrieval, which is an auditory-based and experience-dependent form of maternal behavior.