Artificial Grammar Learning (AGL) paradigms are a powerful method to study language learning and processing. How-ever, unlike natural languages, these tasks rely on grammars specifying relationships between meaningless stimuli with noreal-world referents. Therefore, learning is typically assessed based on grammaticality or familiarity judgements, assess-ing how well-formed a sequence is. We combined a meaningful vocabulary (in which nonsense words refer to propertiesof visual stimuli (colored shapes)) with different grammatical structures (adjacent, center-embedded, or crossed dependen-cies). Using an incremental, starting-small paradigm, participants were asked to interpret increasingly complex sequencesof nonsense words and select the set of visual stimuli that they described. High levels of learning were observed for allgrammars, including those which have previously been difficult to learn in traditional AGL paradigms. Here, the addi-tion of semantics not only allows closer comparisons to natural language but also aids learning, representing a valuableapproach to studying language learning.

Implicit statistical learning, whereby regularities between stimuli are detected without conscious awareness, is importantfor language acquisition. This form of learning has often been assessed using measures that require conscious decisionmaking or explicit reflection (e.g., 2AFC tasks). We aimed to measure statistical learning more implicitly. We leveraged thefact that frequently co-occurring stimuli may be chunked into a single cognitive unit, reducing working memory demands.We developed an artificial grammar in which sequences contained pairs of stimuli which always co-occurred (chunks)and more variable between-chunk transitions. In a novel visual recall paradigm, participants were asked to rememberand recreate sequences of serially presented images. Recall of predictable sequences improved over the course of theexperiment. However, recall dropped to initial levels when participants were presented with random sequences containingno predictable chunks. This approach represents a valuable method to measure statistical learning implicitly, withoutrequiring conscious reflection.

Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods.

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.