Infants' bodies, brains, and environments are ever-changing. Although this continuous transformation is a fundamental feature of development, how infants actively adapt and learn amidst such volatility is still unknown. To address this, we devised a novel learning task in which the location of a reward was systematically altered, transitioning from stable to volatile periods. Through computational modelling, we inferred from the infants' gaze and pupil data the learning processes that enabled them to navigate these changing environments. We found that infants' tonic pupil size reflected trial-by-trial changes in the level of environmental volatility. Moreover, phasic changes in pupil size when observing the reward indicated that infants relied on the information about volatility to optimize their learning. This resulted in the successful performance of the task, as indicated by the pattern of anticipatory looks to the correct reward locations. Together, these results identify the active role that infants play in adapting to change.

Dorsal anterior cingulate cortex (dACC) carries a wealth of value-related information necessary for regulating behavioral flexibility and persistence. It signals error and reward events informing decisions about switching or staying with current behavior. During decision-making, it encodes the average value of exploring alternative choices (search value), even after controlling for response selection difficulty, and during learning, it encodes the degree to which internal models of the environment and current task must be updated. dACC value signals are derived in part from the history of recent reward integrated simultaneously over multiple time scales, thereby enabling comparison of experience over the recent and extended past. Such ACC signals may instigate attentionally demanding and difficult processes such as behavioral change via interactions with prefrontal cortex. However, the signal in dACC that instigates behavioral change need not itself be a conflict or difficulty signal.

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.