Examining the role of attentional allocation in working memory precision with pupillometry in children and adults.

Working memory (WM) precision, or the fidelity with which items can be remembered, is an important aspect of WM capacity that increases over childhood. Why individuals are more or less precise from moment to moment and why WM becomes more stable with age are not yet fully understood. Here, we examined the role of attentional allocation in visual WM precision in children aged 8 to 13 years and young adults aged 18 to 27 years, as measured by fluctuations in pupil dilation during stimulus encoding and maintenance. Using mixed models, we examined intraindividual links between change in pupil diameter and WM precision across trials and the role of developmental differences in these associations. Through probabilistic modeling of error distributions and the inclusion of a visuomotor control task, we isolated mnemonic precision from other cognitive processes. We found an age-related increase in mnemonic precision that was independent of guessing behavior, serial position effects, fatigue or loss of motivation across the experiment, and visuomotor processes. Trial-by-trial analyses showed that trials with smaller changes in pupil diameter during encoding and maintenance predicted more precise responses than trials with larger changes in pupil diameter within individuals. At encoding, this relationship was stronger for older participants. Furthermore, the pupil-performance coupling grew across the delay period-particularly or exclusively for adults. These results suggest a functional link between pupil fluctuations and WM precision that grows over development; visual details may be stored more faithfully when attention is allocated efficiently to a sequence of objects at encoding and throughout a delay period.

Explicitly predicting outcomes enhances learning of expectancy-violating information.

Predictive coding models suggest that the brain constantly makes predictions about what will happen next based on past experiences. Learning is triggered by surprising events, i.e., a prediction error. Does it benefit learning when these predictions are made deliberately, so that an individual explicitly commits to an outcome before experiencing it? Across two experiments, we tested whether generating an explicit prediction before seeing numerical facts boosts learning of expectancy-violating information relative to doing so post hoc. Across both experiments, predicting boosted memory for highly unexpected outcomes, leading to a U-shaped relation between expectedness and memory. In the post hoc condition, memory performance decreased with increased unexpectedness. Pupillary data of Experiment 2 further indicated that the pupillary surprise response to highly expectancy-violating outcomes predicted successful learning of these outcomes. Together, these findings suggest that generating an explicit prediction increases learners' stakes in the outcome, which particularly benefits learning of those outcomes that are different than expected.