Attention is an important resource for prioritizing information in working memory (WM), and it can be deployed both strategically and automatically. Most research investigating the relationship between WM and attention has focused on strategic efforts to deploy attentional resources toward remembering relevant information. However, such voluntary attentional control represents a mere subset of the attentional processes that select information to be encoded and maintained in WM (Theeuwes, Journal of Cognition, 1[1]: 29, 1-15, 2018). Here, we discuss three ways in which information becomes prioritized automatically in WMphysical salience, statistical learning, and reward learning. This review integrates findings from perception and working memory studies to propose a more sophisticated understanding of the relationship between attention and working memory.

When repeatedly selected features have predictive value, an observer can learn to prioritize them. However, relatively little is known about the mechanisms underlying this persistent statistical learning.In two experiments, we investigated the boundary conditions of statistical learning. Each task included a training phase where targets appeared more frequently in one of two target colors, followed by a test phase where targets appeared equally in both colors. A posttest survey probed awareness of target color probability differences. Experiment 1 tested whether statistical learning requires the predictive feature to be inherently bound to the target. Participants searched for a horizontal or vertical line among diagonal distractors and reported its length (long or short). In the bound condition, targets and distractors were colored, whereas targets were presented in white font and surrounded by colored boxes in the unbound condition. Experiment 2 tested whether reducing task difficulty by simplifying the judgment (horizontal or vertical) would eliminate statistical learning. The results suggested that statistical learning is robust to manipulations of binding, but is attenuated when task difficulty is reduced. Finally, we found evidence that explicit awareness may contribute to statistical learning, but its effects are small and require large sample sizes for adequate detection. Public Significance StatementThis study demonstrates that statistical learning can occur under a variety of task conditions, but that manipulations of task difficulty can alter the magnitude of learning effects. This work also uses a larger sample size than most current studies of statistical learning and more appropriate statistical approaches designed to evaluate evidence, which may favor either the null or alternative hypotheses. Finally, we employed a novel approach to investigating the role of explicit awareness in implicit learning.