Gotcha: Working memory prioritization from automatic attentional biases

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 WM-physical 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.

Automated summary

This study discusses three ways in which information becomes automatically prioritized in working memory: physical salience, statistical learning, and reward learning. It integrates findings from perception and working memory studies to propose a more sophisticated understanding of the relationship between attention and working memory.