I am a Ph.D. candidate working with Ed Vogel and Ed Awh at the University of Chicago. My work focuses on the intersection of working memory limits, fluctuations in attention, and individual differences. Below, I have described some of my recent projects.
The link between fluctuations of attention and working memory limits
Our attention fluctuates from moment to moment, and we all have experiences of these fluctuations in our everyday lives. You might be completely focused one minute but staring out the window with a blank mind the next. I am interested in how fluctuations of attention impact working memory performance. In our work, we have found that participants frequently perform at chance for a typical working memory task. (That is, when asked to store many items they store only 0 or 1 more than 10% of the time.) We have used modeling and EEG approaches to pin-point the neural mechanisms that underlie these failures (Adam et al., 2015). Our data support a model of working memory in which most individuals share a common capacity limit (e.g. 3 simple features) but differ in how consistently they achieve this maximum capacity (Adam et al., 2015; Adam, Vogel, & Awh, 2017).
Improving working memory performance by reducing failures
Given the finding that people frequently have working memory failures, we sought ways to reduce their frequency. Are participants even aware that these failures are happening? Surprisingly, we found that people have very poor meta-awareness of their working memory failures (Adam & Vogel, 2017), and that individuals with low working memory capacities are particularly bad at monitoring their performance. Luckily, we also found that giving participants visual feedback can dramatically reduce the frequency of working memory failures (Adam & Vogel, 2016).
The reliability and stability of working memory performance
Researchers commonly use correlational approaches to investigate the relationship between different cognitive processes. For example, we might test the correlation strength between a measure of working memory capacity (e.g. change detection) and a measure of fluid intelligence (e.g. Raven’s matrices) in order to learn if these two abilities covary between individuals. Importantly, internal reliability critically limits the usefulness of correlational approaches. In recent work (Xu, Adam, et al., 2016) we collected more than 300,000 trials of change detection in order to better characterize its reliability and stability over time. Using our large sample, we used an iterative downsampling procedure to create a ‘look-up table’ for change detection reliability as a function of the number of subjects and trials used. This table can serve as a guide for future individual differences studies that include a short change detection measure.