I am a postdoctoral research fellow at the University of California San Diego working with John Serences. I earned my PhD working with Ed Vogel and Ed Awh at the University of Chicago (June 2018). My work uses neural methods (EEG, fMRI) in conjunction with behavior to characterize fundamental constraints on attention and working memory. July 2023, I will be starting as an Assistant Professor in the Department of Psychological Sciences at Rice University in the Cognitive & Affective Neuroscience Research Interest Group.
How we learn to ignore irrelevant information
Because of a limited processing capacity, we can only actively pay attention to a subset of all available information at any given time. Usually, we can use our goals to focus on finding only the relevant information in a visual scene. However, despite our best intentions, we can sometimes get pulled off track when salient but irrelevant information is present. My postdoctoral work has been focused on how we can learn to ignore initially salient information with experience. Using fMRI in conjunction with a visual search task, we have demonstrated how stimulus history influences early visual processing of distractors, but not targets. We found that repeated stimuli did not alter goal-driven search templates, but rather modulated canonically stimulus-driven sensory responses to create a temporally-integrated representation of attentional priority (Adam & Serences, 2021). In a pre-registered EEG study where we obtained unexpected null results, we did a deep dive into characterizing methodological constraints on using steady-state visually evoked potentials (SSVEPs) to track enhancement and suppression of visual information (Adam, Chang, Rangan & Serences, 2021).
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*, Mance*, Fukuda & Vogel, 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*, Mance* et al., 2015; Adam, Robison & Vogel 2018; Adam, Vogel, & Awh, 2017). In other work, we have explored whether participants are self-aware of these failures (Adam & Vogel 2017), and how practice and performance feedback may be used to reduce the rate of these attention failures (Adam & Vogel, 2016; Adam & Vogel, 2018).
Developing methods for tracking working memory performance
Another arm of my work is focused on the measurement of visual working memory and on developing new ways to track active working memory storage. For example, researchers commonly use correlational approaches to investigate the relationship between different cognitive processes, but internal reliability critically limits the usefulness of correlational approaches. In a large study with up to 31 experimental sessions per participant and ~300,000 total trials, (Xu*, Adam*, Fang & Vogel, 2016) we characterized the reliability and stability of visual working memory capacity over time. Using this large sample, we used an iterative downsampling procedure to demonstrate the impact of both trial- and sample-size on estimates of reliability and we quantified the impact of large amounts of practice on stability. In further work, we demonstrated how power issues related to trial-counts have important consequences in other areas of the literature such as pharmacology (Adam, Doss, Pabon, Vogel & de Wit, 2020). In the realm of neural measurement, we have developed methods for using the multivariate EEG signal to predict trial-by-trial working memory load in a large, cross-experiment sample (>250 participants and >250,000 trials; Adam, Vogel & Awh, 2020; Thyer et al. 2022) and we have developed methods for tracking the identity of multiple items held simultaneously in mind (Sutterer*, Foster*, Adam, Vogel & Awh, 2019).