Pupillometry tracks fluctuations in working memory performance
In 3 experiments, we examined fluctuations in working memory (WM) performance and associated changes in pretrial and task-evoked pupil diameter. Additionally, we examined whether particularly poor trials were accompanied by self-reports of off-task attentional states. The results demonstrated that task-evoked pupillary responses can be used to measure moment-to-moment fluctuations in the success of WM maintenance during delay intervals. Further, when individuals reported being in an off-task attentional state, their WM performance suffered. Additionally, when probed directly after a particularly poor trial, participants reported being in an off-task attentional state more often than at random intervals throughout the task. So behavioral, subjective, and physiological data converged when people experienced WM failures. Although pretrial pupil diameter did not consistently differentiate between successful and unsuccessful trials, variability in pretrial pupil diameter accounted for a significant portion of variance in WM task performance. This effect persisted after controlling for mean task-evoked pupillary response and variability in task-evoked pupillary responses. Thus, one of the major reasons people varied in the consistency with which they utilized their WM system was variability in arousal. Such variability in arousal is potentially due to variation in the functioning of the locus coeruleus-norepinephrine (LC-NE) neuromodulatory system, and thus may underlie individual differences in WM capacity and attention control.
KeywordsWorking memory Attention Pupillometry Mind-wandering
We would like to thank Steven Karmann and Ashley Miller for their assistance in data collection.
- Alnæs, D., Sneve, M.H., Espeseth, T., Endestad, T., van de Pavert, S.H.P., & Laeng, B. (2014). Pupil size signals mental effort deployed during multiple object tracking and predicts brain activity in the dorsal attention network and the locus coeruleus. Journal of Vision, 14, 1–1. https://doi.org/10.1167/14.4.1
- Aston-Jones, G., & Cohen, J.D. (2005). An integrative theory of locus coeruleus-norepinephrine function: Adaptive gain and optimal performance. Annual Review of Neuroscience, 28, 403–450. https://doi.org/10.1146/annurev.neuro.28.061604.135709 CrossRefGoogle Scholar
- Aust, F., & Barth, M. (2018). papaja: Create APA manuscripts with R Markdown. Retrieved from https://github.com/crsh/papaja.
- Cowan, N. (2001). Metatheory of storage capacity limits. Behavioral and Brain Sciences, 24, 154–176.Google Scholar
- Franklin, M.S., Broadway, J.M., Mrazek, M.D., Smallwood, J., & Schooler, J.W. (2013). Window to the wandering mind: Pupillometry of spontaneous thought while reading. The Quarterly Journal of Experimental Psychology, 66(12), 2289–2294. https://doi.org/10.1080/17470218.2013.858170 CrossRefGoogle Scholar
- Gilzenrat, M.S., Nieuwenhuis, S., Jepma, M., & Cohen, J.D. (2010). Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function. Cognitive, Affective, & Behavioral Neuroscience, 10, 252–269. https://doi.org/10.3758/CABN.10.2.252 CrossRefGoogle Scholar
- R Core Team (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.
- Unsworth, N., & Robison, M.K. (2017b). The importance of arousal for variation in working memory capacity and attention control: A latent variable pupillometry study. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43, 1962–1987. https://doi.org/10.1037/xlm0000421 Google Scholar