Current Projects

Aging and Working Memory Training

P.I. Alexandru Iordan

This long-term project investigates changes in the neural mechanisms of working memory after several days of training. Participants undergo a series of fMRI scans, neuropsychological assessments, and ten days of training on a computerized working memory task. We aim to identify functional changes in brain activation and possible changes in measures of neural efficiency associated with working memory training. We also aim to identify age-related differences in these changes by including both younger (18-24) and older (65+) adults in this study.

Cognitive and Affective Distraction on Working Memory

P.I. Alexandru Iordan

Distractions occur often in our daily lives and can affect our ability to remain focused on a goal-oriented task. For instance, receiving a distressing text message may interfere with one’s ability to remain focused on studying for an exam. The process known as working memory is critical for the maintenance of goal-oriented information, but the mechanisms of this phenomenon are still being researched. We aim to understand how the effects of distraction impact WM performance and furthermore, what are the underlying brain mechanisms responsible for this effect.

Additional Readings:
Dolcos, F., & McCarthy, G. (2006). Brain systems mediating cognitive interference by emotional
distraction. Journal of Neuroscience, 26(7), 2072–79.

Hakun, J. G., & Ravizza, S. M. (2016). Ventral fronto-parietal contributions to the disruption of visual working memory storage. NeuroImage, 124(Pt A), 783–93.

Affective Working Memory

P.I. Colleen Frank

We seek to understand how working memory functions when holding emotions in mind. Our research currently investigates in what way this ability, known as affective working memory (AWM), plays a role in higher order processing. Our current projects include:

Affective Forecasting and AWM:  Affective forecasting refers to the ability to predict our future feelings, which plays a large role in how we make choices. We examine the role AWM and cognitive working memory plays in individual differences in the accuracy of these predictions.

Rumination and AWM: Rumination involves repetitive negative thinking as a response to distressing events. While we know cognitive working memory capacity is related to the detrimental effects of rumination, we aim to test if affective working memory plays a role in this process.

AWM throughout the lifespan: While it is known that working memory, among other cognitive processes, decline throughout the lifespan, recent research suggests age-related changes in emotional processing may reveal a different trajectory. For example, Mikels et al (2005) found that older adults showed impairments on a task of non-affective maintenance (i.e., brightness maintenance) but performed comparably to younger adults on an affect maintenance task. We seek to understand these diverging trajectories using participants across all adult age groups.

Additional Readings:

Mikels, J.A. & Reuter-Lorenz, P.A. Affective working memory: An integrative psychological construct. Perspectives on Psychological Science, 14(4), 543 – 559.

Frank, C. C., Iordan, A. D., Ballouz, T. L., Mikels, J. A., & Reuter-Lorenz, P. A. (2020). Affective forecasting: A selective relationship with working memory for emotion. Journal of Experimental Psychology: General. Advance online publication.

Effects of Transcranial Direct Current Stimulation

P.I. Colleen Frank & Kathy Xie

We are currently examining the effects of transcranial direct current stimulation (tDCS) on working memory training.  We are interested to see if the addition of the non-invasive brain stimulation yields significant improvements in performance above and beyond the training alone.

Additional Readings:
Jantz, T. K., Katz, B., & Reuter-Lorenz, P. A. (2016). Uncertainty and promise: the effects of transcranial direct current stimulation on working memory. Current Behavioral Neuroscience Reports, 3(2), 109-121.

Cognitive Offloading

P.I. Lilian Cabrera

The limitations of many of our cognitive functions have been well documented. For example, we can only hold a limited amount of information active in memory. When the cognitive demands of a task increase, we can either continue relying on our internal memory processes to remember information or, we can “offload” the demands into the external environment, a behavior referred to as cognitive offloading (Risko & Gilbert, 2016). While individuals of any age can engage in cognitive offloading, this strategy can be especially useful for older adults to compensate for age-related declines in cognition so that they can better meet the demands of everyday life. We are currently investigating factors that influence individuals’ decision to offload and the cognitive consequences of this behavior.

Additional Readings:
Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Science, 20(9), 676-688.

Value Learning

P.I. Lilian Cabrera

Reinforcement learning enables agents to make optimal decisions (maximize gains, minimize losses). We are investigating how learning scenes associated with high or low probability gains or losses to understand how outcome valence and motivational salience influence learning. We are also exploring how the outcome valence and motivational salience, in turn, influence what we explicitly remember about the scenes. The effects of monetary incentives were also examined.

Additional Readings:
O’Brien, J. L., & Raymond, J. E. (2012). Learned predictiveness speeds visual processing. Psychological Science, 23(4), 359-363.

Raymond, J. E., & O’Brien, J. L. (2009). Selective visual attention and motivation: The consequences of value learning in an attentional blink task. Psychological Science, 20(8), 981-988.