Fluctuations of neural activity impact memory. Subsequent memory analyses have demonstrated that particular neural states predict better working memory and long-term memory behavior. These analyses are typically conducted after data collection, but monitoring neural fluctuations in real time would enable more direct and timely interventions. We will use real-time electroencephalography (EEG) to track moment-to- moment fluctuations of neural activity in order to more directly link brain signals with behavior, and to enhance memory performance. In this proposal, we focus on two key moments for memories: pre-stimulus (Aim 1) and active maintenance during a retention interval (Aim 2).
In Aim 1, we will test the hypothesis that pre-stimulus neural signals (oscillatory alpha and theta) predict memory encoding success. In Experiment 1, we will vary the point of time when the stimuli appear based on real-time calculations of alpha and theta power. We will use neural activity as the independent variable to ?trigger? stimulus presentation when the brain is in either advantageous states (low alpha, high theta) or disadvantageous states (high alpha, low theta). We predict that better brain states will predict better working memory and long-term memory precision in a sensitive continuous report task. In Experiment 2, we will provide neurofeedback to reward advantageous pre-stimulus brain states (low alpha, high theta). We predict that up-regulating these advantageous states will lead to enhanced memory performance (more precise memories).
In Aim 2, we will test the hypothesis that sustained activity tracks active maintenance of information. Sustained activity is a key signature of working memory, but recent evidence has questioned its role through the demonstration of activity-silent working memory. In Experiment 3, we will use real-time measures of sustained activity (contralateral delay activity, multivariate alpha topography) to adjust the duration of a retention interval and the identity of working memory probes. We predict that performance will be better (quicker reaction times, more precise memories) when memory probes are triggered based on higher sustained activity. In Experiment 4, we will provide neurofeedback during the retention interval to reward greater sustained activity. We predict that up-regulating these advantageous states will lead to greater memory precision. Across these experiments, we will explore memory encoding via the lens of real-time EEG to trigger information (Experiments 1 & 3) and provide feedback (Experiments 2 & 4). The proposed research will characterize the fate of mnemonic representations by tracking and driving neural activity both pre-encoding (Aim 1) and post-encoding (Aim 2), in order to understand how neural fluctuations give rise to what we remember.
Neural fluctuations in the moment have downstream consequences on what we remember later. The proposed research will track neural fluctuations in real time in order to detect and provide feedback about moments when the brain is in a disadvantageous state. We will explore how these real-time interventions can more directly link brain states with behavior and can be used to enhance working and long-term memory performance.
