Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.18.047662v1?rss=1 Authors: Zhou, P., Sense, F., van Rijn, H., Stocco, A. Abstract: Translational applications of cognitive science depend on having predictive models at the individual, or idiographic, level. However, idiographic model parameters, such as working memory capacity, often need to be estimated from specific tasks, making them dependent on task-specific assumptions. Here, we explore the possibility that idiographic parameters reflect an individual's biology and can be identified from task-free neuroimaging measures. To test this hypothesis, we correlated a reliable behavioral trait, the individual rate of forgetting in long-term memory, with a readily available task-free neuroimaging measure, the resting-state EEG spectrum. Using an established, adaptive fact-learning procedure, the rate of forgetting for verbal and visual materials was measured in a sample of 50 undergraduates from whom we also collected eyes-closed resting-state EEG data. A statistical analysis revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials, in agreement previous results. Importantly, both rates correlated with power levels in the alpha (8-13 Hz) and low beta (13-15 Hz) frequency bands, with the correlation between verbal rate of forgetting and low beta power over the right parietal site being significant even when accounting for multiple comparisons. The results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings. Copy rights belong to original authors. Visit the link for more info