TY - JOUR
T1 - Reflections of idiographic long-term memory characteristics in resting-state neuroimaging data
AU - Zhou, Peiyun
AU - Sense, Florian
AU - van Rijn, Hedderik
AU - Stocco, Andrea
N1 - Funding Information:
All of the supporting data and the analysis code can be found online at https://github.com/UWCCDL/EEG_RateOfForgetting and https://osf.io/47mu3/ . This research was partially supported by grant FA9550-19-1-0299 from the Air Force Office of Scientific Research to AS.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/7
Y1 - 2021/7
N2 - 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. Statistical analyses revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials. Importantly, both rates correlated with resting-state power levels in the low (13–15 Hz) and upper (15–17 Hz) portion of the beta frequency bands. These correlations were particularly strong for visuospatial materials, were distributed over multiple fronto-parietal locations, and remained significant even after a correction for multiple comparisons (False Discovery Rate) and after robust correlation methods were applied. These results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings.
AB - 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. Statistical analyses revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials. Importantly, both rates correlated with resting-state power levels in the low (13–15 Hz) and upper (15–17 Hz) portion of the beta frequency bands. These correlations were particularly strong for visuospatial materials, were distributed over multiple fronto-parietal locations, and remained significant even after a correction for multiple comparisons (False Discovery Rate) and after robust correlation methods were applied. These results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings.
KW - ACT-R
KW - Computational models
KW - EEG
KW - Individual differences
KW - Long-term memory
KW - Rate of forgetting
UR - http://www.scopus.com/inward/record.url?scp=85102751284&partnerID=8YFLogxK
U2 - 10.1016/j.cognition.2021.104660
DO - 10.1016/j.cognition.2021.104660
M3 - Article
C2 - 33756150
AN - SCOPUS:85102751284
SN - 0010-0277
VL - 212
JO - Cognition
JF - Cognition
M1 - 104660
ER -