Graduate Thesis Or Dissertation

 

Classifcation Aided Analysis of Oscillatory Signatures in Controlled Retrieval Public Deposited

https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/kh04dq05z
Abstract
  • Control over memory retrieval is a ubiquitous process within humans, yet this process and its implications are not yet well understood. The current work, inspired by a similar fMRI design (Detre, Natarajan, Gershman, & Norman, in press), uses a modified Think/No-Think(TNT) paradigm to investigate the neural signatures of volition over enhancing and suppressing memory retrieval. Previous studies in this domain have shown a memory enhancement when well learned stimulus-pairs are restudied in cued recall (Think of studied pair item), and a degradation in memory performance when restudied with cued suppression (Dont think of studied pair item). As is the case in the current study, this memory effect has been somewhat difficult to replicate (Bulevich, Roediger, Balota, & Butler, 2006). We address these issues by using category based (faces vs. scenes) multivariate classification of Electroencephalography within a modified TNT paradigm to determine if individuals were successfully able to retrieve or suppress retrieval of the target pair image. A logistic regression using classifier determined retrieval success as a predictor for subsequent memory reveals the classic TNT pattern of enhanced memory for successful cued-retrieval and degraded memory for unsuccessful cued-retrieval. Further, this classification process allowed for a highly selective investigation into the time-frequency signatures of control over retrieval. Current results, using a cluster based analysis, show Theta oscillations (3 to 8 Hz) as more prominent in controlled retrieval as compared to controlled suppression, and Alpha oscillations (8 to 12 Hz) as more prominent in suppression retrieval specifically. These results further suggest a specific role of Beta oscillations (12 to 30 Hz) in high levels of controlled retrieval compared to low.
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  • 2013
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  • 2019-11-18
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