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Title: Consolidation and retention of auditory categories acquired incidentally in performing a visuomotor task
A wealth of evidence indicates the existence of a consolidation phase, triggered by and following a practice session, wherein new memory traces relevant to task performance are transformed and honed to represent new knowledge. But, the role of consolidation is not well-understood in category learning and has not been studied at all under incidental category learning conditions. Here, we examined the acquisition, consolidation and retention phases in a visuomotor task wherein auditory category information was available, but not required, to guide detection of an above-threshold visual target across one of four spatial locations. We compared two training conditions: (1) Constant, whereby repeated instances of one exemplar from an auditory category preceded a visual target, predicting its upcoming location; (2) Variable, whereby five distinct category exemplars predicted the visual target. Visual detection speed and accuracy, as well as the performance cost of randomizing the association of auditory category to visual target location, were assessed during online performance, again after a 24-hour delay to assess the expression of delayed gains, and after 10 days to assess retention. Results revealed delayed gains associated with incidental auditory category learning and retention effects for both training conditions. Offline processes can be triggered even for incidental more » auditory input and lead to category learning; variability of input can enhance the generation of incidental auditory category learning. « less
Authors:
; ;
Award ID(s):
1655126
Publication Date:
NSF-PAR ID:
10085202
Journal Name:
Proceedings of the 40th Annual Conference of the Cognitive Science Society
Sponsoring Org:
National Science Foundation
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We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. 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