{"pk":29768,"title":"Using K-means Clustering for Out-of-Sample Predictions of Memory Retention","subtitle":null,"abstract":"In applied settings, computational models of memory haveproven useful in making principled performance predictions.Specifically, historical data are used to derive modelparameters in order to enable out-of-sample predictions.Parameters are typically fit to meaningful subsets of data.However, labels that demarcate what constitutes a“meaningful” subset are not always available. Here, we utilizea data-driven method to cluster past performance into subsetspossessing statistical similarities. We contrast predictions fromcluster-specific model parameters with predictions based onsubsets that are artifacts of the experimental design. We showthat cluster-based predictions are at least as accurate as thechosen baselines and highlight additional advantages of thedata-driven approach.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"learning; memory; k-means clustering;computational model; prediction"}],"section":"Poster Session 2","is_remote":true,"remote_url":"https://escholarship.org/uc/item/61n9h3q5","frozenauthors":[{"first_name":"Florian","middle_name":"","last_name":"Sense","name_suffix":"","institution":"University of Groningen","department":""},{"first_name":"Michael","middle_name":"","last_name":"Coll","name_suffix":"","institution":"ORISE at Air Force Research Laboratory","department":""},{"first_name":"Michael","middle_name":"","last_name":"Krusmark","name_suffix":"","institution":"Air Force Research Laborator","department":""},{"first_name":"Tiffany","middle_name":"S.","last_name":"Jastrzembski","name_suffix":"","institution":"Air Force Research Laborator","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29768/galley/19622/download/"}]}