Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-23T21:38:34.274Z Has data issue: false hasContentIssue false
  • English
  • Français

Breaking the Percent Memory Retention Ceiling using Bayesian Statistics

Published online by Cambridge University Press:  05 October 2020

Umesh M. Venkatesan Open the ORCID record for Umesh M. Venkatesan [Opens in a new window] ,
Amanda R. Rabinowitz  and
Rachael M. Riccitello
Umesh M. Venkatesan*
Affiliation:
Moss Rehabilitation Research Institute, 50 Township Line Road, Suite 100, Elkins Park, PA 19027, USA
Amanda R. Rabinowitz
Affiliation:
Moss Rehabilitation Research Institute, 50 Township Line Road, Suite 100, Elkins Park, PA 19027, USA
Rachael M. Riccitello
Affiliation:
Moss Rehabilitation Research Institute, 50 Township Line Road, Suite 100, Elkins Park, PA 19027, USA
*
Correspondence and reprint requests to: Umesh M. Venkatesan, PhD. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

Neuropsychological tests of episodic memory often include a measure of memory retention to facilitate the diagnosis of memory disorders. However, the traditional percent retention (PR) score has limited interpretability when smaller amounts of information are both initially learned and later recalled, creating a pseudo-ceiling effect. To improve psychometrics of PR, we investigated a scoring procedure that incorporates levels of certainty into estimates of memory retention based on learning level.

Methods:

Word-list recall data from adults with traumatic brain injury were modeled using a uniform prior in the Bayesian framework. From the resultant posterior probability distributions, we derived a measure referred to as retention probability (RPr), which distinguishes the retention of relatively good and poor learners. PR and RPr scores were compared on their distributional properties and associations with theoretically related memory measures.

Results:

Significant distributional differences between PR and RPr were observed. RPr removed the conspicuous ceiling of PR, resulting in stronger correlational and predictive relationships with other memory measures.

Conclusion:

A Bayesian procedure for quantifying memory retention has psychometric advantages and potentially widespread applicability for measuring the change in behavioral features over time. Future directions are briefly discussed. A sample RPr calculator is provided for interactive exploration of the method.

Keywords

Episodic memoryMemory consolidationNeuropsychological assessmentPsychometricsTraumatic brain injuryDementia
Type
Brief Communication
Information
Journal of the International Neuropsychological Society , Volume 27 , Issue 4 , April 2021 , pp. 396 - 400
Copyright
Copyright © INS. Published by Cambridge University Press, 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Barnett, A.G., van der Pols, J.C., & Dobson, A.J. (2005). Regression to the mean: what it is and how to deal with it. International Journal of Epidemiology, 34(1), 215–220. https://doi.org/10.1093/ije/dyh299 CrossRefGoogle ScholarPubMed
Benedict, R.H.B., Schretlen, D., Groninger, L., & Brandt, J. (1998). Hopkins verbal learning test—revised: normative data and analysis of inter-form and test-retest reliability. The Clinical Neuropsychologist, 12(1), 43–55.CrossRefGoogle Scholar
Elliott, G., Isaac, C.L., & Muhlert, N. (2014). Measuring forgetting: a critical review of accelerated long-term forgetting studies. Cortex, 54, 16–32. https://doi.org/10.1016/j.cortex.2014.02.001 CrossRefGoogle ScholarPubMed
Gelman, A., Carlin, J.B., Stern, H.S., & Rubin, D.B. (2003). Bayesian Data Analysis (2nd ed.) Taylor & Francis. https://books.google.com/books?id=TNYhnkXQSjAC CrossRefGoogle Scholar
Lee, I.A. & Preacher, K.J. (2013). Calculation for the test of the difference between two dependent correlations with one variable in common. http://quantpsy.org Google Scholar
Loftus, G.R. (1985). Evaluating forgetting curves. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11(2), 397–406. https://doi.org/10.1037/0278-7393.11.2.397 Google Scholar
Lynch, S.M. (2007). Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. https://books.google.com/books?id=JN0rPpEpw3IC CrossRefGoogle Scholar
Myers, L. & Sirois, M. (2004). Differences between spearman correlation coefficients. Encyclopedia of Statistical Evidence. https://doi.org/10.1002/0471667196.ess5050 CrossRefGoogle Scholar
Randolph, C., Tierney, M.C., Mohr, E., & Chase, T.N. (1998). The repeatable battery for the assessment of neuropsychological status (RBANS): preliminary clinical validity. Journal of Clinical and Experimental Neuropsychology, 20(3), 310–319. https://doi.org/10.1076/jcen.20.3.310.823 CrossRefGoogle ScholarPubMed
Squire, L.R. (2006). Lost forever or temporarily misplaced? The long debate about the nature of memory impairment. Learning & Memory (Cold Spring Harbor, N.Y.), 13(5), 522–529. https://doi.org/10.1101/lm.310306 CrossRefGoogle ScholarPubMed
Squire, L.R., Genzel, L., Wixted, J.T., & Morris, R.G. (2015). Memory consolidation. Cold Spring Harbor Perspectives in Biology, 7(8), a021766–a021766. https://doi.org/10.1101/cshperspect.a021766 CrossRefGoogle ScholarPubMed
The jamovi project. (2019). Jamovi (Version 0.9). https://www.jamovi.org Google Scholar
Tuyl, F., Gerlach, R., & Mengersen, K. (2009). Posterior predictive arguments in favor of the Bayes-Laplace prior as the consensus prior for binomial and multinomial parameters. Bayesian Analysis, 4. https://doi.org/10.1214/09-BA405 Google Scholar
van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J.B., Neyer, F.J., & van Aken, M.A.G. (2014). A gentle introduction to Bayesian analysis: applications to developmental research. Child Development, 85(3), 842–860. PubMed. https://doi.org/10.1111/cdev.12169 CrossRefGoogle ScholarPubMed
Weissberger, G.H., Strong, J.V., Stefanidis, K.B., Summers, M.J., Bondi, M.W., & Stricker, N.H. (2017). Diagnostic accuracy of memory measures in Alzheimer’s Dementia and mild cognitive impairment: a systematic review and meta-analysis. Neuropsychology Review, 27(4), 354–388. https://doi.org/10.1007/s11065-017-9360-6 CrossRefGoogle ScholarPubMed
Supplementary material: File

Venkatesan et al. supplementary material

Appendices A-B

Download Venkatesan et al. supplementary material(File)
File 20.6 KB