Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T11:16:43.908Z Has data issue: false hasContentIssue false
  • English
  • Français

The application of the first order system transfer function for fitting The California Verbal Learning Test Learning Curve

Published online by Cambridge University Press:  01 March 2010

IGOR I. STEPANOV ,
CHARLES I. ABRAMSON ,
OLIVER T. WOLF  and
ANTONIO CONVIT
IGOR I. STEPANOV*
Affiliation:
Department of Neuropharmacology, Institute for Experimental Medicine, St. Petersburg, Russia
CHARLES I. ABRAMSON
Affiliation:
Department of Psychology, Oklahoma State University, Stillwater, Oklahoma
OLIVER T. WOLF
Affiliation:
Department of Cognitive Psychology, Ruhr-University Bochum, Bochum, Germany
ANTONIO CONVIT
Affiliation:
Center for Brain Health, New York University School of Medicine, New York City, New York Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
*
*Correspondence and reprint requests to: Igor I. Stepanov, Department of Neuropharmacology, Institute for Experimental Medicine, 12 Acad. Pavlov Street, St. Petersburg, 197376, Russia. E-mail: igorstep@is12044.spb.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Very few attempts have been made to apply a mathematical model to the learning curve in the California Verbal Learning Test list A immediate recall. Our rationale was to find out whether modeling of the learning curve can add additional information to the standard CVLT-II measures. We applied a standard transfer function in the form Y = B3*exp(-B2*(X-1))+B4*(1-exp(-B2*(X-1))), where X is the trial number; Y is the number of recalled correct words, B2 is the learning rate, B3 is readiness to learn and B4 is ability to learn. The coefficients of the model were found to be independent measures not duplicating standard CVLT-II measures. Regression analysis revealed that readiness to learn (B3) and ability to learn (B4) were significantly (p < .05) higher in a group of healthy participants than in a group of participants with type 2 diabetes mellitus (T2DM), but the learning rate (B2) did not differ (p > .2). The proposed model is appropriate for clinical application and as a guide for research and may be used as a good supplemental tool for the CVLT-II and similar memory tests. (JINS, 2010, 16, 443–452.)

Keywords

Mathematical modelNeuropsychologic testsT2DMFree recallMemoryAttention
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 16 , Issue 3 , May 2010 , pp. 443 - 452
Copyright
Copyright © The International Neuropsychological Society 2010

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

Benedict, R.H.B. (1997). Brief Visuospatial Memory Test — Revised: Professional manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
Brown, S., & Heathcote, A. (2003). Averaging learning curves across and within participants. Behavior Research Methods, Instruments. & Computers, 35, 11–21.CrossRefGoogle ScholarPubMed
Bruehl, H., Rueger, M., Dziobek, I., Sweat, V., Tirsi, A., Javier, E., et al. . (2007). Hypothalamic-pituitary-adrenal axis dysregulation and memory impairments in type 2 diabetes. Journal of Clinical Endocrinology and Metabolism, 92, 2439–2445.CrossRefGoogle ScholarPubMed
Conners, C.K., & Staff, M.H.S. (Eds.). (2000) Conners’ Continuous Performance Test II: Computer program for Windows technical guide and software manual. North Tonwanda, NY: Multi-Health Systems.Google Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (2000). California Verbal Learning Test — Second edition: Adult version manual. San Antonio, TX: The Psychological Corporation.Google Scholar
Draper, N.R., & Smith, H. (1981). Applied regression analysis. New York: Wiley.Google Scholar
Ericsson, K.A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.CrossRefGoogle ScholarPubMed
Estes, W.K. (2002). Traps in the route to models of memory and decision. Psychonomic Bulletin & Review, 9, 3–25.CrossRefGoogle ScholarPubMed
Fuster, J.M. (2003). Cortex and mind. New York: Oxford University Press.Google Scholar
Gallistel, C.R., Fairhurst, S., & Balsam, P. (2004). The learning curve: Implications of a quantitative analysis. Proceedings of the National Academy of Sciences of the United States of America, 101, 13124–13131.CrossRefGoogle ScholarPubMed
Golden, C.J. (1985). Luria-Nebraska neuropsychological battery: Forms I and II: Manual. Los Angeles: Western Psychological Services.Google Scholar
Grodins, F.S. (1963). Control theory and biological systems. New York: Columbia University Press.Google Scholar
Grön, G., Bittner, D., Schmitz, B., Wunderlich, A.P., & Riepe, M.W. (2002). Subjective memory complaints: Objective neural markers in patients with Alzheimer’s disease and major depressive disorder. Annals of Neurology, 51, 491–498.CrossRefGoogle ScholarPubMed
Himmelblau, D.M. (1970). Process analysis by statistical methods. New York: Wiley.Google Scholar
Leark, R.A., Greenberg, L.K., Kindschi, C.L., Dupuy, T.R., & Hughes, S.J. (2007). Test of variables of attention: Clinical manual. Los Alamitos, CA: The TOVA Company.Google Scholar
Lezak, M.D. (1995). Neuropsychological Assessment. 3 ed.New York: Oxford University Press.Google Scholar
Luria, A.R. (1962). Vishye korkovye funktzii tcheloveka i ikh narusheniya pri lokal’nikh povrezhdeniyakh mozga [The higher cortical functions in humans and its dysfunctions at local brain injury]. Moscow: Moscow State University Publishing House.Google Scholar
Mazur, J.E., & Hastie, R. (1978). Learning as accumulation: A reexamination of the learning curve. Psychological Bulletin, 85, 1256–1274.CrossRefGoogle ScholarPubMed
Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.CrossRefGoogle ScholarPubMed
Milsum, J.H. (1966). Biological control systems analysis. New York and London: McGraw-Hill Book Company.Google Scholar
Postle, B.R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, 23–38.CrossRefGoogle ScholarPubMed
Rabin, L.A., Barr, W.B., & Burton, L.A. (2005). Assessment practices of clinical neuropsychologists in the United States and Canada: A survey of INS, NAN, and APA division 40 members. Archives of Clinical Neuropsychology, 20, 33–65.CrossRefGoogle Scholar
Shrager, Y., Levy, D.A., Hopkins, R.O., & Squire, L.R. (2008). Working memory and the organization of brain systems. The Journal of Neuroscience, 28, 4818–4822.CrossRefGoogle ScholarPubMed
Squire, L.R., & Schacter, D.L. (2002). The neuropsychology of memory. New York: Guilford Press.Google Scholar
Stepanov, I.I., & Abramson, Ch.I. (2008). The application of the first order system transfer function for fitting the 3-arm radial maze learning curve. Journal of Mathematical Psychology, 52, 309–319.CrossRefGoogle Scholar
Towill, D.R. (1976). Transfer functions and learning curves. Ergonomics, 19, 623–638.CrossRefGoogle Scholar
Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53, 1–25.CrossRefGoogle Scholar
Warschausky, S., Kay, J.B., Chi, P.L., & Donders, J. (2005). Hierarchical linear modeling of California Verbal Learning Test — Children’s version learning curve characteristics following childhood traumatic head injury. Neuropsychology, 19, 193–198.CrossRefGoogle ScholarPubMed
Wechsler, D. (1997). Wechsler Memory Scale — Third Edition: Administration and scoring manual. San Antonio, TX: The Psychological Corporation.Google Scholar
Wixted, J.T. (1997). Genuine power curves in forgetting: A quantitative analysis of individual participant forgetting functions. Memory & Cognition, 25, 731–739.CrossRefGoogle Scholar
Zimprich, D., Rast, P., & Martin, M. (2008). Individual differences in verbal learning in old age. In Hofer, S., & Alwin, D.F. (Eds.), The handbook of cognitive aging: Interdisciplinary perspective (pp. 224–243). Thousand Oaks, CA: Sage Publications.CrossRefGoogle Scholar