Characterizing Cognitive Adaptability via Robust Automated Knowledge Capture

  • Robert G. Abbott
  • J. Chris Forsythe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)


Applications such as individually tailored training and behavior emulation call for cognitive models tailored to unique individuals on the basis of empirical data. While the study of individual differences has been a mainstay of psychology, a prevailing assumption in cognitive theory and related modeling has been that cognitive processes are largely invariant across individuals and across different conditions for an individual. Attention has focused on identifying a universally correct set of components and their interactions. At the same time, it is known that aptitudes for specific skills vary across individuals and different individuals will employ different strategies to perform the same task [3]. Moreover, individuals will perform tasks differently over time and under different conditions (e.g. Taylor et al, 2004). To reach their full potential, systems designed to augment cognitive performance must thus account for such between- and within-individual differences in cognitive processes. We propose that cognitive adaptability is a trait necessary to explain the inherently dynamic nature of cognitive processes as individuals adapt their available resources to ongoing circumstances. This does not imply a “blank slate;” humans are predisposed to process information in particular ways. Instead, we assert that given variation in the structure and functioning of the brain, there exists inherent flexibility that may be quantified and used to predict differences in cognitive performance between individuals and for a given individual over time. This paper presents an early report on research we are undertaking to discover the dynamics of cognitive adaptability, with emphasis on a task environment designed to evoke and quantify adaptation in controlled experiments.


Cognitive Model Sandia National Laboratory Task Environment Memory Score Drawing Task 
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  1. 1.
    Anderson, J.R., Lebiere, C.: The atomic components of thought. Erlbaum, Mahwah (1998)Google Scholar
  2. 2.
    Cooper, C.: Individual Differences. Arnold, Belfast (2002)Google Scholar
  3. 3.
    Miller, M.B., Van Horn, J.D., Wolford, G.L., Handy, T.C., Valsangkar-Smyth, M., Inati, S., Grafton, S., Gazzaniga, M.S.: Extensive individual differences in brain activations associated with episodic retrieval are reliable over time. Journal of Cognitive Neuroscience 148, 1200–1214 (2002)CrossRefGoogle Scholar
  4. 4.
    Taylor, S.F., Welsh, R.C., Wager, T.D., Luan Phan, K., Fitzgerald, K.D., Gehring, W.J.: A functional neuroimaging study of motivation and executive function. NeuroImage 21(3), 1045–1054 (2004)CrossRefPubMedGoogle Scholar
  5. 5.
    Wray, R.E., Laird, J.E.: An architectural approach to consistency in hierarchical execution. Journal of Artificial Intelligence Research 19, 355–398 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Robert G. Abbott
    • 1
  • J. Chris Forsythe
    • 1
  1. 1.Sandia National LaboratoriesAlbuquerqueUSA

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