A Markov Model of Conditional Associative Learning in a Cognitive Behavioural Scenario

  • Stefan Glüge
  • Oussama H. Hamid
  • Jochen Braun
  • Andreas Wendemuth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)


In conditional learning, one investigates the computational principles by which the human brain solves challenging recognition problems. The role of temporal context in the learning of arbitrary visuo-motor associations has so far been studied mostly in primates. We model the explicit learning task where a sequence of visual objects is presented to human subjects. The computational modelling of the algorithms that appear to underlie human performance shall capture the effects of confusion in ordered and random presentation of objects. We present a Markov model where the learning history of a subject on a certain object is represented by the states of the model. The analysis of the resulting Markov chain makes it possible to judge the influence of two model parameters without the simulation of a specific learning scenario. As the model is able to reproduce the learning behaviour of human subjects it might be useful in the development of future experiments.


Markov Model Temporal Context Current Object Fractal Object Recall Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bunge, S.A., Wallis, J.D., Parker, A., Brass, M., Crone, E.A., Hoshi, E., Sakai, K.: Neural circuitry underlying rule use in humans and nonhuman primates. J. Neurosci. 25(45), 10347–10350 (2005)CrossRefGoogle Scholar
  2. 2.
    Gaffan, D., Harrison, S.: Inferotemporal-frontal disconnection and fornix transection in visuomotor conditional learning by monkeys. Behav. Brain Res. 31, 149–163 (1988)CrossRefGoogle Scholar
  3. 3.
    Glüge, S., Hamid, O.H., Wendemuth, A.: A Simple Recurrent Network for Implicit Learning of Temporal Sequences. Cognitive Computation 2(4), 265–271 (2010)CrossRefGoogle Scholar
  4. 4.
    Hamid, O.H., Braun, J.: Task-irrelevant temporal order and learning of arbitrary visuo-motor associations. In: Perception 36: ECVP 2007, Arezzo, pp. 50–51 (2007)Google Scholar
  5. 5.
    Hamid, O.H., Wendemuth, A., Braun, J.: Temporal context and conditional associative learning. BMC Neuroscience 11, 45 (2010)CrossRefGoogle Scholar
  6. 6.
    Miyashita, Y.: Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature 335, 817–820 (1988)CrossRefGoogle Scholar
  7. 7.
    Miyashita, Y., Chang, H.S.: Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature 331, 68–70 (1988)CrossRefGoogle Scholar
  8. 8.
    Muhammad, R., Wallis, J.D., Miller, E.K.: A comparison of abstract rules in the prefrontal cortex, premotor cortex, the inferior temporal cortex and the striatum. J. Cognit. Neurosci. 18, 974–989 (2006)CrossRefGoogle Scholar
  9. 9.
    Petrides, M.: Conditional learning and the primate frontal cortex. In: Perecman, E. (ed.) The Frontal Lobes Revisited, pp. 91–108. The IRBN Press, New York (1987)Google Scholar
  10. 10.
    Wise, S.P., Murray, E.A.: Arbitrary associations between antecedents and actions. Trends Neurosci. 23(6), 271–276 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Stefan Glüge
    • 1
  • Oussama H. Hamid
    • 2
  • Jochen Braun
    • 2
  • Andreas Wendemuth
    • 1
  1. 1.Faculty of Electrical Engineering and Information TechnologyOtto von Guericke University MagdeburgMagdeburgGermany
  2. 2.Cognitive Biology, Institute of BiologyOtto von Guericke University MagdeburgMagdeburgGermany

Personalised recommendations