Decision Making Logic of Visual Brain

  • Andrzej W. Przybyszewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)


A popular view is that the brain is making fast decisions in temporal and frontal cortices predominantly on the bases of the feedforward pathways (FF). In later stages iterations (reverberations) with delayed feedback connections (FB) may be helpful. We propose, an opposite concept, that decisions are made in single neurons from the retina to the cortex, and that FB is fast as FF, and from the beginning participates in making decisions. The main differences between FF and FB are their different logics: FF follows driver logical rules, but FB follows modulator logical rules. Driver logical rules are gathering all possible information together therefore and they are context dependent, FB pathways, however, using selective modulator logical rules extract only hypothetically important information. We say that FF FB interaction is prediction hypothesis testing system. Our psychophysical system is different than Turing Machine because we are often insensible to changes of some symbols but same symbols in different configuration may lead to different classification of the same object. In present work we are looking for the anatomical and neurophysiological basis of these perceptual effects. We describe interactions between parts and their configurations on the basis of a single cell electrophysiological activity in cortical area V4. This area is related to simple shape classification. We have divided area V4 cell responses into three categories and found equivalent classes of object attributes for each cell response category. On this basis, we found decision rules for different area V4 cells (rough set theory - Pawlak, 1992 [1]). Some of these rules are not consistent, which may suggest that the brain may use different, non-consistent strategies in parallel in order to classify significant attributes of the unknown object.


Imprecise computation bottom-up top-down processes neuronal activity 


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  1. 1.
    Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)zbMATHGoogle Scholar
  2. 2.
    Pollen, D.A., Przybyszewski, A.W., Rubin, M.A., Foote, W.: Spatial receptive field organization of macaque V4 neurons. Cereb Cortex 12, 601–616 (2002)CrossRefGoogle Scholar
  3. 3.
    Przybyszewski, A.W.: Checking Brain Expertise Using Rough Set Theory. In: Kryszkiewicz, M., et al. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 746–755. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Zadah, L.A.: Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. Journal of Statistical Planning and Inference 105, 233–264 (2002)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Przybyszewski, A.W., Linsay, P.S., Gaudiano, P., Wilson, C.: Basic Difference Between Brain and Computer: Integration of Asynchronous Processes Implemented as Hardware Model of the Retina. IEEE Trans Neural Networks 18, 70–85 (2007)CrossRefGoogle Scholar
  6. 6.
    Treisman, A.: Features and objects: the fourteenth Bartlett memorial lecture. Q. J. Exp. Psychol. A. 40, 201–237 (1988)Google Scholar
  7. 7.
    Przybyszewski, A.W., Gaska, J.P., Foote, W., Pollen, D.A.: Striate cortex increases contrast gain of macaque LGN neurons. Vis. Neurosci. 17, 485–494 (2000)CrossRefGoogle Scholar
  8. 8.
    Przybyszewski, A.W., Kon., M.A.: Synchronization-based model of the visual system supports recognition. Program No. 718.11, 2003 Abstract Viewer/Itinerary Planner. Washington, DC, Society for Neuroscience, Online (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andrzej W. Przybyszewski
    • 1
    • 2
  1. 1.Dept of NeurologyUniversity of Massachusetts Medical CenterWorcesterUS
  2. 2.Dept pf PsychologyMcGill UniversityMontrealCanada

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