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Computational Modeling of Visual Selective Attention Based on Correlation and Synchronization of Neural Activity

  • Kleanthis C. Neokleous
  • Marios N. Avraamides
  • Christos N. Schizas
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)

Abstract

Within the broad area of computational intelligence, it is of great importance to develop new computational models of human behaviour aspects. In this report we look into the recently suggested theory that neural synchronization of activity in different areas of the brain occurs when people attend to external visual stimuli. Furthermore, it is suspected that this cross-area synchrony may be a general mechanism for regulating information flow through the brain. We investigate the plausibility of this hypothesis by implementing a computational model of visual selective attention that is guided by endogenous and exogenous goals (i.e., what is known as top down and bottom-up attention). The theoretical structure of this model is based on the temporal correlation of neural activity that was initially proposed by Niebur and Koch (1994). While a saliency map is created in the model at the initial stages of processing visual input, at a later stage of processing, neural activity passes through a correlation control system which comprises of coincidence detector neurons. These neurons measure the degree of correlation between endogenous goals and the presented visual stimuli and cause an increase in the synchronization between the brain areas involved in vision and goal maintenance. The model was able to simulate with success behavioural data from the “at-tentional blink” paradigm (Raymond and Sapiro, 1992). This suggests that the temporal correlation idea represents a plausible hypothesis in the quest for understanding attention.

Keywords

Visual Stimulus Stimulus Onset Asynchrony Neural Activity Attentional Blink Rapid Serial Visual Presentation 
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.

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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Kleanthis C. Neokleous
    • 2
  • Marios N. Avraamides
    • 1
  • Christos N. Schizas
    • 2
  1. 1.Department of PsychologyUniversity of CyprusNicosiaCyprus
  2. 2.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

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