Impact of Attention on Perception in Cognitive Dynamic Systems

Abstract

The proposed aim of this thesis, inspired by the human brain, is to improve on the performance of a perceptual processing algorithm, referred to as a “perceptor”. This is done by trying to bridge the gap between neuroscience and engineering. To this end, we build on localized perception-action cycle in cognitive neuroscience by categorizing it under the umbrella of perceptual attention, which lends itself to increase gradually the contrast between relevant information and irrelevant information. Stated in another way, irrelevant information is filtered away while relevant information about the environment is enhanced from one cycle to the next. Accordingly, we propose to improve on the performance of a perceptor by modifying it to operate under the influence of perceptual attention. For this purpose, we first start with a single-layered perceptor and investigate the impact of perceptual attention on its performance through two computer experiments: The first experiment uses simulated (real-valued) data that are generated to purposely make the problem challenging. The second experiment uses real-life radar data that are complex-valued, hence the proposal to introduce Wirtinger calculus into derivation of our proposed method. We then take one step further and extend our proposed method to the case where a perceptor is hierarchical. In this context, every constitutive component of a hierarchical perceptor is modified to operate under the influence of perceptual attention. Then, another experiment is carried out to demonstrate the positive impact of perceptual attention on the performance of that hierarchical perceptor, just described.