Quantitative Model of Human Dynamic Attention and Perception

The development of tractable mathematical models of gradient-frequency nonlinear resonator networks is critical to advanced computer applications that require faithful computer simulation of human attention and/or perception. Conventional linear resonator models are computationally and analytically tractable, however tractability is achieved at the expense of capturing many significant features of attention and perception. Allocation of attention to complex event sequences displays significant nonlinearities, including phase transitions and higher-order resonances. The perception of acoustic events also shows significant nonlinearities including extreme perceptual sensitivity, high frequency resolution, and higher-order resonances. Modern theoretical models of attentional and perceptual phenomena have one thing in common: they are all nonlinear oscillators or networks of nonlinear oscillators responding to perceptual input. Thus a nonlinear time-frequency transformation software library will be useful for analysis of temporal structure across the various different time scales associated with human attention and perception. In Phase I, feasibility of signal analysis by gradient-frequency nonlinear resonator networks was demonstrated. Phase II will pursue advanced development of computer models of gradient-frequency nonlinear resonator networks. A software library for deployment in advanced military and commercial computer applications will be developed, tested and documented.