Evolution of attention in learning. In: N. Schmajuk (Ed.), Computational Models of Classical Conditioning (2010) Kruschke, J.K., & Hullinger, R.H. Cambridge University Press.. 10 – 52
An Evolutionary Analysis of Learned Attention (2014) Richard A. Hullinger, John K. Kruschke, Peter M. Todd Cognitive Science A Multidisciplinary Journal, 39 (9),
Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments.