Abstract
When stimuli have to be matched in a complex task (such as whether 2 letters have the same name), then performance is better when stimuli are presented across the hemispheres of the brain, whereas for simpler tasks (such as whether 2 letters have the same shape), better performance is achieved when stimuli are presented unilaterally. The authors show that this bilateral distribution advantage effect emerged spontaneously in a neural network model learning to solve simple and complex tasks with separate input layers and separate, but interconnected, resources in a hidden layer. The authors show that relating computational models to behavioral and imaging data proves fruitful for understanding hemispheric processing and generating testable hypotheses.
