June 21, 2022
Daniel v/d Meer
supervisors: Maartje Raijmakers, Raoul Grasman, & Han van der Maas
As intrinsically connectionist models, artificial neural networks (ANN) are not made to explicitly deal with symbolic representations. Still, in their parallelly distributed way of processing data, their mechanisms have promising similarities to biological neural networks. By using ANNs as model for human processing, Raijmakers and colleagues (1996) investigated rule-like behavior in ANNs by applying a binary encoded version of the discrimination-shift paradigm. Based on their findings, they concluded that simple ANNs do not show rule-like behavior. By replicating the results of Raijmakers and colleagues, and extending them with the usage of more complex stimuli as well as deeper network architectures we found that the behavior of ANNs in response to the discrimination-shift task depends on several factors. Most important were the priority with which layers are retrained as well as a clearly separated and complete representation of the stimulus dimensions in the hidden layer. Although rudimentary compared to human brains, the networks of our trials exhibited behavior that is comparable to the ones of adult humans.