Learning about environmental geometry: a flaw in Miller and Shettleworth's (2007) operant model

Many studies have examined how humans and other animals reestablish a sense of direction following disorientation in enclosed environments. Results showing that geometric shape of an enclosure is typically encoded, sometimes to the exclusion of featural cues, have led to suggestions that geometry might be encoded in a dedicated geometric module. Recently, Miller and Shettleworth (2007) proposed that the reorientation task be viewed as an operant task and they presented an associative operant model that appears to account for many empirical findings from reorientation studies. In this paper we show that, although Miller and Shettleworth's insights into the operant nature of the reorientation task may be sound, their mathematical model has a serious flaw. We present simulations to illustrate the implications of the flaw. We also propose that the output of a simple neural network, the perceptron, can be used to conduct operant learning within the reorientation task and can solve the problem in Miller and Shettleworth's model.