New Models for Old Questions: Evolutionary Robotics and the 'A Not B' Error

Abstract. In psychology the ‘A not B’ error, whereby infants perseverate in reaching to the location where a toy was previously hidden after it has been moved to a new location, has been the subject of fifty years research since it was first identified by Piaget [1]. This paper describes a novel implementation of the ‘A not B’ error paradigm which is used to test the notion that minimal systems evolutionary robotics modelling can be used to explore developmental process and to generate new hypotheses for test in natural experimental populations. The model demonstrates that agents controlled by plastic continuous time recurrent neural networks can perform the ‘A not B’ task and that homeostatic mediation of plasticity can produce perseverative error patterns similar to those observed in human infants. In addition, the model shows a developmental trend for the production of perseverative errors to reduce during development.

• Type: Conference
• Year: 2007
• Where: ECAL
• Authors: Rachel Wood, Ezequiel A. Di Paolo
• Keywords: Artificial Intelligence, ECAL 2007, Error Paradigm, Perseverative Error Patterns, Perseverative Errors