Abstract

A connectionist system that is capable of learning about the spatial structure of a simple world is used for the purposes of synthetic epistemology: the creation and analysis of artificial systems in order to clarify philosophical issues that arise in the explanation of how agents, both natural and artificial, represent the world. In this case, the issues to be clarified focus on the content of representational states that exist prior to a fully objective understanding of a spatial domain. In particular, the criticisms of (Chrisley, 1993) that were raised in (Holland, 1994) are addressed: how can we determine that a system’s spatial representations are more objective than before? And under what conditions (tasks, training regimes, environments) do such increases in objectivity occur? After analysing the results of experiments that attempt to shed light on these questions, the study concludes by comparing and contrasting this work with related research.