Categorisation through internal simulation of perception and behaviour

Abstract

The `simulation hypothesis' is an intriguing explanation for cognition, and holds that `thinking consists of simulated interaction with the environment' ([4], p.242). However, the neuroscientific proof for a simulation mechanism in the brain is indirect. In this paper we present a minimal-model approach to investigate the `simulation hypothesis'. Our minimal model is called ACP? and is an extension of the Active Categorical Perception model (ACP) presented in [8]. In ACP?, robots have a neurocontroller with an output-input feedback mechanism that allows them to simulate perception and behaviour internally. Our experiments focus on the performance of robots with three different types of neurocontroller (two feedforward and one recurrent type of neurocontroller). Their performance is compared over three experimenta conditions in which the output-input feedback mechanism is functional for variable durations. The results show that feedforward-neurocontrolled robots benefit from output-input feedback, while recurrent-neurocontrolled robots do not. Based on these results, two closely related conclusions are drawn: (1) the `simulation hypothesis' may be too specific, and (2) predicting future perception may depend on neural recurrency (i.e., internal feedback) in in general, rather than on the ability to simulate perception by feeding back actions.