Lesioning McCloskey and Lindemann’s (1992) Mathnet: The Effect of Damage Location and Amount

Abstract

McCloskey and Lindemann [32] provide a simulation of brain damage on a neural network architecture and offer evidence that different lesions to a same network can lead to different error distributions. We briefly review the various kinds of networks that have been proposed to simulate various arithmetical fact retrieval phenomena and we present a simple network designed to make some computational constraints apparent. Additionally, we replicate McCloskey and Lindemann’s [32] simulation by training 5 different artificial “subjects” and inflicting various types of damage upon each. Examination of the behaviour of our version of the network after different amounts of damage to its various connection blocks confirms that the error pattern may vary. These variations in the error patterns can be analyzed. The data may help to clarify the functioning of the network and give insight into the reasons why it produces several effects observed in human behaviour.

Development of numerical cognition in children and artificial systems: a review of the current knowledge and proposals for multi‐disciplinary research

“…the two arguments of the operation. In a later study, lesioned MATHNET models showed good adherence to neuropsychological data patients with brain damages [85]. However, the success of MATHNET in replicating human data can be entirely attributed to an implausible frequency manipulation [46].…”