A Computational Model of Number Comparison
Marco Zorzi, Brian Butterworth
Abstract
Number comparison is a task that has been widely used to investigate the mental representation of number magnitudes. It is frequently assumed that the mapping from numerals to a “mental number line” is compressive (i.e., logarithmic) or that magnitude representations have the property of scalar variability. In this study, we simulate the process of selecting the larger of two numbers in a neural network model. We show that it is possible to account for the main experimental effects (e.g., the distance effect and the number size effect) with a simple architecture using a linear representation of numerical magnitudes. The compressive effects that are found in the reaction times emerge from the non-linear interactions that are intrinsic to the decision process.