Neural Models of the Psychosemantics of `Most'
This work addresses the problem of understanding psychosemantic tasks for cognitive scientists and linguists, but it is incremental as it applies existing neural methods to a specific domain.
The paper tackled the problem of modeling how linguistic meanings relate to cognitive tasks by training neural networks on a visual verification task for the quantifier 'most', finding that the models qualitatively mirrored human performance in sensitivity to set size ratios but differed in patterns for image types.
How are the meanings of linguistic expressions related to their use in concrete cognitive tasks? Visual identification tasks show human speakers can exhibit considerable variation in their understanding, representation and verification of certain quantifiers. This paper initiates an investigation into neural models of these psycho-semantic tasks. We trained two types of network -- a convolutional neural network (CNN) model and a recurrent model of visual attention (RAM) -- on the "most" verification task from \citet{Pietroski2009}, manipulating the visual scene and novel notions of task duration. Our results qualitatively mirror certain features of human performance (such as sensitivity to the ratio of set sizes, indicating a reliance on approximate number) while differing in interesting ways (such as exhibiting a subtly different pattern for the effect of image type). We conclude by discussing the prospects for using neural models as cognitive models of this and other psychosemantic tasks.