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arXiv · 1804.05018

Comparatives, Quantifiers, Proportions: A Multi-Task Model for the Learning of Quantities from Vision

Sandro Pezzelle·Ionut-Teodor Sorodoc·Raffaella Bernardi
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Abstract

The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.

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Sandro Pezzelle, Ionut-Teodor Sorodoc, Raffaella Bernardi. 2018-04-13. Comparatives, Quantifiers, Proportions: A Multi-Task Model for the Learning of Quantities from Vision. https://arxiv.org/abs/1804.05018

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