Tense/aspect morphology on verbs is often thought to depend on event features like telicity, but it is not known how speakers identify these features in visual scenes. To examine this question, we asked Japanese speakers to describe computer‐generated animations of simple actions with variation in visual features related to telicity. Experiments with adults and children found that they could use goal information in the animations to select appropriate past and progressive verb forms. They also produced a large number of different (...) verb forms. To explain these findings, a deep‐learning model of verb production from visual input was created that could produce a human‐like distribution of verb forms. It was able to use visual cues to select appropriate tense/aspect morphology. The model predicted that video duration would be related to verb complexity, and past tense production would increase when it received the endpoint as input. These predictions were confirmed in a third study with Japanese adults. This work suggests that verb production could be tightly linked to visual heuristics that support the understanding of events. (shrink)

According to a recent proposal, natural concepts are represented in an optimally designed similarity space, adhering to principles a skilled engineer would use for creatures with our perceptual and cognitive capacities. One key principle is that natural concepts should be easily learnable. While evidence exists for parts of this optimal design proposal, there has been no direct evidence linking naturalness to learning until now. This article presents results from a computational study on perceptual color space, demonstrating that naturalness indeed facilitates (...) learning. (shrink)

This paper studies the learnability of natural concepts in the context of the conceptual spaces framework. Previous work proposed that natural concepts are represented by the cells of optimally partitioned similarity spaces, where optimality was defined in terms of a number of constraints. Among these is the constraint that optimally partitioned similarity spaces result in easily learnable concepts. While there is evidence that systems of concepts generally regarded as natural satisfy a number of the proposed optimality constraints, the connection between (...) naturalness and learnability has been less well studied. To fill this gap, we conduct a computational study employing two standard models of concept learning. Applying these models to the learning of color concepts, we examine whether natural color concepts are more readily learned than nonnatural ones. Our findings warrant a positive answer to this question for both models employed, thus lending empirical support to the notion that learnability is a distinctive characteristic of natural concepts. (shrink)