The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation

TL;DR

This paper presents a computational model integrating memory and attention to explain how presentation timing affects novel word generalization, aligning with empirical findings and emphasizing cognitive processes in vocabulary learning.

Contribution

It introduces a novel computational model that combines memory and attention mechanisms to replicate effects of exemplar presentation timing on word generalization.

Findings

Model replicates empirical effects of presentation timing on generalization

Forgetting and attention to novelty influence word generalization patterns

Sensitivity to exemplar frequency impacts vocabulary acquisition behaviors

Abstract

People exhibit a tendency to generalize a novel noun to the basic-level in a hierarchical taxonomy -- a cognitively salient category such as "dog" -- with the degree of generalization depending on the number and type of exemplars. Recently, a change in the presentation timing of exemplars has also been shown to have an effect, surprisingly reversing the prior observed pattern of basic-level generalization. We explore the precise mechanisms that could lead to such behavior by extending a computational model of word learning and word generalization to integrate cognitive processes of memory and attention. Our results show that the interaction of forgetting and attention to novelty, as well as sensitivity to both type and token frequencies of exemplars, enables the model to replicate the empirical results from different presentation timings. Our results reinforce the need to incorporate general cognitive processes within word learning models to better understand the range of observed behaviors in vocabulary acquisition.