The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation
This work addresses a specific cognitive science problem for researchers studying vocabulary acquisition, but it is incremental as it builds on existing models by adding general cognitive processes.
The study tackled the problem of how presentation timing affects basic-level generalization of novel nouns by extending a computational word learning model to include memory and attention mechanisms. The results showed that the model replicated empirical findings, with forgetting and attention to novelty interacting with exemplar frequencies to explain the reversal of generalization patterns.
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.