Reinforcement and inference in cross-situational word learning

TL;DR

This paper introduces an adaptive model for cross-situational word learning that balances reinforcement and inference, explaining diverse experimental learning strategies through adjustable parameters.

Contribution

It presents a novel adaptive algorithm with tunable parameters for reinforcement and inference, aligning model predictions with experimental data and elucidating different learning strategies.

Findings

Inference dominates in fast mapping scenarios.

Reinforcement is key in segregated contextual diversity.

Balanced strategies emerge in mixed experimental conditions.

Abstract

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.