# A Neural Network Architecture for Learning Word-Referent Associations in Multiple Contexts

## TL;DR

This paper introduces a biologically inspired neural network architecture that learns word-referent associations across multiple contexts, effectively handling ambiguity and mimicking human learning patterns.

## Contribution

It presents a novel multi-layered neurocomputational model utilizing Self-Organizing Maps to incrementally learn and adapt associations in varying contexts, inspired by psycholinguistic and neurolinguistic evidence.

## Key findings

- Achieves up to 78% accuracy in ambiguous situations
- Replicates human learning rates in cross-situational experiments
- Displays similar learning patterns across different conditions

## Abstract

This article proposes a biologically inspired neurocomputational architecture which learns associations between words and referents in different contexts, considering evidence collected from the literature of Psycholinguistics and Neurolinguistics. The multi-layered architecture takes as input raw images of objects (referents) and streams of word's phonemes (labels), builds an adequate representation, recognizes the current context, and associates label with referents incrementally, by employing a Self-Organizing Map which creates new association nodes (prototypes) as required, adjusts the existing prototypes to better represent the input stimuli and removes prototypes that become obsolete/unused. The model takes into account the current context to retrieve the correct meaning of words with multiple meanings. Simulations show that the model can reach up to 78% of word-referent association accuracy in ambiguous situations and approximates well the learning rates of humans as reported by three different authors in five Cross-Situational Word Learning experiments, also displaying similar learning patterns in the different learning conditions.