A Cognitive Robotics Model for Contextual Diversity in Language Learning

The number of contexts in which a word is encountered, or contextual diversity, has been shown to be a relevant predictor of word-naming and lexical decision times. In this work we present an end-to-end scenario in which we collect data with a humanoid robot in three different contextual diversity levels, use the data to train a cognitive architecture with the objective of mirroring the same phenomenon observed in the literature, and ultimately we test the model by collecting test data with the robot and matching them with the learned word-object mappings. Results show that the approach manages to capture and describe successfully a computational representation of the impact of contextual diversity on word-object mapping, showing how with greater contextual diversity the mapping is more precise compared to the cases with lower diversity.