  Children's tendency to associate novel words with novel referents has been
taken to reflect a bias toward mutual exclusivity. This tendency may be
advantageous both as (1) an ad-hoc referent selection heuristic to single out
referents lacking a label and as (2) an organizing principle of lexical
acquisition. This paper investigates under which circumstances
cross-situational neural models can come to exhibit analogous behavior to
children, focusing on these two possibilities and their interaction. To this
end, we evaluate neural networks' on both symbolic data and, as a first, on
large-scale image data. We find that constraints in both learning and selection
can foster mutual exclusivity, as long as they put words in competition for
lexical meaning. For computational models, these findings clarify the role of
available options for better performance in tasks where mutual exclusivity is
advantageous. For cognitive research, they highlight latent interactions
between word learning, referent selection mechanisms, and the structure of
stimuli of varying complexity: symbolic and visual.

Computation and Language