Recent results show that 18 months old infants are sensitive to taxonomic relations and that, similarly to adults, these relations are modulated by Inter Stimulus Interval. A very influential proposal in the distributed representations literature explains the impact of ISI on semantic priming as the result of a phenomenon called hysteresis. Here we propose that the same results could also be explained by the opposite phenomenon of adaptation. The existence of two possible explanations calls for more experiments to understand if hysteresis or adaptation can explain the role of ISI on semantic priming.

We propose a neural network model that accounts for the emer-gence of the taxonomic constraint in early word learning. Ourproposal is based on Mayor and Plunkett (2010)’s neurocom-putational model of the taxonomic constraint and overcomesone of its limitations, namely the fact that it considers arti-ficially built, simplified stimuli. In fact, while in the originalmodel the visual stimuli are random, sparse dot patterns, in ourproposed solution they are photographic images from the Im-ageNet database. In our model the represented objects in theimage can be of different size, color, location in the picture,point of view, etc.. We show that, notwithstanding the aug-mented complexity in the input, the proposed model comparesfavorably with respect to Mayor and Plunkett (2010)’s model.

We propose a neural network model that accounts for the emer-gence of the taxonomic constraint and for the whole objectconstraint in early word learning. Our proposal is based onMayor and Plunkett (2010)’s neurocomputational model of thetaxonomic constraint and extends it in two directions. Firstly,we deal with realistic visual and acoustic stimuli. Secondly,we model the well-known whole object constraint in the visualcomponent. We show that, despite the augmented input com-plexity, the proposed model compares favorably with respectto previous systems.