Acquiring robust semantic representations of numbers is crucial for math achievement. However, the learning stage where magnitude becomes automatically elicited by number symbols (i.e., digits from 1 to 9) remains unknown due to the difficulty to measure automatic semantic processing. We used a frequency-tagging EEG paradigm targeting automatic…

We present a neural network model of referent identification in a visual world task. Inputs are visual representations of item pairs unfolding with sequences of phonemes identifying the target item. The model is trained to output the semantic representation of the target and to suppress the distractor. The training set uses a 200-word lexicon…