Rule Learning by Seven-Month-Old Infants and Neural Networks

7-month-old infants with sequences of syllables generated by an artificial grammar; the infants were then able to discriminate between sequences generated both by that grammar and another, even though sequences in the familiarization and test phases employed different syllables. Marcus et al. stated that their infants were representing, extracting, and generalizing abstract algebraic rules. This conclusion was motivated also by their statement that the infants ’ discrimination could not be performed by a popular class of simple neural network model. Marcus et al. make a number of statements regarding the supposed inability of statistical learning mechanisms, including neural networks, to account for their data. One model they describe (2) was developed, however, to model precisely the kinds of abstract generalizations exhibited by the infants in the report. Marcus et al. state that this model cannot account for their data because, unlike the infants, it relies on being supplied with attested examples of sentences that are acceptable in the artificial language used during the test phase. This is not the case. Our model used a simple recurrent network (3) with an extra encoding layer between the input and hidden layers of nodes. During training, the network was presented with grammatical sequences of syllables (each input node corresponded to a particular syllable). The network’s task was to predict the next syllable. Weights on connections between the nodes were adjusted with the use of back-propagation. At test, the weights on connections to the hidden layer were frozen (simulating an adaptive learning procedure). Both grammatical and ungrammatical sequences of hitherto unseen syllables were then presented to the input layer, using input nodes that had not been used for training. The network’s classification of these sequences [determined by an equivalent of the Luce choice rule (4)] did not differ from human participants’ above-chance classification of the same stimuli. This was achieved, contrary to the description given by Marcus et al., without pre-test exposure to any test sequences, without feedback at test on which sequences were grammatical and which ungrammatical, and when the input nodes corresponded to individual words in the language (contrary to a further statement in their repor