Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization
Fig 8
Self-supervised VS forced learning.
We compare the two learning strategies resp. in A and B, in terms of convergence and dynamics. the self-supervising strategy might correspond to a babbling stage in which each audio unit is selected and tested at each cycle in a random fashion. Instead, the forcing strategy makes it possible to control the learning of each unit separately until convergence. In the supervised case (forced STR activity in B), the error is high for one specific STR unit in the beginning and then it is diminishing iteratively over time. We select one by one each STR unit until the error is diminishing to a certain threshold level during a limited amount of time, then the next neuron is selected to optimize the GP vector that optimally triggers the STG categories and the STR units. For the unsupervised case (unsupervised motor babbling in A), as at each iteration a different STR unit is selected because of internal noise, it is not clear to see such gradual decreasing of error for each unit.