  Infants, adults, non-human primates and non-primates all learn patterns
implicitly, and they do so across modalities. The biological evidence supports
the hypothesis that the mechanism for this learning is general but
computationally local. We hypothesize that the mechanism itself is predictive
error-driven learning. We build on recent work that advanced a biologically
plausible model of error backpropagation learning which proposes that higher
order thalamic nuclei provide a locale for a temporal difference between
top-down predictions and an actual event outcome. Our neural network based on
that work also models the auditory cortex hierarchy of core, belt and parabelt
and the caudal-rostral axis within regions. We simulated two studies showing
statistical learning in infants, a seminal study using synthesized speech and a
more recent study using human speech. Before simulating these studies the
network was trained on spoken sentences from the TIMIT corpus to emulate
infant's experience listening to random speech. The implemented neural network,
learning only by predicting the next brief speech segment, learned in both
simulations to predict in-word syllables better than next-word syllables
showing that prediction could be the basis for word segmentation and thus
statistical learning.
