ABSTRACT
When people read, they classify a relatively long string of characters in parallel. Machine learning principles predict that classification learning with such high dimensioned inputs and outputs will fail unless biases are imposed to reduce input and output variability and/or the number of candidate input/output mapping functions evaluated during learning. The present paper draws insight from observed reading behaviors to propose some potential sources of such biases, and demonstrates, through neural network simulations of letter-sequence classification learning that: (1) Increasing dimensionality does hinder letter classification learning and (2) the proposed sources of bias do reduce dimensionality problems. The result is a model that explains word superiority and word frequency effects, as well as consistencies in eye fixation positions during reading, solely in terms of letter classification learning.
