2 research outputs found
Critical behavior in a cross-situational lexicon learning scenario
The associationist account for early word-learning is based on the
co-occurrence between objects and words. Here we examine the performance of a
simple associative learning algorithm for acquiring the referents of words in a
cross-situational scenario affected by noise produced by out-of-context words.
We find a critical value of the noise parameter above which learning
is impossible. We use finite-size scaling to show that the sharpness of the
transition persists across a region of order about ,
where is the number of learning trials, as well as to obtain the
learning error (scaling function) in the critical region. In addition, we show
that the distribution of durations of periods when the learning error is zero
is a power law with exponent -3/2 at the critical point