Third, differences in the rate of automatization may reflect
the memorability of the stimuli. According to instance theory,
stimuli that are easy to remember will show evidence of automaticity
relatively quickly, whereas stimuli that are hard to remember
will take a long time to show evidence of automaticity. It
may be that the letter arrays studied by Shiffrin, Schneider,
and others are hard to remember. By contrast, the simulations
so far have assumed that each and every encounter with a stimulus
is encoded and retrieved.
The effect of memorability can be modeled by slowing down
the retrieval time or by varying the probability that a stimulus
will be encoded and retrieved. Most likely, memorability has
both effects (Ratcliff, 1978), but it is interesting to consider
them separately. The effects of slowing down retrieval can
be so: The learning rate, as measured by the powerfunction
exponent was slower for the 500-ms memory process than for
the 400-ms memory process when they both raced against the
same algorithm.
The effect of varying retrieval probability is to slow the
rate of learning by reducing the effective number of traces
in the race. Reaction times and standard deviations will still
decrease as a power function of n, but with a smaller exponent,
reflecting a slower rate of learning.
These analyses suggest that there may be no discrepancy
between the rate of automatization predicted by instance theory
and the rate observed in typical studies of automaticity.
It may be possible to observe automatization in a single session,
as was suggested earlier, as long as the number of stimuli
is small and the stimuli themselves are easy to remember (also
see Logan, 1988; Naveh-Benjamin & Jonides, 1984; Smith & Lerner,
1986).
