  How stable and general is behavior once maximum learning is reached? To
answer this question and understand post-acquisition behavior and its related
individual differences, we propose a psychological principle that naturally
extends associative models of Pavlovian conditioning to a dynamical oscillatory
model where subjects have a greater memory capacity than usually postulated,
but with greater forecast uncertainty. This results in a greater resistance to
learning in the first few sessions followed by an over-optimal response peak
and a sequence of progressively damped response oscillations. We detected the
first peak and trough of the new learning curve in our data, but their
dispersion was too large to also check the presence of oscillations with
smaller amplitude. We ran an unusually long experiment with 32 rats over 3960
trials, where we excluded habituation and other well-known phenomena as sources
of variability in the subjects' performance. Using the data of this and another
Pavlovian experiment by Harris et al. (2015), as an illustration of the
principle we tested the theory against the basic associative single-cue
Rescorla-Wagner (RW) model. We found evidence that the RW model is the best
nonlinear regression to data only for a minority of the subjects, while its
dynamical extension can explain the almost totality of data with strong to very
strong evidence. Finally, an analysis of short-scale fluctuations of individual
responses showed that they are described by random white noise, in contrast
with the colored-noise findings in human performance.
