Author Summary
Acquisition of responses towards full predictors of rewards,
namely Pavlovian conditioning, has long been explained
using the reinforcement learning theory. This theory
formalizes learning processes that, by attributing values
to situations and actions, makes it possible to direct
behaviours towards rewarding objectives. Interestingly,
the implied mechanisms rely on a reinforcement signal
that parallels the activity of dopamine neurons in such
experiments. However, recent studies challenged the
classical view of explaining Pavlovian conditioning with a
single process. When presented with a lever whose
retraction preceded the delivery of food, some rats started
to chew and bite the food magazine whereas others chew
and bite the lever, even if no interactions were necessary
to get the food. These differences were also visible in brain
activity and when tested with drugs, suggesting the
coexistence of multiple systems. We present a computational
model that extends the classical theory to account
for these data. Interestingly, we can draw predictions from
this model that may be experimentally verified. Inspired by
mechanisms used to model instrumental behaviours,
where actions are required to get rewards, and advanced
Pavlovian behaviours (such as overexpectation, negative
patterning), it offers an entry point to start modelling the
strong interactions observed between them.