  In two-alternative forced choice tasks, prior knowledge can improve
performance, especially when operating near the psychophysical threshold. For
instance, if subjects know that one choice is much more likely than the other,
they can make that choice when evidence is weak. A common hypothesis for these
kinds of tasks is that the prior is stored in neural activity. Here we propose
a different hypothesis: the prior is stored in synaptic strengths. We study the
International Brain Laboratory task, in which a grating appears on either the
right or left side of a screen, and a mouse has to move a wheel to bring the
grating to the center. The grating is often low in contrast which makes the
task relatively difficult, and the prior probability that the grating appears
on the right is either 80% or 20%, in (unsignaled) blocks of about 50 trials.
We model this as a reinforcement learning task, using a feedforward neural
network to map states to actions, and adjust the weights of the network to
maximize reward, learning via policy gradient. Our model uses an internal state
that stores an estimate of the grating and confidence, and follows Bayesian
updates, and can switch between engaged and disengaged states to mimic animal
behavior. This model reproduces the main experimental finding - that the
psychometric curve with respect to contrast shifts after a block switch in
about 10 trials. Also, as seen in the experiments, in our model the difference
in neuronal activity in the right and left blocks is small - it is virtually
impossible to decode block structure from activity on single trials if noise is
about 2%. The hypothesis that priors are stored in weights is difficult to
test, but the technology to do so should be available in the not so distant
future.
