PLoS Comput BiolplosploscompPLOS Computational Biology1553-734X1553-7358Public Library of ScienceSan Francisco, CA USA10.1371/journal.pcbi.1005829PCOMPBIOL-D-17-01745CorrectionCorrection: A Unifying Probabilistic View of Associative LearningThe PLOS Computational Biology Staff161120171120171311e10058292017The PLOS Computational Biology StaffThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.A Unifying Probabilistic View of Associative Learning

There is an error in equation 15, the “discounted time derivative” h_{t} is defined incorrectly. It should read as follows:

“Operationally, the only change from the Kalman filter model described above is to replace the stimulus features x_{t} with their discounted time derivative, h_{t} = x_{t} - γx_{t+1}. To see why this makes sense, note that the immediate reward can be expressed in terms of the difference between two values:
rt=V(xt)−γV(xt+1)=wtxt−γwtxt+1=wt(xt−γxt+1).

This error does not affect the simulations, which were implemented with the correct definition.

ReferenceGershmanSJ (2015) A Unifying Probabilistic View of Associative Learning.