Abstract

The role of information uncertainty has wide implications ranging from emotional modulation to optimal decision making. Yet the concept has been employed as an ad-hoc explanation for various phenomena. One useful approach to the problem is to use a formal computational model to test different parameters extracted from animal and human studies on stimulus uncertainty. We developed an integrated simulation environment written in Matlab (Korea University Conditioning Simulator: KUCS) which provides graphical user interface for several influential models of associative learning such as Rescorla-Wagner model, Mackintosh model, Pearce and Hall model, Schmajuk-Pearce-Hall model, Esber-Hasselgrove model, and Temporal Difference model. Using KUCS, We first demonstrated common predictions on basic conditioning phenomena: acquisition, extinction, blocking, conditioned inhibition, latent inhibition, and second-order conditioning to confirm the validity of the simulator and to find some novel limitations and predictions. We then generated a series of data under uncertainty and compared them with animal and human experiments to examine how the models’ predictions on the associative strength and associability concur with the experimental data. The simulator program is available in https://github.com/knowblesse/KUCS.