A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used

Conditioned inhibition.

<p>Left column: simulation (top) and data (bottom) from conditioned inhibition with a long inhibitor. Right column: simulation (top) and data (bottom) from conditioned inhibition with a short inhibitor. Data plots redrawn from Figure 4 in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005796#pcbi.1005796.ref092" target="_blank">92</a>]. Model parameters: <i>α</i>_<i>t</i> = 0.09, <i>α</i>_<i>V</i> = 0.06, <i>μ</i> = 1, <i>σ</i> = 0.35, <i>m</i> = 0.16, <i>H</i> = 30.</p