E. Alonso

This paper introduces a formal model of associative learning that predicts various phenomena through representational and computational mechanisms, including a double error term and a biologically plausible variable asymptote.

This paper presents a unified model combining associative learning and timing by integrating the Rescorla-Wagner model with a Drift-Diffusion timing model, showing it accounts for 8 out of 10 tested experimental phenomena.