Inter-trial effects in visual pop-out search: Factorial comparison of Bayesian updating models
Fig 1
Illustrations of the drift diffusion model (DDM, shown in blue) and the LATER model (shown in red).
The DDM assumes that evidence accumulates, from the starting point (S0), through random diffusion in combination with a drift rate r until a boundary (i.e., threshold, θ) is reached. The LATER model makes the same assumptions, except that the rate r is considered to be constant within any individual trial, but to vary across trials (so as to explain trial-to-trial variability in RTs).