Figure 1
From: Diffusion model-based understanding of subliminal affective priming in continuous flash suppression
Graphical illustration representing the Wiener diffusion model and the hierarchical diffusion model (HDM). (A) A graphical illustration of the Wiener diffusion model. α = boundary separation indicating the evidence required to make a response; β = initial bias indicating the a priori status of the evidence counter as a proportion of α; δ = average rate of information uptake; τ = time used for everything except making a decision. The picture is drawn based on Vandekerckhove et al.15. (B, C) Plots showing generated reaction times (RT) via the ‘RWiener’ package16 using the parameters shown in each figure. Although different parameters were used to generate the data, the mean reaction times are similar (two-sample t-test; t = 1.02, p = 0.31). (D) Graphical notation of the model used in parameter estimation. We followed the notation method suggested in the book ‘Bayesian Cognitive Modeling’17. The node distinctions between observed versus unobserved variables use shaded and unshaded nodes, and those between stochastic versus deterministic variables use single- and double-bordered nodes. Subscripts and plates indicate repetitions of the parameter across participants p, conditions i, and trials j.
