Difference vs. Ratio of evidence in sequential sampling models In sequential sampling models - for instance Ratcliff and Smith, (2006) - participants' responses in a binary choice experiment are modelled by a particle, which moves up or down towards the boundaries for selecting each response over time according to the evidence in favour of each, or, analogously, their expected utility (Busemeyer &amp; Townsend, 1993), in a way that looks something like this:



My question is if anyone knows, and preferably can provide a reference for, whether in such a model responses are best predicted by


the difference in the evidence for/expected utility of each response (i.e. $P(Response\ A) = Evidence\ for\ A - Evidence\ for\ B$) or
the ratio of evidence/expected utility (i.e. $P(Response\ A) = \frac{Evidence\ for\ A}{Evidence\ for\ B}$)


My intuition is that it's the ratio between the two responses, rather than the absolute difference, which should best predict responses, but I can't find a reference for this, and I'm sure this question has been answered somewhere before.

Has anyone any ideas here?
Unless I'm misunderstanding what you mean, ratio of evidence is actually a terrible method because that would end up with an immediate decision as soon as any evidence is encountered for either side, giving a ratio of infinity for that side (something/0 is greater than any finite decision threshold).

Try Gold and Shadlen 2007 for a review http://synapse.princeton.edu/~sam/gold_shadlen07_annu_rev_neurosci.pdf