Table 1. Evidence Accumulation Models in Decision Making.

Model	Evidence accumulated	Stochastic attention	Decision criterion
Note. The model names are abbreviated as follows: AAM = associative accumulation model (Bhatia, 2013); LCA = leaky competing accumulators (Usher & McClelland, 2004); MADDM = multialternative attentional drift-diffusion model (Krajbich, Armel, & Rangel, 2010; Krajbich & Rangel, 2011); MDFT = multialternative decision field theory (Roe, Busemeyer, & Townsend, 2001); MLBA = multiattribute linear ballistic accumulator (Trueblood, Brown, & Heathcote, 2014); RN = range-normalization model (Soltani, De Martino, & Camerer, 2012); MDbS = multialternative decision by sampling.
AAM	Transformed values on one attended attribute	One attribute is stochastically selected for each step of evidence accumulation	Absolute threshold
LCA	Differences in transformed attribute values, aggregated over attributes	Not assumed	External stopping time
MADDM	Pre-choice attractiveness ratings, weighted by visual attention	One alternative is selected for each step of evidence accumulation	Relative threshold
MDFT	Differences in attribute values between the alternative and the average of the other alternatives, on one attribute	One attribute is stochastically selected for each step of evidence accumulation	Relative threshold
MLBA	Differences in transformed attribute values, aggregated over attributes	Not assumed	Absolute threshold
RN	Transformed attribute values, aggregated over attributes	Not assumed	Not specified
MDbS	Ordinal comparisons between a pair of alternatives on single dimensions	A pair and an attribute are stochastically selected for each step of evidence accumulation	Relative threshold