Evidence accumulation models, such as the diffusion decision
model, allow researchers to estimate a set of parameters
from empirical response times and accuracy data
obtained in binary decision tasks. These parameters can
then be used to quantify individual differences. For the
drift rate parameter (reflecting evidence accumulation
speed), higher parameter estimates seem to be linked to
higher intelligence scores. However, cognitive abilities
such as general intelligence are known to predict
socioeconomic outcomes (e.g., educational attainment, job
prestige, income). If drift rates reflect a type of cognitive
ability, they should also exhibit similar patterns. We thus
studied the associations of evidence accumulation model
parameters with several socioeconomic
outcomes in a very large sample of online implicit
association test data (Project Implicit; N>5,000,000). We
found associations between cognitive process model
parameters and indicators of socioeconomic success
marked by small effect sizes, with some very surprising results regarding drift rate variability. Our results highlight the
utility of big data approaches in the field of cognitive
modeling that have only recently become practically
feasible through novel simulation-based inference
methods.