Toward Generalizable Estimation of Behavioral Models with Parameter Dependencies.

Behavioral models are instrumental for studying human cognition, yet many inferences derived from such models fail to generalize. We argue that this is driven in part by the increasing complexity of behavioral models, where non-linearities and discontinuities create dynamic parameter interactions that limit the generalizability of inferences across different contexts, experiments, and datasets. We first demonstrate the problems that arise from parameter dependency. We then propose a new methodological framework for understanding the generalizability of behavioral modeling results using multivariate sampling distributions for the model parameters. We derive and validate novel sampling distributions for complex non-linear behavioral models by transforming the mimicry between different parameter values into the chances of one set of parameters being inferred from data generated by another set of parameters. Our approach is computationally scalable to evaluate how model estimates change across the parameter space and different experiments, which can limit the generalizability of experimental results. We then apply our approach to current behavioral models, revealing new theoretical insights. Using our approach, we reinterpret results from recent modeling work in decision-making and category learning. We conclude by discussing the implications of our proposed framework for building stronger, more generalizable psychological research and theory through behavioral modeling.