Title: The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling,
Abstract: Psychiatric neuroscience is increasingly aware of the need to define
psychopathology in terms of abnormal neural computation. The central tool in
this endeavour is the fitting of computational models to behavioural data. The
most prominent example of this procedure is fitting reinforcement learning (RL)
models to decision-making data collected from mentally ill and healthy subject
populations. These models are generative models of the decision-making data
themselves, and the parameters we seek to infer can be psychologically and
neurobiologically meaningful. Currently, the gold standard approach to this
inference procedure involves Monte-Carlo sampling, which is robust but
computationally intensive---rendering additional procedures, such as
cross-validation, impractical. Searching for point estimates of model
parameters using optimization procedures remains a popular and interesting
option. On a novel testbed simulating parameter estimation from a common RL
task, we investigated the effects of smooth vs. boundary constraints on
parameter estimation using interior point and deterministic direct search
algorithms for optimization. Ultimately, we show that the use of boundary
constraints can lead to substantial truncation effects. Our results discourage
the use of boundary constraints for these applications.