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Psychiatric neuroscience is increasingly aware of the need to definepsychopathology in terms of abnormal neural computation. The central tool inthis endeavour is the fitting of computational models to behavioural data. Themost prominent example of this procedure is fitting reinforcement learning (RL)models to decision-making data collected from mentally ill and healthy subjectpopulations. These models are generative models of the decision-making datathemselves, and the parameters we seek to infer can be psychologically andneurobiologically meaningful. Currently, the gold standard approach to thisinference procedure involves Monte-Carlo sampling, which is robust butcomputationally intensive---rendering additional procedures, such ascross-validation, impractical. Searching for point estimates of modelparameters using optimization procedures remains a popular and interestingoption. On a novel testbed simulating parameter estimation from a common RLtask, we investigated the effects of smooth vs. boundary constraints onparameter estimation using interior point and deterministic direct searchalgorithms for optimization. Ultimately, we show that the use of boundaryconstraints can lead to substantial truncation effects. Our results discouragethe use of boundary constraints for these applications.

The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling