Abstract
Background: The Pavlovian go/no-go task is commonly used to measure individual differences in Pavlovian biases and their interaction with instrumental learning. The task has also been widely used in computational psychiatry research, to correlate Pavlovian biases with mental health symptoms. However, prior research has reported unacceptable reliability for computational model-based performance measures for this task, limiting its usefulness in individual-differences research. Here, we apply several strategies previously shown to enhance task-measure reliability (e.g., task gamification, hierarchical Bayesian modeling for model estimation) to the Pavlovian go/no-go task, to improve the reliability of the task as a tool for future research.
Methods: In two experiments, two independent samples of adult participants (N = 103, N = 110) completed a novel, gamified version of the Pavlovian go/no-go task multiple times over several weeks. We used hierarchical Bayesian modeling to derive reinforcement learning model-based indices of participants’ task performance, and to estimate the reliability of these measures.
Results: In Experiment 1, we observed considerable practice effects, with most participants reaching near-ceiling levels of performance with repeat testing. Consequently, the test-retest reliability of some model parameters was unacceptable (as low as 0.379). In Experiment 2, participants completed a modified version of the task designed to lessen these practice effects. We observed greatly reduced practice effects and improved estimates of the test-retest reliability (range: 0.696–0.989).
Conclusion: The results demonstrate that model-based measures of performance on our modified Pavlovian go/no-go task can reach levels of reliability sufficient for use in individual-differences research. We therefore provide the task code for use by the computational psychiatry community (as well as other researchers). Additional investigation is necessary to validate the modified version of the task in other populations and settings.