No abstract

Drift diffusion models (DDM) are widely used to investigate decision-making processes in psychology, behavioral economics, neuroscience, and psychiatry. As one of the most cited software packages, HDDM (Hierarchical Bayesian estimation of DDMs), a python library, has been useful in helping researchers with minimal coding experience fit DDMs and other sequential sampling models to their experimental data. Despite the popularity of HDDM, its compatibility issues during installation and the lack of advanced Bayesian modeling functionalities, unfortunately, hamper its further applications in research practices. To circumvent these challenges, we integrated Bayesian modeling Python package ArviZ into HDDM and encapsulated them into a virtualized ready-to-use package in Docker, called dockerHDDM. Augmented by ArviZ, dockerHDDM provides richer data analysis functions and data visualization tools. This tutorial provides a hands-on guide on how to use dockerHDDM to efficiently conduct Bayesian hierarchical analysis of DDMs and is expected to facilitate the implementation, analysis, and reproducibility of DDMs. The workflow showcased here can be further generalized into broader applications of Bayesian data analysis.

Open Science is becoming a mainstream scientific ideology in psychology and related fields. However, researchers, especially early-career researchers (ECRs) in developing countries, are facing significant hurdles in engaging in Open Science and moving it forward. In China, various societal and cultural factors discourage ECRs from participating in Open Science, such as the lack of dedicated communication channels and the norm of modesty. To make the voice of Open Science heard by Chinese-speaking ECRs and scholars at large, the Chinese Open Science Network (COSN) was initiated in 2016. With its core values being grassroots-oriented, diversity, and inclusivity, COSN has grown from a small Open Science interest group to a recognized network both in the Chinese-speaking research community and the international Open Science community. So far, COSN has organized three in-person workshops, 12 tutorials, 48 talks, and 55 journal club sessions and translated 15 Open Science-related articles and blogs from English to Chinese. Currently, the main social media account of COSN (i.e., the WeChat Official Account) has more than 23,000 subscribers, and more than 1,000 researchers/students actively participate in the discussions on Open Science. In this article, we share our experience in building such a network to encourage ECRs in developing countries to start their own Open Science initiatives and engage in the global Open Science movement. We foresee great collaborative efforts of COSN together with all other local and international networks to further accelerate the Open Science movement.

Previous studies have identified that enhanced learning rate is a key mechanism for adapting to fast-changing reward environments with high volatility, and that impairments in flexible learning rate may be associated with several psychiatric conditions. However, these studies have mostly assumed a single strategy manifested in the human probabilistic learning process. In contrast, we propose an alternative perspective and develop a hybrid mixture-of-strategy (MOS) model that incorporates three strategies: the expected utility strategy, which aims to maximize rewards; the magnitude-oriented strategy, which only considers reward magnitude; and the habitual strategy, which tends to repeat previous decisions. While the expected utility strategy is statistically optimal, the magnitude-oriented and habitual strategies are suboptimal but computationally simpler. In an open dataset where healthy controls and patients with anxiety and depression performed a probabilistic reversal learning task with distinct volatility conditions, our MOS model outperforms previous best-fitting models. Parameter analyses suggest that individuals with anxiety and depression exhibit weaker preferences for the optimal expected utility strategy but stronger preferences for the magnitude-oriented heuristic. The relative strength of these two strategies also predicts individual variation in symptom severity. Importantly, our MOS model explains the slower learning in patients by the different weighting of the different strategies rather than learning rate per se. These findings underscore the complexity of human learning and decision-making and suggest the possibility of mixed strategies in learning and decision-making. Further research is necessary to distinguish the flexible-learning-rate account from the mixture-of-strategy account.