Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood function is not available. With this work, we propose a novel method for globally amortized Bayesian inference based on invertible neural networks which we call BayesFlow. The method uses simulation to learn a global estimator for the probabilistic mapping from observed data to underlying model parameters. A neural network pre-trained in this way can then, without additional training or optimization, infer full posteriors on arbitrary many real data sets involving the same model family. In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics. Learning summary statistics from data makes the method applicable to modeling scenarios where standard inference techniques with hand-crafted summary statistics fail. We demonstrate the utility of BayesFlow on challenging intractable models from population dynamics, epidemiology, cognitive science and ecology. We argue that BayesFlow provides a general framework for building reusable Bayesian parameter estimation machines for any process model from which data can be simulated.

Web-based data collection is increasingly popular in both experimental and survey-based research, because it is flexible, efficient and location-independent. While dedicated software for laboratory-based experimentation and online surveys is commonplace, researchers looking to implement experiments in the browser have, heretofore, often had to manually construct their studies’ content and logic using code. We introduce lab.js, a free, open-source experiment builder that makes it easy to build experiments for both online and in-laboratory data collection. Through its visual interface, stimuli can be designed and combined into a study without programming, though studies’ appearance and behavior can be fully customized using HTML, CSS and JavaScript code if required. Presentation and response times are kept and measured with high accuracy and precision heretofore unmatched in browser-based studies. Experiments constructed with lab.js can be run directly on a local computer, and published online with ease, with direct deployment to cloud hosting, export to any web server, and integration with popular data collection tools. Studies can also be shared in an editable format, archived, re-used and adapted, enabling effortless, transparent replications, and thus facilitating open, cumulative science. The software is provided free of charge under an open-source license; further information, code and extensive documentation are available from https://lab.js.org/.

One of the most prominent response-time models in cognitive psychology is the diffusion model, which assumes that decision-making is based on a continuous evidence accumulation described by a Wiener diffusion process. In the present paper, we examine two basic assumptions of standard diffusion model analyses. Firstly, we address the question of whether participants adjust their decision thresholds during the decision process. Secondly, we investigate whether so-called Lévy-flights that allow for random jumps in the decision process account better for experimental data than do diffusion models. Specifically, we compare the fit of six different versions of accumulator models to data from four conditions of a number-letter classification task. The experiment comprised a simple single-stimulus task and a more difficult multiple-stimulus task that were both administered under speed versus accuracy conditions. Across the four experimental conditions, we found little evidence for a collapsing of decision boundaries. However, our results suggest that the Lévy-flight model with heavy-tailed noise distributions (i.e., allowing for jumps in the accumulation process) fits data better than the Wiener diffusion model.

Within-person couplings play a prominent role in psychological research and previous studies have shown that inter-individual differences in within-person couplings predict future behavior. For example, stress reactivity -operationalized as the within-person coupling of stress and positive or negative affect -is an important predictor of various (mental) health outcomes and has often been assumed to be a more or less stable personality trait. However, issues of reliability of these couplings have been largely neglected so far. In this work, we present an estimate for the reliability of within-person couplings that can be easily obtained using the user-modifiable R code accompanying this work. Results of a simulation study show that this index performs well even in the context of unbalanced data due to missing values. We demonstrate the application of this index in a measurement burst study targeting the reliability and test-retest correlation of stress reactivity estimates operationalized as within-person couplings. Reliability and test-retest correlations of stress reactivity estimates were rather low, challenging the implicit assumption of stress reactivity as a stable personlevel variable. We highlight key factors that researchers planning studies targeting interindividual differences in within-person couplings should consider to maximize reliability.

Web-based data collection is increasingly popular in both experimental and survey-based research because it is flexible, efficient, and location-independent. While dedicated software for laboratory-based experimentation and online surveys is commonplace, researchers looking to implement experiments in the browser have, heretofore, often had to manually construct their studies’ content and logic using code. We introduce , a free, open-source experiment builder that makes it easy to build studies for both online and in-laboratory data collection. Through its visual interface, stimuli can be designed and combined into a study without programming, though studies’ appearance and behavior can be fully customized using html, css, and JavaScript code if required. Presentation and response times are kept and measured with high accuracy and precision heretofore unmatched in browser-based studies. Experiments constructed with can be run directly on a local computer and published online with ease, with direct deployment to cloud hosting, export to web servers, and integration with popular data collection platforms. Studies can also be shared in an editable format, archived, re-used and adapted, enabling effortless, transparent replications, and thus facilitating open, cumulative science. The software is provided free of charge under an open-source license; further information, code, and extensive documentation are available from https://lab.js.org/.