Switching to online: Testing the validity of supervised remote testing for online reinforcement learning experiments.

Online experiments are an alternative for researchers interested in conducting behavioral research outside the laboratory. However, an online assessment might become a challenge when long and complex experiments need to be conducted in a specific order or with supervision from a researcher. The aim of this study was to test the computational validity and the feasibility of a remote and synchronous reinforcement learning (RL) experiment conducted during the social-distancing measures imposed by the pandemic. An additional feature of this study was to describe how a behavioral experiment originally created to be conducted in-person was transformed into an online supervised remote experiment. Open-source software was used to collect data, conduct statistical analysis, and do computational modeling. Python codes were created to replicate computational models that simulate the effect of working memory (WM) load over RL performance. Our behavioral results indicated that we were able to replicate remotely and with a modified behavioral task the effects of working memory (WM) load over RL performance observed in previous studies with in-person assessments. Our computational analyses using Python code also captured the effects of WM load over RL as expected, which suggests that the algorithms and optimization methods were reliable in their ability to reproduce behavior. The behavioral and computational validation shown in this study and the detailed description of the supervised remote testing may be useful for researchers interested in conducting long and complex experiments online.

Statistical process control charts for better analysis of sequential data in psychology: The case of illusion of control experiments.

Illusion of control (IOC) is a bias in the judgment of personal success with implications to learning theories and health policies; some important questions in the investigation of IOC may be related to traditional measures in the field, namely self-assessment using Likert scales about the sense of control. Statistical process control (SPC) and Shewhart charts are methods developed to monitor and control industrial processes, never applied in psychological studies before. The present two studies investigated the use of the technique of Shewhart charts in the analysis of IOC. The purpose was to explore the use of SPC and Shewhart charts in the analysis of data sequences from psychological experiments; the objective was to analyze the results of reaction time (RT) data sequences plotted in SPC charts, in comparison with self-assessment judgments from an IOC task. Participants were 63 undergraduate students (Study 1) and 103 mine workers (Study 2) instructed to try to control a traffic light on a computer by pressing or not the keyboard. Higher probabilities of the successful outcome generated judgments of illusion and shifts (due to cognitive activity) in the charts of RT; lower probabilities resulted in null illusion and RT presented a random and stable profile. Patterns for different groups emerged in Shewhart charts. SPC can contribute to the analysis of the behavior of sequences of data in psychological studies, so that the charts indicate changes and patterns not detected by traditional ANOVA and other linear models.