Computational models of learning have proved largely successful in characterizing potential mechanisms which allow humans to make decisions in uncertain and volatile contexts. We report here findings that extend existing knowledge and show that a modified reinforcement learning model, which has separate parameters according to whether the previous trial gave a reward or a punishment, can provide the best fit to human behavior in decision making under uncertainty. More specifically, we examined the fit of our modified reinforcement learning model to human behavioral data in a probabilistic two-alternative decision making task with rule reversals. Our results demonstrate that this model predicted human behavior better than a series of other models based on reinforcement learning or Bayesian reasoning. Unlike the Bayesian models, our modified reinforcement learning model does not include any representation of rule switches. When our task is considered purely as a machine learning task, to gain as many rewards as possible without trying to describe human behavior, the performance of modified reinforcement learning and Bayesian methods is similar. Others have used various computational models to describe human behavior in similar tasks, however, we are not aware of any who have compared Bayesian reasoning with reinforcement learning modified to differentiate rewards and punishments.
There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.