How to Run Behavioural Experiments Online: Best Practice Suggestions for Cognitive Psychology and Neuroscience
Bibliographic record
Abstract
The combination of a replication crisis, the global COVID-19 pandemic in 2020, and recent technological advances, have accelerated the on-going transition of research in cognitive psychology and neuroscience to the online realm. When participants cannot be tested in-person, data of acceptable quality can still be collected online. While online research offers many advantages, numerous pitfalls may hinder researchers in addressing their questions appropriately, potentially resulting in unusable data and misleading conclusions. Here, we present an overview of the costs and benefits of conducting online studies in cognitive psychology and neuroscience, coupled with detailed best practice suggestions that span the range from initial study design to the final interpretation of data. These suggestions offer a critical look at issues regarding recruitment of typical and (sub)clinical samples, their comparison, and the importance of context-dependency in each part of a study. We illustrate our suggestions by means of a fictional online study, applicable to traditional paradigms such as research on working memory with a control and treatment group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".