How to Run Behavioural Experiments Online: Best Practice Suggestions for Cognitive Psychology and Neuroscience
Bibliographic record
Abstract
The combination of a replication crisis, global COVID-19 pandemic, 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 a cost-benefit analysis 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 recent online experiment investigating cognitive working memory skills in adults with the learning disorder dyslexia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.354 | 0.529 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.014 | 0.009 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".