AVOKE: an open-source web-based experimentation toolbox for evoking audiovisual responses
Bibliographic record
Abstract
As web-based experiments become increasingly popular, the need for accessible, efficient research methods is greater than ever. However, current open-source frameworks sometimes lack detailed documentation, leaving many novice researchers struggling to create their experiments without significant time investments in learning the required technical skills. To meet this demand and further the capabilities of web-based experiments, we propose AVOKE—a diverse set of experimentation plugins and extensions built on top of jsPsych, an open-source JavaScript library for web-based behavioural experiments. AVOKE includes the code and documentation needed for novice researchers to easily integrate a variety of audiovisual stimuli in their experiments. Currently, AVOKE supports temporally-precise presentation of audiovisual stimuli (e.g., external media sources like YouTube, moving objects, etc.), as well as the collection of behavioural responses, like keypresses and video capture (e.g., for recording face videos or participants). All features have been developed according to jsPsych standards and validated through numerous tests developed in Jest—an established open-source JavaScript testing framework. Here, we elaborate on the implementation, data output structure, usage examples, and limitations of the different plugins and extensions comprising AVOKE. We also discuss potential future additions to enhance usability and diversify the feature set of AVOKE. Upon finalization, we hope to integrate AVOKE into the official jsPsych library. Overall, AVOKE fills a gap in existing web-based methods by enabling easy simultaneous presentation and recording of visuals and sound. As an open-source package, we hope for others to contribute to AVOKE as we continue to push the boundaries of web-based audiovisual experiments.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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 source (direct Gemma or distilled Codex), 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".