Bibliographic record
Abstract
When programming complex experiments (for example, involving the generation of stimuli online), the traditional experiment programming software are not well equipped.One solution is to give up entirely the use of such software in favor of a lowlevel programming language.Here we show how E-Prime can be connected to Mathematica so that the easiness and reliability of this software can be preserved while at the same time granting it the full computational power of a high-level programming language.As an example, we show how to generate noisy images with noise proportional to the rate of success of the participants with as few as 12 lines of codes in E-Prime.Psychology experiments can be rather simple, being composed of a fixed number of trials, presenting a fixed set of stimuli and collecting a fixed set of responses.For such situations, many software exists that can program the experiment rapidly and easily (such as Superlab, ERTS, InQuisit, E-Prime, DirectRT, to name a few, Stahl, 2006).However, more sophisticated experiments are sometimes required which can for example (i) continue training until a performance criterion is reached, (ii) generate random stimuli, (iii) alter stimulus differently to adapt to the participant, (iv) interpret the participant's response and continue the experiment accordingly, etc.The possibilities are endless and we are only enumerating a few.All these possibilities can be implemented as long as a programming language is available.However, (a) very few experiment programming software offer the possibility to include lines of code within the experiment, (b) when they do, it is often a
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.021 |
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".