"How many more times?" Response-effect binding via observational learning requires more than 2 trials
Bibliographic record
Abstract
Repeatedly performing a movement leads to the automatic association and binding together of the underlying response codes leading to the movement and perceptual codes representing the effect of the movement. This response-effect (R-E) association is considered bidirectional in nature such that the activation of one code automatically activates the other. Research has indicated that bidirectional R-E associations are not only acquired through active action experiences, but also through observation. The current research investigated whether R-E associations could be acquired via 2 or 50 observational acquisition trials. Twenty participants completed a pre-test, acquisition phase and a post-test. The pre- and post-test were identical and consisted of 50 trials of a free-choice task in which participants could press either the right or left button following the presentation of a low or high tone. During the acquisition phase, participants observed a model executing right or left button-presses. Each button-press was followed by a specific tone to create the R-E association (high tone followed left button-press and low tone followed a right button-press). Half of the participants completed 50 acquisition trials whereas the other half completed 2 acquisition trials. The learning of R-E associations was evaluated by comparing the number of responses chosen by the participants that were compatible with the preceding tone (e.g., left button press following a high tone) that were executed in the pre- and post-test phases. Analyses revealed a significant interaction between Time (pre, post) and Group (2, 50) for frequency of compatible response choices. Participants who completed 50 acquisition trials demonstrated a preference to interact with the button that was compatible with the effect tone. Participants who completed 2 acquisition trials did not demonstrate this preference. The current study established that 50 trials are sufficient for one to establish observational R-E associations, whereas only 2 observations are not.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".