Choices in a key press decision-making task are more optimal after gaining both aiming and reward experience
Bibliographic record
Abstract
When presented with two different target-penalty configurations of similar maximum expected gain (MEG), participants prefer aiming to configurations with more advantageous spatial, rather than more advantageous gain parameters-perhaps due to the motor system's inherent prioritisation of spatial information during movements with high accuracy demands such as aiming. To test this hypothesis, participants in the present studies chose between target-penalty configurations via key presses to reduce the importance of spatial parameters of the response and performance-related feedback. Configurations varied in spatial (target-penalty region overlap) and gain parameters (negative penalty values) and could have similar or different MEG. Choices were made without prior aiming experience (Experiment 1), after aiming experience provided information of movement variability (Experiment 2), or after aiming experience provided information of movement variability and outcome feedback (Experiment 3). Overall, configurations with advantageous spatial or gain parameters were chosen equally (Both-Similar condition) in all experiments. However, average behaviour at the group level was not reflective of the behaviour of most individual participants with three subgroups emerging: those with a value preference, distance preference, or no preference. In Experiments 1 and 2, these individual differences cannot be explained by MEG differences between configurations or participants' movement variability, but these variables predicted choice behaviour in Experiment 3. Further in the Both-Different condition, participants only selected the larger MEG configuration at a level above chance when both variability and outcome information were given prior to the key press task (Experiment 3). In sum, the data indicate that prioritisation of spatial information did not emerge at the group level when performing key presses and more optimal behaviour emerged when information regarding movement variability and outcome feedback were given.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".