Using Rapid Reaching Tasks To Reveal The Underlying Mechanisms Of Decision Making In Humans (P3.030)
Bibliographic record
Abstract
OBJECTIVE: To study the effect of multiple target encodings and initial hand position on visuomotor behaviour during decision making. BACKGROUND: Decision making is a key component of our everyday life. At any given moment, we are faced with several opportunities and are required to select and perform an appropriate action based on the given choices. To facilitate this decision process, it has been suggested that instead of selecting actions and then specifying their parameters, the visuomotor system actually plans multiple actions in parallel, which then compete for execution. Consistent with this, previous experiments in our lab have shown that when subjects are required to rapidly reach toward multiple potential targets before one is cued for action, they show a “middling behaviour”, with the initial trajectory aimed for an averaged location between possible targets. METHODS: We required participants (28 RH subjects, 19 used for analysis) to perform rapid reaches toward multiple potential targets and manipulated the starting position of the hand in relation to the potential targets using a 32 inch NEC LCD touchscreen monitor. A recording system tracked index finger movements via two infrared emitting diodes. RESULTS: In contrast to our predictions, we show that the “middling behaviour” we have observed previously can vary depending on initial hand position. Participant trajectories showed a significant bias toward the right side when either the start position or one of the targets was on the contralateral (left) side of the body. That is, when the hand was required to reach across the body, subjects initiated a trajectory that was biased toward the right target location. However, when the hand reached ipsilaterally, we observed the “middling behavior” that was found in our previous experiment. CONCLUSIONS: Our findings show how the visuomotor competition between targets evolves in three-dimensional space and that the decision to act is affected by the initial body position.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".