Separating value from selection frequency in rapid reaching biases to visual targets
Bibliographic record
Abstract
Stimuli associated with positive rewards in one task often receive preferential processing in a subsequent task, even when those associations are no longer relevant. Here we use a rapid reaching task to investigate these biases. In Experiment 1 we first replicated the learning procedure of Raymond and O'Brien (2009), for a set of arbitrary shapes that varied in value (positive, negative) and probability (20%, 80%). In a subsequent task, participants rapidly reached toward one of two shapes, except now the previously learned associations were irrelevant. As in the previous studies, we found significant reach biases toward shapes previously associated with a high probable, positive outcome. Unexpectedly, we also found a bias toward shapes previously associated with a low probable, negative outcome. Closer inspection of the learning task revealed a potential second factor that might account for these results; since a low probable negative shape was always paired with a high probable negative shape, it was selected with disproportionate frequency. To assess how selection frequency and reward value might both contribute to reaching biases we performed a second experiment. The results of this experiment at a group level replicated the reach-bias toward positively rewarding stimuli, but also revealed a separate bias toward stimuli that had been more frequently selected. At the level of individual participants, we observed a variety of preference profiles, with some participants biased primarily by reward value, others by frequency, and a few actually biased away from both highly rewarding and high frequency targets. These findings highlight that: (1) rapid reaching provides a sensitive readout of preferential processing; (2) target reward value and target selection frequency are separate sources of bias; and (3) group-level analyses in complex decision-making tasks can obscure important and varied individual differences in preference profiles.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".