Failure is unavoidable: The effects of reward, reward-learning and penalty on rapid reaching
Bibliographic record
Abstract
Stimuli that are highly predictive of reward or loss in one context are readily associated with privileged perceptual processing in other contexts (Raymond & O’Brien, Psych Science 2010). Here we study how these associations extend to the dorsal stream’s automatic pilot (Chapman & Gallivan et al, Cognition 2010; Pisella et al, Nature Neuroscience, 2000). In previous work, we have shown that participants forced to begin reaching before they know the final target position show movement trajectories that are biased toward the side of space containing a greater number of targets, up to a limit of about four (Gallivan & Chapman et al, Psych Science, 2011). Using the same task in the current study, participants made rapid reaches toward displays with two differently shaped targets. Each target shape was associated with a specific gain or loss in a preliminary learning task (Experiments 1 & 2) or the shape-specific gain or loss association was acquired during the rapid reach task (Experiments 3 & 4). The design of our experiments allowed us to explore the independent consequences of reward value (gain vs. loss) and reward probability (low vs. high) on the movement trajectories. We show that reaches are automatically biased toward both stimuli with a positive reward-value and stimuli with a high probability of being acted upon. Conversely, we find no evidence that reaches are biased away from (or toward) stimuli with a negative reward-value. This indicates that target selection and inhibition in non-conscious motor planning are not symmetric – the automatic pilot is much faster to select targets (especially those of high value) than it is to inhibit non-targets (even if moving toward them leads to a negative outcome). In essence, the automatic pilot cannot avoid what is not good for it. For that, the more deliberate processes of the frontal lobes are likely involved. Meeting abstract presented at VSS 2012
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".