Action Without Awareness: Reaching to an Object You Do Not Remember Seeing
Bibliographic record
Abstract
BACKGROUND: Previous work by our group has shown that the scaling of reach trajectories to target size is independent of obligatory awareness of that target property and that "action without awareness" can persist for up to 2000 ms of visual delay. In the present investigation we sought to determine if the ability to scale reaching trajectories to target size following a delay is related to the pre-computing of movement parameters during initial stimulus presentation or the maintenance of a sensory (i.e., visual) representation for on-demand response parameterization. METHODOLOGY/PRINCIPAL FINDINGS: Participants completed immediate or delayed (i.e., 2000 ms) perceptual reports and reaching responses to different sized targets under non-masked and masked target conditions. For the reaching task, the limb associated with a trial (i.e., left or right) was not specified until the time of response cuing: a manipulation that prevented participants from pre-computing the effector-related parameters of their response. In terms of the immediate and delayed perceptual tasks, target size was accurately reported during non-masked trials; however, for masked trials only a chance level of accuracy was observed. For the immediate and delayed reaching tasks, movement time as well as other temporal kinematic measures (e.g., times to peak acceleration, velocity and deceleration) increased in relation to decreasing target size across non-masked and masked trials. CONCLUSIONS/SIGNIFICANCE: Our results demonstrate that speed-accuracy relations were observed regardless of whether participants were aware (i.e., non-masked trials) or unaware (i.e., masked trials) of target size. Moreover, the equivalent scaling of immediate and delayed reaches during masked trials indicates that a persistent sensory-based representation supports the unconscious and metrical scaling of memory-guided reaching.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".