Editorial: Perceiving and Acting in the Real World: From Neural Activity to Behavior
Bibliographic record
Abstract
The interaction between perception and action represents one of the pillars of human evolutionary success. Our interactions with the surrounding world involve a variety of behaviors, almost always including movements of the eyes and hands. Such actions rely on neural mechanisms that must process an enormous amount of information in order to generate appropriate motor commands. Yet, compared to the great advancements in the field of perception for cognition, the neural underpinnings of how we control our movements, as well as the interactions between perception and motor control, remain elusive. With this research topic we provide a framework for: (1) the perception of real objects and shapes using visual and haptic information, (2) the reference frames for action and perception, and (3) how perceived target properties are translated into goal-directed actions and object manipulation. The studies in this special issue employ a variety of methodologies that include behavioral kinematics, neuroimaging, transcranial magnetic stimulation, and patient cases. Here we provide a brief summary and commentary on the articles included in this research topic.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".