Allocentric representations for target memory and reaching in human cortex
Bibliographic record
Abstract
The use of allocentric cues for movement guidance is complex because it involves the integration of visual targets and independent landmarks and the conversion of this information into egocentric commands for action. Here, we focus on the mechanisms for encoding reach targets relative to visual landmarks in humans. First, we consider the behavioral results suggesting that both of these cues influence target memory, but are then transformed-at the first opportunity-into egocentric commands for action. We then consider the cortical mechanisms for these behaviors. We discuss different allocentric versus egocentric mechanisms for coding of target directional selectivity in memory (inferior temporal gyrus versus superior occipital gyrus) and distinguish these mechanisms from parieto-frontal activation for planning egocentric direction of actual reach movements. Then, we consider where and how the former allocentric representations of remembered reach targets are converted into the latter egocentric plans. In particular, our recent neuroimaging study suggests that four areas in the parietal and frontal cortex (right precuneus, bilateral dorsal premotor cortex, and right presupplementary area) participate in this allo-to-ego conversion. Finally, we provide a functional overview describing how and why egocentric and landmark-centered representations are segregated early in the visual system, but then reintegrated in the parieto-frontal cortex for action.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".