Time-course of allocentric-to-egocentric conversion in memory-guided reach
Bibliographic record
Abstract
It has been suggested that both egocentric and allocentric cues can be used for memory-guided movements, and that allocentric memory dominates during longer memory intervals (Obhi & Goodale, 2005; Hay & Redon, 2006). In the present study we examined 1) at what point in the reach plan allocentric representations are converted to egocentric representations and 2) the rates of decay of egocentric and allocentric memory. Nine subjects reached for a remembered target in complete darkness after a variable memory delay (2.5s, 5.5s, or 8.5 seconds in total). In the Ego Task the target was presented alone in the periphery on a CRT screen. In the Allo Task the target was presented along with four nearby blue disks (visual landmarks). After the variable delay, the landmarks reappeared at a shifted location, and subjects were instructed to reach to the target relative to the landmarks. In the Allo-Ego Conversion Task the shifted landmarks re-appeared twice: once before the variable delay and once immediately after (just before the reach cue). We analyzed the variance of reaching errors and reaction time (RT) for each memory delay in the three tasks. In the Ego Task, variance increased significantly in medium and long delays compared to the short delay; RT was longer in the short delay than medium and long delays, and the latter was significant. In the Allo Task there was no significant difference in variance and RT across the delays. In the Allo-Ego Conversion Task, there were significant increase in variance and decrease in RT for the medium and long delays compared to the short delay, which was similar to Ego Task. These results confirm that egocentric memory for reaching degrades more rapidly than allocentric memory, but despite this, in our Allo-Ego Task subjects preferred to convert allocentric into egocentric representations at the first possible opportunity.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".