Neural substrates for allocentric-to-egocentric conversion of target representation for memory-guided reach
Bibliographic record
Abstract
Allocentric cues can be used to encode target location in visuo-spatial memory (Chen et al., 2011; Obhi & Goodale, 2005), and the allocentric representation is converted into egocentric representation at the first possible opportunity for reach (Chen et al., 2011). However, neural substrates for allocentric-to-egocentric conversion have not been explored yet. Here we used fMRI to investigate brain areas involved in allocentric-to-egocentric conversion for memory-guided reach. Ten participants reached towards a remembered target location represented in allocentric frames of reference. Participants fixated a central point while a target was presented along with an allocentric cue for 2s. The concurrent presentation of target and cue was followed by a delay period (6s), after which an auditory instruction ("Same cue" or "Different cue") instructed participants that the allocentric cue would re-appear at the same location, allowing for a conversion of target location from allocentric to egocentric, or at a different location, requiring participants to wait for the re-appearance of the cue before they knew the target location to reach. A second delay period (10s) followed the auditory instruction. Next, the allocentric cue re-appeared for 2s and was followed by the go-signal to reach towards the remembered target location relative to the location of the re-displayed allocentric cue. We hypothesized that brain areas involved in allocentric-to-egocentric conversion would show higher activation when the allocentric representation of target location could be turned into an egocentric representation. This would be revealed by higher activation in the "Same cue" as compared to the "Different cue" condition during the second delay period. This pattern was revealed in bilateral dorsal precuneus, left angular gyrus and bilateral inferior frontal gyrus. Our results suggest that posterior parietal cortex and frontal areas play a critical role in converting allocentric representation of target location into egocentric representation for reach planning. Meeting abstract presented at VSS 2014
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".