Influence of a visual landmark shift on memory-guided reaching in monkeys
Bibliographic record
Abstract
The brain uses various sources of visual information, including both egocentric and allocentric to aim movements. It has been shown that humans optimally weigh egocentric and allocentric (landmark) cues when pointing (Bryne & Crawford 2010) but it is not known if monkeys do this. The main purpose of this study is to determine the influence of allocentric cue shifts on reaching responses in non-human primates. In order to do this, reach and gaze data were collected from one female Macaca mulatta monkey (ML) trained to perform a memory-guided reaching task. The hand was initially placed at 1 of 3 locations of a waist level LED bar while gaze fixated centrally. A landmark (4 ‘dots’ spaced 10° apart) was then presented at 1 of 15 locations on a touch screen after a delay. A visual target then appeared transiently at a variable location within this landmark, followed by a visual mask. After the mask, the landmark either reappeared at the same location (stable-landmark condition) or shifted by 8° in one of 8 directions (landmark-shift condition). The fixation light then extinguished, signaling a reach to the target. ‘No-landmark’ controls were the same, but without the landmark. Mean ellipse area for gaze endpoints is 18.2°² and the mean ellipse area for reach end points is 16.3°². Reaches correlated (r =0.45) with target location relative to landmarks, showing animals did not simply reach to the landmark. Correlations for reach and gaze were poor (r =0.05). Reach had lower variance and was better correlated to targets than gaze suggesting gaze was not used to guide reach in this task. In the landmark-shift condition, reaches shifted partially (mean=23%) with the landmark. Overall, this data suggests that the monkey is influenced by visual landmarks when reaching to a remembered target in a similar way as humans.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".