Using classification-based decoding to analyze the Spatiotopic and Retinotopic memory representations in primates
Bibliographic record
Abstract
Studies show that a successful visually guided behaviour depends on the target's spatiotopic position. Targets can be encoded and retrieved from memory (decoded) to perform different tasks. However, whether "this retinotopically encoded information transforms to spatiotemporal encoded representations before residing in memory" is not investigated thoroughly. This study aims to explore how accurate the retinotopic representation derives from spatiotopic encoding. We designed a task in which the spatiotopic and retinotopic encoding targets were evaluated on the same trials. The extra-cellular activity was recorded by inserting two 10x10 multi-electrode arrays in the dorsal and ventral circumvolutions of the lateral prefrontal cortex (area 8a, and 9/46, respectively) in two rhesus macaque monkeys. For example, an animal can remember the spatiotopic location of a stimulus in a particular position on display that leads to a success-based reward. However, does the animal keep the target's location as a retinotopically-encoded representation or a spatiotopic one? A classification-based decoding method was employed on single-cell recordings to show spatiotopic and retinotopic representations of a target. Albeit the results' accuracy in decoding retinotopic representations were higher than spatiotopic ones, the lower accuracy in spatiotopic location retrieval can be attributed to the specific brain areas where the cell recordings were performed. Additionally, our results indicate that spatiotopically-positioned targets are encoded as well as the retinotopic encoding. Different classification techniques were used (decision tree vs linear discriminant analysis), and the results show that each method responds better to a specific encoding in both spatiotopic and retinotopic frames of reference, respectively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".