The Geometry of Working Memory in Action: Uncovering the Latent Structure of Systematic and Unsystematic Errors With and Without Saccadic Eye Movements
Bibliographic record
Abstract
Recent research emphasizes how working memory (WM) supports action in dynamic environments. But action almost always involves fast, “saccadic” eye movements that challenge WM representations, requiring spatial remapping and distorting spatial perception, especially along the saccade direction. To capture the impact of these distortions, we used a spatial WM task where participants, after a saccade or fixation, indicated remembered locations of memory items with a mouse. From the responses we extracted multiple measures of spatial error: response variability, shifts relative to the fixation point, rotations, compression and nonlinear distortions of space. These measures submitted to a principal component analysis (PCA) yielded two components: systematic and unsystematic spatial error both of which were correlated with established WM tasks, showing their relevance for WM fidelity. Systematic error revealed a centripetal bias, pulling towards spatial anchors like the fixation point and the memory array’s centre of mass. This suggests that systematic error reflects a spatial scaffold that encodes relational information but is inherently imprecise. Saccades increased both error components and amplified WM load effects. Notably, we found that distortions of systematic error were especially pronounced along saccade direction for items that were remapped across visual fields, implying that WM fidelity is more vulnerable when information needs to pass through the corpus callosum. Together, our findings are consistent with the idea of WM actively reconstructing spatial information. They offer new insights into how saccades and spatial remapping shape WM in a dynamic world.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".