A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality
Bibliographic record
Abstract
A dataset consisting of whole-brain fMRI (functional magnetic resonance imaging)/MEG (magnetoencephalography) images, eye tracking files, and behavioral records from healthy adult human participants when they performed a spatial-memory paradigm in a virtual environment was collected to investigate the neural representation of the cognitive map defined by unique spatial relationship of three objects, as well as the neural dynamics of the cognitive map following the task demand from localizing self-location to remembering the target location relative to the self-body. The dataset, including both fMRI and MEG, was also used to investigate the neural networks involved in representing a target within and outside the visual field. The dataset included 19 and 12 university students at Peking University for fMRI and MEG experiments, respectively (fMRI: 12 women, 7 men; MEG: 4 women, 8 men). The average ages of those participants were 24.9 years (MRI: 18-30 years) and 22.5 years (MEG: 19-25 years), respectively. fMRI BOLD and T1-weighted images were acquired using a 3T Siemens Prisma scanner (Siemens, Erlangen, Germany) equipped with a 20-channel receiver head coil. MEG neuromagnetic data were acquired using a 275-channel MEG system (CTF MEG, Canada). The dataset could be further used to investigate a range of neural mechanisms involved in human spatial cognition or to develop a bioinspired deep neural network to enhance machines' abilities in spatial processing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".