Bibliographic record
Abstract
In a previous experiment with patients who had undergone unilateral temporal thermocoagulation lesions to alleviate intractable epilepsy, we demonstrated that the right parahippocampal cortex was critical for the performance of a spatial memory task (Bohbot et al. (1998) Neuropsychologia 36:1217-1238). Based on this evidence, we predicted that H.M., whose caudal parahippocampal cortex was structurally intact (Corkin et al. (1997) J Neurosci 17:3964-3979), would be able to learn the spatial memory task. This task was designed to be a human analogue of the Morris water maze in that it measured participants' ability to learn the location of a target, which was an invisible weight sensor placed under a carpet (Bohbot et al. (1998) Neuropsychologia 36:1217-1238). H.M. was first tested with the sensor under a small carpet (162 cm x 150 cm). Then, interspersed with the first sensor location, he was tested with the sensor in a second location, covered by a larger carpet (250 cm x 210 cm). He found the second target location in a direct path on only 10% of the trials. In contrast, when tested on the first sensor location, he walked directly toward the center of the testing area in 19/35 trials and from there found the sensor in a direct path on 15 of the 19 trials (80%). The number of direct hits at the first target location was significantly greater than chance (P < 0.0005). An analysis of H.M.'s paths showed that they were characteristic of fast learning, and that he did not rely on egocentric, short-term, or working memory strategies to learn the task. H.M's ability to locate the sensor is remarkable given his severe amnesia and his inability to explicitly recollect the testing episode. These findings underscore the role of the parahippocampal cortex in spatial memory.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".