A model of how hierarchical representations constructed in the hippocampus are used to navigate through space
Why this work is in the frame
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Bibliographic record
Abstract
Animals can navigate through complex environments with amazing flexibility and efficiency: they forage over large areas, quickly learning rewarding behavior and changing their plans when necessary. Some insights into the neural mechanisms supporting this ability can be found in the hippocampus (HPC)—a brain structure involved in navigation, learning, and memory. Neuronal activity in the HPC provides a hierarchical representation of space, representing an environment at multiple scales. In addition, it has been observed that when memory-consolidation processes in the HPC are inactivated, animals can still plan and navigate in a familiar environment but not in new environments. Findings like these suggest three useful principles: spatial learning is hierarchical, learning a hierarchical world-model is intrinsically valuable, and action planning occurs as a downstream process separate from learning. Here, we demonstrate computationally how an agent could learn hierarchical models of an environment using off-line replay of trajectories through that environment and show empirically that this allows computationally efficient planning to reach arbitrary goals within a reinforcement learning setting. Using the computational model to simulate hippocampal damage reproduces navigation behaviors observed in rodents with hippocampal inactivation. The approach presented here might help to clarify different interpretations of some spatial navigation studies in rodents and present some implications for future studies of both machine and biological intelligence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it