Predictive Representations in Hippocampal and Prefrontal Hierarchies
Bibliographic record
Abstract
As we navigate the world, we use learned representations of relational structures to explore and to reach goals. Studies of how relational knowledge enables inference and planning are typically conducted in controlled small-scale settings. It remains unclear, however, how people use stored knowledge in continuously unfolding navigation (e.g., walking long distances in a city). We hypothesized that multiscale predictive representations guide naturalistic navigation in humans, and these scales are organized along posterior-anterior prefrontal and hippocampal hierarchies. We conducted model-based representational similarity analyses of neuroimaging data collected while male and female participants navigated realistically long paths in virtual reality. We tested the pattern similarity of each point, along each path, to a weighted sum of its successor points within predictive horizons of different scales. We found that anterior PFC showed the largest predictive horizons, posterior hippocampus the smallest, with the anterior hippocampus and orbitofrontal regions in between. Our findings offer novel insights into how cognitive maps support hierarchical planning at multiple scales. SIGNIFICANCE STATEMENT Whenever we navigate the world, we represent our journey at multiple horizons: from our immediate surroundings to our distal goal. How are such cognitive maps at different horizons simultaneously represented in the brain? Here, we applied a reinforcement learning-based analysis to neuroimaging data acquired while participants virtually navigated their hometown. We investigated neural patterns in the hippocampus and PFC, key cognitive map regions. We uncovered predictive representations with multiscale horizons in prefrontal and hippocampal gradients, with the longest predictive horizons in anterior PFC and the shortest in posterior hippocampus. These findings provide empirical support for the computational hypothesis that multiscale neural representations guide goal-directed navigation. This advances our understanding of hierarchical planning in everyday navigation of realistic distances.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".