Adult neurogenesis promotes efficient, nonspecific search strategies in a spatial alternation water maze task
Bibliographic record
Abstract
Abstract Goal-directed navigation requires learning strategies that are efficient and minimize costs. In some cases it may be desirable to flexibly adjust behavioral responses depending on the cues that vary from one episode to the next. In others, successful navigation might be achieved with inflexible, habit-like responses that reduce cognitive load. Adult neurogenesis is believed to contribute to the spatial processing functions of the hippocampus, particularly when behavioral flexibility is required. However, little is known about the role of neurogenesis in spatial navigation when goals are unpredictable or change according to certain rules. We hypothesized that neurogenesis is necessary in a spatial navigation task that involves different patterns of reinforcement. Intact and neurogenesis-deficient rats were trained to escape to one of two possible platform locations in a spatial water maze. The platform either repeated in the same location for all trials in a day, alternated between two locations across trials, or randomly moved between the two locations. Neurogenesis selectively enhanced escape performance in the alternating condition, but not by improving platform choice accuracy. Instead, neurogenesis-intact rats made fewer search errors and developed an efficient habit-like strategy where they consistently swam to a preferred location. If the platform was not present, they proceeded to the other possible location. In contrast, neurogenesis-deficient rats were indecisive and navigationally less-efficient. Thus, in conditions where goals follow a predictable spatiotemporal pattern, adult neurogenesis promotes the adoption of navigation strategies that are spatially nonspecific but, nonetheless, accurate and efficient.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".