Hippocampal development and the dissociation of cognitive-spatial mapping from motor performance
Bibliographic record
Abstract
The publication of a recent article in F1000Research has led to discussion of, and correspondence on a broader issue that has a long history in the fields of neuroscience and psychology. Namely, is it possible to separate the cognitive components of performance, in this case spatial behavior, from the motoric demands of a task? Early psychological experiments attempted such a dissociation by studying a form of spatial maze learning where initially rats were allowed to explore a complex maze, termed “latent learning,” before reinforcement was introduced. Those rats afforded the latent learning experience solved the task faster than those that were not, implying that cognitive map learning during exploration aided in the performance of the task once a motivational component was introduced. This form of latent learning was interpreted as successfully demonstrating that an exploratory cognitive map component was acquired irrespective of performing a learned spatial response under deprivation/motivational conditions. The neural substrate for cognitive learning was hypothesized to depend on place cells within the hippocampus. Subsequent behavioral studies attempted to directly eliminate the motor component of spatial learning by allowing rats to passively view the distal environment before performing any motor response using a task that is widely considered to be hippocampal-dependent. Latent learning in the water maze, using a passive placement procedure has met with mixed results. One constraint on viewing cues before performing a learned swimming response to a hidden goal has been the act of dynamically viewing distal cues while moving through a part of the environment where an optimal learned spatial escape response would be observed. We briefly review these past findings obtained with adult animals to the recent efforts of establishing a “behavioral topology” separating cognitive-spatial learning from tasks differing in motoric demands in an attempt to define when cognitive-spatial behavior emerges during development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".