A role for neurogenesis in probabilistic reward learning.
Bibliographic record
Abstract
Rewards are often unreliable and optimal choice requires behavioral flexibility and learning about the probabilistic nature of uncertain rewards. Probabilistic learning occurs over multiple trials, often without conscious knowledge, and is traditionally associated with striatal function. While the hippocampus is classically recognized for its role in memory for individual experiences, recent work indicates that it is also involved in probabilistic forms of learning but little is known about the features that support such learning. We hypothesized that adult neurogenesis may be involved, because adult-born neurons contribute to both learning and reward-related behaviors. To test this, we used an appetitive probabilistic reversal learning task where a correct lever is rewarded with 80% probability and an incorrect lever is rewarded with 20% probability. Behavioral flexibility was assessed by switching correct-incorrect lever identities after 8 consecutive correct choices. Transgenic male rats that lacked adult neurogenesis displayed an initial deficit in discriminating the correct and incorrect levers, but they were not impaired at reversing behavior when the reward contingencies switched. When reward was withheld after a correct lever choice, neurogenesis-deficient rats were more likely to choose the incorrect lever on the subsequent trial. Also, rats with intact neurogenesis were more sensitive to reward at the incorrect lever. Differences were not observed in control transgenic rats that had intact neurogenesis. These results identify a novel role for neurogenesis in learning about uncertain, probabilistic rewards. Altered sensitivity to reward and negative feedback furthermore implicates neurogenesis in cognitive phenotypes associated with mood disorders such as depression. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".