Dynamic Agent-Based Model of Hand-Preference Behavior Patterns in the Mouse
Bibliographic record
Abstract
Using a new agent-based model that mimics the learning process in hand-reaching behavior of individual mice, we show that mouse hand preference is probabilistic, dependent on the environment and prior learning. We quantify the learning capabilities of three inbred strains and show that population distributions of hand preference emerge from the properties of individual mice. The model informs our understanding of gene—environment interactions because it accommodates genotypic differences in learning and memory abilities, and environmental biases. We tuned each strain’s model to match their experimental hand-preference distributions in unbiased worlds and, by comparing simulations and experiments, identified and quantified a constitutive left-bias in hand preference of one strain. The models, tuned for unbiased worlds, match experimental measures in left- and right-biased worlds and in biased worlds after previous training. New measures quantitatively assess this matching, revealing that two strains, previously considered non-learners of hand preference, actually have significant learning ability and we confirm this with new experiments. Model mice match the kinetics of hand-preference learning of one strain and predict the limits of learning. We conclude that genetically evolved hand-preference behavior in mice is inherently probabilistic to provide robustness and allow constant adaptability to ever-changing environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".