Touchscreen Response Precision Is Sensitive to the Explore/Exploit Trade-off
Bibliographic record
Abstract
The explore/exploit trade-off is a fundamental property of choice selection during reward-guided decision making, where the "same" choice can reflect either of these internal cognitive states. An unanswered question is whether the execution of a decision provides an underexplored measure of internal cognitive states. Touchscreens are increasingly used across species for cognitive testing and afford the ability to measure the precise location of choice touch responses. We examined how male and female mice in a restless bandit decision making task interacted with a touchscreen to determine if the explore/exploit trade-off, prior reward, and/or sex differences change the variability in the kinetics of touchscreen choices. During exploit states, successive touch responses are closer together than those made in an explore state, suggesting exploit states reflect periods of increased motor stereotypy. Although exploit decisions might be expected to be rewarded more frequently than explore decisions, we find that immediate past reward reduces choice variability independently of explore/exploit state. Male mice are more variable in their interactions with the touchscreen than females, even in low-variability trials such as exploit or following reward. These results suggest that as exploit behavior emerges in reward-guided decision making, all mice become less variable and more automated in both their choice and the actions taken to make that choice, but this occurs on a background of increased male variability. These data uncover the hidden potential for touchscreen decision making tasks to uncover the latent neural states that unite cognition and movement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".