Decision-making trades off learned and perceived information
Bibliographic record
Abstract
A fundamental question in cognitive science is how information from internal memory is combined with external sensory input when making decisions. We hypothesized that previously learned and currently perceived information trade off against each other, such that extracting information from one source reduces the gathering and usage of information from the other. To test this hypothesis, we designed a two-armed bandit task where each arm is composed of both learned and perceived elements. We monitored participants' gathering of perceptual information using eye tracking. Participants' choices and gaze deployment showed a trade-off between the impact of learned and perceived information. The more a participant utilized internally stored learned information, the less they gathered perceptual information, and vice versa. Modeling participants' information gathering indicated that the trade-off results from the faster gathering of learned information, which, when used, makes it less valuable to further invest effort in gathering additional perceptual information. Preliminary findings also suggested that an individual's tendency to primarily rely on one source of information is a stable individual trait. These findings reveal how humans balance between learning and perception in forming decisions.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".