Human Economic Choice as Costly Information Processing
Bibliographic record
Abstract
We develop and test a model that provides a unified account of the neural processes underlying behavior in a classical economic choice task. The model describes in a stylized way brain processes engaged in evaluating information provided by the experimental stimuli, and produces a consistent account of several important features of the decision process in different environments: e.g., when the probability is specified or not (ambiguous choices). These features include the choices made, the time to decide, the error rate in choice, and the patterns of neural activation. The model predicts that the further two stimuli are from each other in utility space, the shorter the reaction time will be, fewer errors in choice will be made, and less neural activation will be required to make the choice. The model also predicts that choices with ambiguity can be made more quickly and will require reduced neural activation in the horizontal intra-parietal sulcus than for choices with risk. Also, everything else being equal a larger value of certainty option in the choice will induce larger neural activation, and less experience on the part of the subject making choices will induce larger activation. We provide experimental evidence that is consistent with these predictions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".