Ambiguity and conflict: Dissecting uncertainty in decision-making.
Bibliographic record
Abstract
Making decisions is fundamental to how we navigate, survive, and thrive in our environment. The quality of information used to support decisions is rarely perfect. Many decisions are made under conditions of uncertainty, arising from ambiguous or conflicting information. Conflict and ambiguity, though conceptually distinct, both generate uncertainty, a commonality that has led to overlapping and inconsistent terminology in the literature. Evidence from human and animal research suggests a behavioral dissociation in responding to conflict and ambiguity. This dissociation can be studied through the implementation of spatial or operant tasks in rodents which find close parallels in gambling tasks in humans. Pharmacological manipulations in rodents and fMRI studies in humans further suggest a dissociation in the neural processing of conflict and ambiguity such that fronto-striato-parietal circuits may be most important for interpreting ambiguous information, while the ventral striatum and ventral hippocampus are critical for resolving conflicting information. Overall, the neural representation and resolution of conflict and ambiguity remain relatively understudied despite the fundamental importance of these processes to understanding decision-making. We highlight the need for further research to differentiate these related yet distinct processes through implementation of carefully designed behavioral tasks with neural circuit-dissection techniques and the potential to pursue translational research between rodents and humans. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".