Deciphering Decision Making: Variation in Animal Models of Effort- and Uncertainty-Based Choice Reveals Distinct Neural Circuitries Underlying Core Cognitive Processes
Bibliographic record
Abstract
Maladaptive decision-making is increasingly recognized to play a significant role in numerous psychiatric disorders, such that therapeutics capable of ameliorating core impairments in judgment may be beneficial in a range of patient populations. The field of "decision neuroscience" is therefore in its ascendancy, with researchers from diverse fields bringing their expertise to bear on this complex and fascinating problem. In addition to the advances in neuroimaging and computational neuroscience that contribute enormously to this area, an increase in the complexity and sophistication of behavioral paradigms designed for nonhuman laboratory animals has also had a significant impact on researchers' ability to test the causal nature of hypotheses pertaining to the neural circuitry underlying the choice process. Multiple such decision-making assays have been developed to investigate the neural and neurochemical bases of different types of cost/benefit decisions. However, what may seem like relatively trivial variation in behavioral methodologies can actually result in recruitment of distinct cognitive mechanisms, and alter the neurobiological processes that regulate choice. Here we focus on two areas of particular interest, namely, decisions that involve an assessment of uncertainty or effort, and compare some of the most prominent behavioral paradigms that have been used to investigate these processes in laboratory rodents. We illustrate how an appreciation of the diversity in the nature of these tasks can lead to important insights into the circumstances under which different neural regions make critical contributions to decision making.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".