Information Processing in Decision-Making Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Decisions result from an interaction between multiple functional systems acting in parallel to process information in very different ways, each with strengths and weaknesses. In this review, the authors address three action-selection components of decision-making: The Pavlovian system releases an action from a limited repertoire of potential actions, such as approaching learned stimuli. Like the Pavlovian system, the habit system is computationally fast but, unlike the Pavlovian system permits arbitrary stimulus-action pairings. These associations are a "forward'' mechanism; when a situation is recognized, the action is released. In contrast, the deliberative system is flexible but takes time to process. The deliberative system uses knowledge of the causal structure of the world to search into the future, planning actions to maximize expected rewards. Deliberation depends on the ability to imagine future possibilities, including novel situations, and it allows decisions to be taken without having previously experienced the options. Various anatomical structures have been identified that carry out the information processing of each of these systems: hippocampus constitutes a map of the world that can be used for searching/imagining the future; dorsal striatal neurons represent situation-action associations; and ventral striatum maintains value representations for all three systems. Each system presents vulnerabilities to pathologies that can manifest as psychiatric disorders. Understanding these systems and their relation to neuroanatomy opens up a deeper way to treat the structural problems underlying various disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it