Toward an integrative perspective on the neural mechanisms underlying persistent maladaptive behaviors
Bibliographic record
Abstract
A host of public health problems -- from drug addiction to obesity -- are associated with persistent, maladaptive behaviors. The underlying causes of such behaviors have received considerable attention from psychologists, clinicians, computational theorists, and neuroscientists. These diverse perspectives were showcased in a symposium at the University of Rochester entitled Persistent, Maladaptive Behaviors: Why We Make Bad Choices. Here, we synthesize novel findings and perspectives arising from the symposium and integrate those findings within the broader literature. We begin by reviewing theoretical models of maladaptive behaviors and their underlying neural circuitry. We then discuss the behavioral and clinical manifestations of maladaptive behaviors. Given the multifaceted nature of maladaptive behavior, we argue that a dimensional approach may help inform theoretical models and clinical interventions, particularly those that rely on brain stimulation to induce neuro-plastic changes that rescue and remediate aberrant neural responses. We conclude that an interdisciplinary approach to studying maladaptive behavior will help advance the field toward improved prevention, diagnosis, and treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".