The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology
Bibliographic record
Abstract
The circumplex model of affect proposes that all affective states arise from cognitive interpretations of core neural sensations that are the product of two independent neurophysiological systems. This model stands in contrast to theories of basic emotions, which posit that a discrete and independent neural system subserves every emotion. We propose that basic emotion theories no longer explain adequately the vast number of empirical observations from studies in affective neuroscience, and we suggest that a conceptual shift is needed in the empirical approaches taken to the study of emotion and affective psychopathologies. The circumplex model of affect is more consistent with many recent findings from behavioral, cognitive neuroscience, neuroimaging, and developmental studies of affect. Moreover, the model offers new theoretical and empirical approaches to studying the development of affective disorders as well as the genetic and cognitive underpinnings of affective processing within the central nervous system. This work was supported in part by NIMH Grants MH01232, MH59139, MH36197, MHK02-74677, and MH068318; a grant from the National Alliance for Research in Schizophrenia and Affective Disorders (NARSAD); NSF Grant BSC-0421702; and funding from the Thomas D. Klingenstein and Nancy D. Perlman Family Fund and the Suzanne Crosby Murphy Endowment at Columbia University.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".