Deeply Felt Affect: The Emergence of Valence in Deep Active Inference
Bibliographic record
Abstract
The positive-negative axis of emotional valence has long been recognised as fundamental to adaptive behaviour, but its domain-generality has largely eluded formal theories and modelling. Using deep active inference – a hierarchical inference scheme that rests on inverting a model of how sensory data are generated – we develop a principled Bayesian account of emotional valence. This formulation associates valence with subjective fitness and exploits the domain-generality of second-order beliefs (i.e., beliefs about beliefs). We construct an affective agent that infers its valence state from the expected precision of its phenotype-congruent action model (i.e., subjective fitness) in any given environment. The ensuing affective states then optimise that confidence pre-emptively. The evidence for inferred (i.e., ‘felt’) valenced states depends upon the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). We simulate affective inference in a T-maze paradigm requiring context learning, followed by context reversal. The result is a deep (biologically plausible) agent that infers its affective state and reduces its uncertainty through internal action (i.e., optimises prior beliefs that underwrite confidence). Thus, we demonstrate the potential of active inference in providing a formal and computationally tractable account of the link between affect, (mental) action, and implicit meta-cognition.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".