The computational unconscious: Adaptive narrative control, psychopathology, and subjective well-being
Bibliographic record
Abstract
This paper introduces the notion of adaptive narrative control, a conception of how subpersonal computational processes shape the contents of conscious experience to realize adaptive behavior. We unpack the implications of the theory for understanding the computational mechanisms underwriting psychopathology and improvements in subjective well-being associated with psychedelic therapy and meditation. The core idea of adaptive narrative control is that systems equipped with an ‘attention schema’ — a model of its own attentional states and how attentional states can be controlled — can come to anticipate not only the epistemic implications of certain attentional states, but also the pragmatic consequences, such as how certain attentional states potentiate certain affective responses. In anticipating affective states, the system is able to regulate affective states through ‘mental action’ — the endogenous control of attention. We argue that using mental action to bias the sampling of evidence to control the ‘narrative’ — the upshot of inference understood to correspond to the contents of conscious experience — allows the system to regulate affective and physiological states in ways that potentiate adaptive behavior. However, it is this adaptive capacity which gives rise to the computational mechanism — ‘avoidant mental action’, or equivalently ‘motivated inattention’ — which we argue is a core mechanism underlying psychopathology. We unpack this approach within the active inference framework to provide specification of the candidate mechanisms. We argue this conception can be used to account for the rigid belief formation characterizing ‘canalization’ and show how the decrements in subjective well-being come as a consequence of reduced recognition and categorisation of emotions (i.e., alexithymia or impaired emotional granularity). We argue that while avoidant mental action facilitates adaptive behavior, certain environmental conditions can lead it to resulting in decreased subjective well-being and psychopathology. Our account partially echoes a Freudian perspective on the function and effects of avoidant defense mechanisms like repression, and brings into view a novel computational conception of the dynamic unconscious— the ‘computational unconscious’. Finally, we explore how this conceptualisation can expand and refine the ‘REBUS’ model of psychedelic action and therapy, and explain some of the increments in subjective well-being associated with meditation.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".