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Record W4391261105 · doi:10.31234/osf.io/x7aew

The computational unconscious: Adaptive narrative control, psychopathology, and subjective well-being

2024· preprint· en· W4391261105 on OpenAlexaff
George Deane, Jonas Mago, Aikaterini Fotopoulou, Matthew D. Sacchet, Robin Carhart‐Harris, Lars Sandved-Smith

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsMcGill UniversityUniversité de MontréalMila - Quebec Artificial Intelligence Institute
Fundersnot available
KeywordsUnconscious mindNarrativePsychopathologyPsychologyControl (management)Cognitive psychologyComputer sciencePsychoanalysisCognitive scienceArtificial intelligenceClinical psychologyArtLiterature

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.325
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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