Evidence map›Paper›PMID 40760399›Full record

ReviewCognitive, affective & behavioral neuroscience2025

Born to act: Deferred action and desire as active inference.

Valery Krupnik

Abstract readReview
In one paragraph

Review in Cognitive, affective & behavioral neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Valery KrupnikDepartment of Mental Health, US Naval Hospital Camp Pendleton, H200 Mercy Circle, Camp Pendleton, CA, 92055, USA. valery.y.krupnik.civ@health.mil.ORCID 0000-0003-1086-6716

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The active inference framework (AIF) considers the brain as a generative model guiding behavior under the imperative of minimizing the model's variational free energy. Computationally, this is accomplished by hierarchical Bayesian inference. The theory views organisms as doxastic agents, which has drawn the criticism of being insufficient to explain conative agents motivated by desire. Specifically, it has been noted that the concept of desire is not isomorphic with belief and, therefore, fits poorly with AIF. In this paper, we build on previous work that suggests a path to integrating conation in AIF and present three arguments. First, the dichotomy between belief and desire is unnecessary. To that end, we define desire as a hierarchical inference that starts from a domain-general inference on the agent's affective dynamics (affective charge) and descends to contextualized inference on the precision of action policies. We suggest that this hierarchy is implemented by a coordinated activity of the intrinsic brain networks: default mode, action mode, executive, and salient. Second, we argue for a central role that deferred action plays in the process of desire by allowing for affect-dependent awareness of the agent's states over different timescales. Third, we suggest that the proposed model of desire and deferred action has ramifications for understanding psychopathology, which we frame as the malfunction of deferred action and desire and use obsessive-compulsive disorder and depression as examples. This view entails that disorders of affect and motivation are subjectively desired despite their associated suffering.

Indexed as

AffectModels, NeurologicalModels, PsychologicalMotivationBayes TheoremHumansActive inferenceDeferred actionDepressionDesireMotivationObsessive-compulsive disorderSelf-efficacy

Identifiers

PMID40760399
PMCPMC12615558

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.