Evidence map›Paper›PMID 40566157›Full record

ArticleEntropy (Basel, Switzerland)2025

Deep Hybrid Models: Infer and Plan in a Dynamic World.

Matteo Priorelli, Ivilin Peev Stoianov

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Embodied decisions as active inference.PLoS computational biology · 2025
    Article
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

2 authors.

Matteo PriorelliInstitute of Cognitive Sciences and Technologies, National Research Council of Italy, 35137 Padova, Italy.ORCID 0009-0007-4481-8727
Ivilin Peev StoianovInstitute of Cognitive Sciences and Technologies, National Research Council of Italy, 35137 Padova, Italy.ORCID 0000-0003-0642-259X

Funding

European Union's Horizon H2020-EIC-FETPROACT-2019 Programme for Research and Innovation Grant Agreement 951910
6 · The paper itself

Abstract

To determine an optimal plan for complex tasks, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically motivated proposal casts planning and control as an inference process.

Indexed as

active inferencedeep hybrid modelsmotor control

Identifiers

PMID40566157
PMCPMC12191456

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.