Evidence map›Paper›PMID 41358251›Full record

ArticleAdvances in neural information processing systems2024

Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning.

Gaia Molinaro, Cédric Colas, Pierre-Yves Oudeyer, Anne G E Collins

Abstract read
In one paragraph

Article in Advances in neural information processing systems, 2024. 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. 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

4 authors.

Gaia MolinaroUniversity of California, Berkeley.
Cédric ColasMassachusetts Institute of Technology.
Pierre-Yves OudeyerInria Centre at the University of Bordeaux.
Anne G E CollinsUniversity of California, Berkeley.

Funding

The neural computations supporting hierarchical reinforcement learningR01MH119383 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI COLLINS, ANNE G.E. · 2019 to 2023
$1.9M
NIMH NIH HHS R01 MH119383
6 · The paper itself

Abstract

Humans are autotelic agents who learn by setting and pursuing their own goals. However, the precise mechanisms guiding human goal selection remain unclear. Learning progress, typically measured as the observed change in performance, can provide a valuable signal for goal selection in both humans and artificial agents. We hypothesize that human choices of goals may also be driven by

Identifiers

PMID41358251
PMCPMC12676560

What OpenQuestion holds

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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.