Evidence map›Paper›PMID 42529568›Full record

ArticleAdvances in neural information processing systems2025

Flexible inference for animal learning rules using neural networks.

Yuhan Helena Liu, Victor Geadah, Jonathan Pillow

Abstract read

In one paragraph

Article in Advances in neural information processing systems, 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

3 authors.

Yuhan Helena LiuPrinceton University, Princeton, NJ, USA.

Victor GeadahPrinceton University, Princeton, NJ, USA.

Jonathan PillowPrinceton University, Princeton, NJ, USA.

Funding

Subcortical Neural Coding and DynamicsU19NS104648 · NINDS · PRINCETON UNIVERSITY · PI PILLOW, JONATHAN WILLIAM · 2017 to 2021

$15.3M

Neural time-integration underlying higher cognitive functionR01EY017366 · NEI · UNIVERSITY OF TEXAS AT AUSTIN · PI HUK, ALEXANDER C, PILLOW, JONATHAN WILLIAM · 2008 to 2016

$2.9M

Mechanisms of persistent neural activityR01EY033064 · NEI · UNIVERSITY OF TEXAS AT AUSTIN · PI Alexander C Huk, Jonathan William Pillow · 2022 to 2026

$2.6M

Real-time statistical algorithms for controlling neural dynamics and behaviorR01EB026946 · NIBIB · STATE UNIVERSITY NEW YORK STONY BROOK · PI PARK, IL MEMMING, PILLOW, JONATHAN WILLIAM · 2018 to 2020

$1.1M

Adaptive statistical algorithms for learning and control of neural dynamicsRF1DA056404 · NIDA · PRINCETON UNIVERSITY · PI PARK, IL MEMMING, PILLOW, JONATHAN WILLIAM · 2022 to 2022

$969k

NEI NIH HHS R01 EY017366NEI NIH HHS R01 EY033064NIBIB NIH HHS R01 EB026946NIDA NIH HHS RF1 DA056404NINDS NIH HHS U19 NS104648

6 · The paper itself

Abstract

Understanding how animals learn is a central challenge in neuroscience, with growing relevance to the development of animal- or human-aligned artificial intelligence. However, existing approaches tend to assume fixed parametric forms for the learning rule (e.g., Q-learning, policy gradient), which may not accurately describe the complex forms of learning employed by animals in realistic settings. Here we address this gap by developing a framework to infer learning rules directly from behavioral data collected during

Identifiers

PMID42529568

PMCPMC13417974

What OpenQuestion holds

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Registered trials

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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.