Evidence map›Paper›PMID 40021555›Full record

ArticleBehavior research methods2025

The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research.

Konstantinos Voudouris, Ben Slater, Lucy G Cheke, Wout Schellaert, José Hernández-Orallo, Marta Halina, Matishalin Patel, Ibrahim Alhas, Matteo G Mecattaf, John Burden and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Behavior research methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Discerning Quantity: Numerosity in Two Embodied Machine Learning Agents.Behavioral sciences (Basel, Switzerland) · 2026
    Article
  2. 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

14 authors.

Konstantinos VoudourisDepartment of Psychology, University of Cambridge, Cambridge, UK. kv301@srcf.net.
Ben SlaterDepartment of Psychology, University of Cambridge, Cambridge, UK.
Lucy G ChekeDepartment of Psychology, University of Cambridge, Cambridge, UK.
Wout SchellaertVRAIN / ValGRAI, Universitat Politècnica de València, València, Spain.
José Hernández-OralloVRAIN / ValGRAI, Universitat Politècnica de València, València, Spain.
Marta HalinaDepartment of History and Philosophy of Science, University of Cambridge, Cambridge, UK.
Matishalin PatelCentre for Data Science AI and Modelling, University of Hull, Hull, UK.
Ibrahim AlhasLeverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
Matteo G MecattafLeverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
John BurdenLeverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
Joel HolmesLeverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
Niharika ChaubeyLeverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
Niall DonnellyFaculty of Environment, Science and Economy, University of Exeter, Exeter, UK.
Matthew CrosbyDepartment of Computing, Imperial College London, London, UK.

Funding

Defense Advanced Research Projects Agency HR00112120007Economic and Social Research Council ES/P000738/1Leverhulme Trust G108086Programa de Ayudas a la Formación de Foctores en Colaboración con Empresas DOCEMPR21Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) de la Comunitat Valenciana 2014-2020 INNEST/2021/317 (Neurocalçat)TWCF TWCF-2020-20539
6 · The paper itself

Abstract

The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research communities. In this paper, we present the latest version of the Animal-AI Environment, outlining several major features that make the game more engaging for humans and more complex for AI systems. These features include interactive buttons, reward dispensers, and player notifications, as well as an overhaul of the environment's graphics and processing for significant improvements in agent training time and quality of the human player experience. We provide detailed guidance on how to build computational and behavioural experiments with the Animal-AI Environment. We present results from a series of agents, including the state-of-the-art deep reinforcement learning agent Dreamer-v3, on newly designed tests and the Animal-AI testbed of 900 tasks inspired by research in the field of comparative cognition. The Animal-AI Environment offers a new approach for modelling cognition in humans and non-human animals, and for building biologically inspired artificial intelligence.

Indexed as

Artificial IntelligenceCognitionVirtual RealityAnimalsHumansArtificial intelligenceCognitionComparative cognitionComputational neuroscienceDevelopmental psychologyReinforcement learning

Identifiers

PMID40021555
PMCPMC11870899

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.