Evidence map›Paper›PMID 42018578›Full record

ArticlePLoS computational biology2026

Sharing the spotlight: Uncovering common attentional dynamics across species.

Mina Glukhova, Alejandro Tlaie, Robert Taylor, Pierre-Antoine Ferracci, Katharine Shapcott, Berkutay Mert, Olga Arne, Andrei Ciuparu, Raul C Muresan, Martha N Havenith and 1 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. 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

11 authors.

Mina GlukhovaErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.ORCID https://orcid.org/0009-0007-0663-5161
Alejandro TlaieErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.
Robert TaylorErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.
Pierre-Antoine FerracciErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.
Katharine ShapcottErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.
Berkutay MertErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.
Olga ArneErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.
Andrei CiuparuTransylvanian Institute of Neuroscience, Experimental and Theoretical Neuroscience Lab, Cluj-Napoca, Romania.
Raul C MuresanTransylvanian Institute of Neuroscience, Experimental and Theoretical Neuroscience Lab, Cluj-Napoca, Romania.
Martha N HavenithErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.
Marieke L SchölvinckErnst Strüngmann Institute for Neuroscience in cooperation with the Max Planck Society, Zero-Noise Lab, Frankfurt am Main, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sustained attention is a key underlying process to many natural behaviours that are shared across species. Yet the way attention is commonly studied in a lab context precludes meaningful cross-species comparisons. Here, we engaged mice, monkeys, and humans in the same, naturalistic perceptual decision task in a virtual reality environment. We captured their behaviour in several parameters along the speed/accuracy axes along which sustained attention is classically defined, and used Hidden Markov Models (HMMs) to infer four attentional states. We show that the dynamics of these states, both in terms of their durations and transitions, are more similar across species than might have been expected. Moreover, attentional state fluctuations seem to be internally generated and are not predicted by task attributes. The task and analyses developed here represent a new approach to infer the dynamics of sustained attention from naturalistic behaviours, in a way that is generalizable across species.

Indexed as

AttentionAnimalsComputational BiologyHaplorhiniHidden Markov ModelsHumansMaleMarkov ChainsMiceSpecies Specificity

Identifiers

PMID42018578
PMCPMC13152210

What OpenQuestion holds

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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.