Evidence map›Paper›PMID 40568544›Full record

ArticleRoyal Society open science2025

Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience.

Nicholas A Coles, Bartosz Perz, Maciej Behnke, Johannes C Eichstaedt, Soo Hyung Kim, Tu N Vu, Chirag Raman, Julian Tejada, Van-Thong Huynh, Guangyi Zhang and 46 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

56 authors.

Nicholas A ColesUniversity of Florida, Gainesville, FL, USA.ORCID https://orcid.org/0000-0001-8583-5610
Bartosz PerzWrocław University of Science and Technology, Wroclaw, Województwo Dolnośląskie, Poland.
Maciej BehnkeAdam Mickiewicz University, Poznan, Poland.ORCID https://orcid.org/0000-0002-2455-4556
Johannes C EichstaedtStanford University, Stanford, CA, USA.
Soo Hyung KimChonnam National University, Gwangju, Jeollanam-do, Republic of Korea.
Tu N VuChonnam National University, Gwangju, Jeollanam-do, Republic of Korea.
Chirag RamanDelft University of Technology, Delft, Zuid-Holland, The Netherlands.
Julian TejadaFederal University of Sergipe, Sao Cristovao, Sergipe, Brazil.
Van-Thong HuynhFPT University, Hanoi, Vietnam.ORCID https://orcid.org/0000-0002-6240-2959
Guangyi ZhangHarvard Medical School, Boston, MA, USA.
Tanming CuiIndependent Researcher, State College, PA, USA.
Sharanyak PodderIndian Institute of Science Education and Research Bhopal, Bhopal, Madhya Pradesh, India.
Rushi ChavdaIndian Institute of Technology Bombay, Mumbai, Maharashtra, India.
Shubham PandeyIndian Institute of Technology Bombay, Mumbai, Maharashtra, India.
Arpit UpadhyayIndian Institute of Technology Bombay, Mumbai, Maharashtra, India.
Jorge I Padilla-BuriticaInstitución Universitaria ITM, Medellín, Colombia.
Carlos J Barrera CausilInstitución Universitaria ITM, Medellín, Colombia.
Linying JiMontana State University, Bozeman, MT, USA.
Felix DollackNara Institute of Science and Technology, Ikoma, Nara, Japan.
Kiyoshi KiyokawaNara Institute of Science and Technology, Ikoma, Nara, Japan.
Huakun LiuNara Institute of Science and Technology, Ikoma, Nara, Japan.
Monica Perusquia-HernandezNara Institute of Science and Technology, Ikoma, Nara, Japan.
Hideaki UchiyamaNara Institute of Science and Technology, Ikoma, Nara, Japan.ORCID https://orcid.org/0000-0002-6119-1184
Xin WeiNara Institute of Science and Technology, Ikoma, Nara, Japan.
Houwei CaoNew York Institute of Technology, Old Westbury, NY, USA.
Ziqing YangNew York Institute of Technology, Old Westbury, NY, USA.
Alessia IancarelliNortheastern University-Boston Campus, Boston, MA, USA.
Kieran McVeighNortheastern University-Boston Campus, Boston, MA, USA.
Yiyu WangNortheastern University-Boston Campus, Boston, MA, USA.
Isabel M BerwianPrinceton University, Princeton, NJ, USA.
Jamie C ChiuPrinceton University, Princeton, NJ, USA.
Dan-Mircea MireaPrinceton University, Princeton, NJ, USA.
Erik C NookPrinceton University, Princeton, NJ, USA.
Henna I VartiainenPrinceton University, Princeton, NJ, USA.ORCID https://orcid.org/0000-0002-5416-898X
Claire WhitingPrinceton University, Princeton, NJ, USA.
Young Won ChoThe Pennsylvania State University, University Park, PA, USA.
Sy-Miin ChowThe Pennsylvania State University, University Park, PA, USA.
Zachary F FisherThe Pennsylvania State University, University Park, PA, USA.
Yanling LiThe Pennsylvania State University, University Park, PA, USA.
Xiaoyue XiongThe Pennsylvania State University, University Park, PA, USA.
Yuqi ShenThe Pennsylvania State University, University Park, PA, USA.
Enzo TagliazucchiUniversidad Adolfo Ibanez, Penalolen, Chile.ORCID https://orcid.org/0000-0003-0421-9993
Leandro A BugnonResearch Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Santa Fe, Argentina.
Raydonal OspinaUniversidade Federal da Bahia, Salvador, Brazil.ORCID https://orcid.org/0000-0002-9884-9090
Nicolas M BrunoUniversity of Buenos Aires, Buenos Aires, Argentina.
Tomas A D'AmelioUniversity of Buenos Aires, Buenos Aires, Argentina.
Federico ZamberlanUniversity of Buenos Aires, Buenos Aires, Argentina.
Luis R Mercado DiazUniversity of Connecticut, Storrs, CT, USA.
Javier O Pinzon-ArenasUniversity of Connecticut, Storrs, CT, USA.
Hugo F Posada-QuinteroUniversity of Connecticut, Storrs, CT, USA.ORCID https://orcid.org/0000-0003-4514-4772
Maneesh BilalpurUniversity of Pittsburgh, Pittsburgh, PA, USA.
Saurabh HindujaUniversity of Akron, Akron, OH, USA.
Fernando Marmolejo-RamosFlinders University, Adelaide, South Australia, Australia.
Shaun CanavanUniversity of South Florida, Tampa, FL, USA.
Liza JivnaniUniversity of South Florida, Tampa, FL, USA.
Stanisław SaganowskiWrocław University of Science and Technology, Wroclaw, Województwo Dolnośląskie, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Researchers are increasingly using machine learning to study physiological markers of emotion. We evaluated the promises and limitations of this approach via a big team science competition. Twelve teams competed to predict self-reported affective experiences using a multi-modal set of peripheral nervous system measures. Models were trained and tested in multiple ways: with data divided by participants, targeted emotion, inductions, and time. In 100% of tests, teams outperformed baseline models that made random predictions. In 46% of tests, teams also outperformed baseline models that relied on the simple average of ratings from training datasets. More notably, results uncovered a methodological challenge: multiplicative constraints on generalizability. Inferences about the accuracy and theoretical implications of machine learning efforts depended not only on their architecture, but also how they were trained, tested, and evaluated. For example, some teams performed better when tested on observations from the same (vs. different) subjects seen during training. Such results could be interpreted as evidence against claims of universality. However, such conclusions would be premature because other teams exhibited the opposite pattern. Taken together, results illustrate how big team science can be leveraged to understand the promises and limitations of machine learning methods in affective science and beyond.

Indexed as

affective computingbig team scienceemotiongeneralizabilitymachine learningphysiology

Identifiers

PMID40568544
PMCPMC12187420

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.