Evidence map›Paper›PMID 35663036›Full record

ReviewiScience2022

Toward understanding the communication in sperm whales.

Jacob Andreas, Gašper Beguš, Michael M Bronstein, Roee Diamant, Denley Delaney, Shane Gero, Shafi Goldwasser, David F Gruber, Sarah de Haas, Peter Malkin and 10 more

Abstract readReview
In one paragraph

Review in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Animal Welfare, Agency, and Animal-Computer Interaction.Animals : an open access journal from MDPI · 2025
    Article
  8. Article
  9. Vowel- and Diphthong-Like Spectral Patterns in Sperm Whale Codas.Open mind : discoveries in cognitive science · 2025
    Article
  10. Article
  11. Listen to Us: Perceptions of Animal Voice and Agency.Animals : an open access journal from MDPI · 2023
    Article
  12. Evidence from sperm whale clans of symbolic marking in non-human cultures.Proceedings of the National Academy of Sciences of the United States of America · 2022
    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

20 authors.

Jacob AndreasMIT CSAIL, Cambridge, MA, USA.
Gašper BegušDepartment of Linguistics, University of California, Berkeley, CA, USA.
Michael M BronsteinDepartment of Computer Science, University of Oxford, Oxford, UK.
Roee DiamantLeon H. Charney School of Marine Sciences, University of Haifa, Haifa, Israel.
Denley DelaneyExploration Technology Lab, National Geographic Society, Washington DC, USA.
Shane GeroDominica Sperm Whale Project, Roseau, Commonwealth of Dominica.
Shafi GoldwasserSimons Institute for the Theory of Computing, University of California, Berkeley, CA, USA.
David F GruberDepartment of Natural Sciences, Baruch College and The Graduate Center, PhD Program in Biology, City University of New York, New York, NY, USA.
Sarah de HaasGoogle Research, Mountain View, CA USA.
Peter MalkinGoogle Research, Mountain View, CA USA.
Nikolay PavlovProject CETI, New York, NY, USA.
Roger PayneProject CETI, New York, NY, USA.
Giovanni PetriISI Foundation, Turin, Italy.
Daniela RusMIT CSAIL, Cambridge, MA, USA.
Pratyusha SharmaMIT CSAIL, Cambridge, MA, USA.
Dan TchernovLeon H. Charney School of Marine Sciences, University of Haifa, Haifa, Israel.
Pernille TønnesenMarine Bioacoustics Lab, Zoophysiology, Department of Biology, Aarhus University, Aarhus, Denmark.
Antonio TorralbaMIT CSAIL, Cambridge, MA, USA.
Daniel VogtSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Robert J WoodSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning has been advancing dramatically over the past decade. Most strides are human-based applications due to the availability of large-scale datasets; however, opportunities are ripe to apply this technology to more deeply understand non-human communication. We detail a scientific roadmap for advancing the understanding of communication of whales that can be built further upon as a template to decipher other forms of animal and non-human communication. Sperm whales, with their highly developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent model for advanced tools that can be applied to other animals in the future. We outline the key elements required for the collection and processing of massive datasets, detecting basic communication units and language-like higher-level structures, and validating models through interactive playback experiments. The technological capabilities developed by such an undertaking hold potential for cross-applications in broader communities investigating non-human communication and behavioral research.

Indexed as

Artificial intelligenceEthologyLinguisticsNatural language processing

Identifiers

PMID35663036
PMCPMC9160774

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.