Evidence map›Paper›PMID 40161574›Full record

ArticleFrontiers in neuroscience2025

Understanding speech in "noise" or free energy minimization in the soundscapes of the anthropocene.

Daniel J Strauss, Alexander L Francis, Zeinab Schäfer, Matthias Latzel, Farah I Corona-Strauss, Stefan Launer

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 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. 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

6 authors.

Daniel J Strauss *Systems Neuroscience and Neurotechnology Unit, Faculty of Medicine, Saarland University, and School of Engineering, htw saar, Homburg/Saar, Germany.
Alexander L Francis *Speech Perception and Cognitive Effort Lab, Department of Speech, Language and Hearing Sciences, Purdue University, West Lafayette, IN, United States.
Zeinab SchäferSystems Neuroscience and Neurotechnology Unit, Faculty of Medicine, Saarland University, and School of Engineering, htw saar, Homburg/Saar, Germany.
Matthias LatzelSonova AG, Stäfa, Switzerland.
Farah I Corona-StraussSystems Neuroscience and Neurotechnology Unit, Faculty of Medicine, Saarland University, and School of Engineering, htw saar, Homburg/Saar, Germany.
Stefan LaunerSonova AG, Stäfa, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Listening to speech in the presence of irrelevant sounds is ubiquitous in the modern world, but is generally acknowledged to be both effortful and unpleasant. Here we argue that this problem arises largely in circumstances that our human auditory system has not evolved to accommodate. The soundscapes of the Anthropocene are frequently characterized by an overabundance of sound sources, the vast majority of which are functionally irrelevant to a given listener. The problem of listening to speech in such environments must be solved by an auditory system that is not optimized for this task. Building on our previous work linking attention to effortful listening and incorporating an active inference approach, we argue that the answers to these questions have implications not just for the study of human audition. They are also significant for the development and broad awareness of hearing aids and cochlear implants, as well as other auditory technologies such as earbuds, immersive auditory environments, and systems for human-machine interaction.

Indexed as

attentionevolutionfree energy principlehearinglistening effortnoisespeech

Identifiers

PMID40161574
PMCPMC11949948

What OpenQuestion holds

Textmetadata
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.