Evidence map›Paper›PMID 41530609›Full record

ReviewPsychonomic bulletin & review2026

Phonetic cue weighting as auditory feature-based selective attention.

Chiung-Yu Chang, Lisa D Sanders

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Review in Psychonomic bulletin & review, 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

2 authors.

Chiung-Yu ChangDepartment of Psychological and Brain Sciences, University of Massachusetts Amherst, Tobin Hall, 135 Hicks Way, Amherst, MA, 01003, USA. chiungyuchan@umass.edu.ORCID https://orcid.org/0000-0003-3276-2600
Lisa D SandersDepartment of Psychological and Brain Sciences, University of Massachusetts Amherst, Tobin Hall, 135 Hicks Way, Amherst, MA, 01003, USA.ORCID https://orcid.org/0000-0001-6763-1462

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Listeners use multiple acoustic features, to different extents in different circumstances, in perceiving variable speech signals as streams of phonemes. One class of models conceptualizes this phonetic cue weighting as allocating different amounts of attention to different acoustic features (i.e., attentional theories). These theories posit or assume that similar neural mechanisms underlie phonetic cue weighting and a more general form of auditory feature-based selective attention. We describe multiple attentional theories, consider their relationship to other models of phoneme perception, and review related empirical evidence. In general, attentional theories are able to explain both training-induced and context-dependent cue reweighting. The neuroscientific literature on auditory feature-based selective attention suggests potential neural mechanisms of phonetic cue weighting, including at the single-neuron level. At least three challenges need to be met in order to ascribe all of cue weighting to auditory feature-based selective attention: (1) the complexity of continuous speech, (2) individual differences in cue weighting and selective attention abilities, and (3) any need for specialized neural systems in allocating attention to phonetic cues. To date, using attentional theories to understand speech perception has been fruitful, but more detailed and mechanistic models of feature-based selective attention are needed in order to define the extent to which the attention literature applies in full to cue weighting in speech perception.

Indexed as

AttentionCuesPhoneticsSpeech PerceptionHumansAuditory selective attentionFeature-based attentionIndividual differencesSpeech perception

Identifiers

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.