Evidence map›Paper›PMID 35213616›Full record

ArticlePloS one2022

Fine-grained time course of verb aspect processing.

Serge Minor, Natalia Mitrofanova, Gillian Ramchand

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Secondary imperfectivisation is reverbalisation.Morphology (Dordrecht, Netherlands) · 2025
    Article
  5. Article
  6. 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

3 authors.

Serge MinorDepartment of Language and Culture, UiT - The Arctic University of Norway, Tromsø, Norway.ORCID 0000-0003-1337-2412
Natalia MitrofanovaDepartment of Language and Culture, UiT - The Arctic University of Norway, Tromsø, Norway.
Gillian RamchandDepartment of Language and Culture, UiT - The Arctic University of Norway, Tromsø, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sentence processing is known to be highly incremental. Speakers make incremental commitments as the sentence unfolds, dynamically updating their representations based on the smallest pieces of information from the incoming speech stream. Less is known about linguistic processing on the sub-word level, especially with regard to abstract grammatical information. This study employs the Visual World Paradigm to investigate the processing of grammatical aspect by Russian-speaking adults (n = 124). Aspectual information is encoded relatively early within the Russian verb which makes this an ideal testing ground to investigate the incrementality of grammatical processing on the sub-word level. Participants showed preference for pictures of ongoing events when they heard sentences involving Imperfective verbs, and for pictures of completed events when they heard sentences involving Perfective verbs. Crucially, the analysis of the participants' eye-movements showed that they exhibited preference for the target picture already before they heard the end of the verb. Moreover, the latency of this effect depended on where the aspectual information was encoded within the verb. These results indicate that the processing and integration of grammatical aspect information can happen rapidly and incrementally on a fine-grained word-internal level. Methodologically, the study draws together a set of analytical techniques which can be fruitfully applied to the analysis of effect latencies in a wide range of studies within the Visual World eye-tracking paradigm.

Indexed as

LanguageSpeech PerceptionAdolescentAdultFemaleHumansMaleMiddle Aged

Identifiers

PMID35213616
PMCPMC8880397

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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.