Evidence map›Paper›PMID 38132836›Full record

ArticleJournal of Intelligence2023

The Visual Advantage Effect in Comparing Uni-Modal and Cross-Modal Probabilistic Category Learning.

Xunwei Sun, Qiufang Fu

Open access · goldAbstract read
In one paragraph

Article in Journal of Intelligence, 2023. 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, top 79% of its field
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, 0 citations in OpenAlex.

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 at 2 institutions in 1 country.

Xunwei SunState Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Qiufang FuState Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0002-7993-8610
Institute of Psychology, Chinese Academy of Sciences · CNPeking University · CN

Funding

the National Key Research and Development Program of China 2021ZD0204202the National Natural Science Foundation of China, and the German Research Foundation NSFC 62061136001/DFG TRR-169
6 · The paper itself

Abstract

People rely on multiple learning systems to complete weather prediction (WP) tasks with visual cues. However, how people perform in audio and audiovisual modalities remains elusive. The present research investigated how the cue modality influences performance in probabilistic category learning and conscious awareness about the category knowledge acquired. A modified weather prediction task was adopted, in which the cues included two dimensions from visual, auditory, or audiovisual modalities. The results of all three experiments revealed better performances in the visual modality relative to the audio and audiovisual modalities. Moreover, participants primarily acquired unconscious knowledge in the audio and audiovisual modalities, while conscious knowledge was acquired in the visual modality. Interestingly, factors such as the amount of training, the complexity of visual stimuli, and the number of objects to which the two cues belonged influenced the amount of conscious knowledge acquired but did not change the visual advantage effect. These findings suggest that individuals can learn probabilistic cues and category associations across different modalities, but a robust visual advantage persists. Specifically, visual associations can be learned more effectively, and are more likely to become conscious. The possible causes and implications of these effects are discussed.

Indexed as

cross-modal learningimplicit learningweather prediction task

Identifiers

PMID38132836
PMCPMC10744040
OpenAlexW4389057437

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.