Evidence map›Paper›PMID 42312048›Full record

ArticleFrontiers in psychology2026

AI-mediated environments and the reconfiguration of symbolic convergence: cognitive implications for shared meaning formation.

Jun-Yi Chen, Ting-Yun Xu, Tun-Yuan Tien

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Jun-Yi ChenFuzhou University of International Studies and Trade, Fuzhou, China.
Ting-Yun XuXiamen University of Technology, Xiamen, China.
Tun-Yuan TienXiamen University, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence is increasingly embedded in everyday communication, not only as a tool for generating or modifying messages, but as a condition that reshapes how meanings become available, circulated, and stabilized. Existing research has often focused on trust, anthropomorphism, or user attitudes toward AI. Less attention has been given to how AI-mediated environments may alter the process through which meanings become collectively shared. This article addresses that gap by using Symbolic Convergence Theory to examine how AI-mediated environments reshape the process through which meanings become shared, stabilized, and cognitively available. It treats fantasy themes, fantasy types, symbolic convergence, and rhetorical vision as linked levels in the formation of shared symbolic reality. Building on this framework, the article argues that AI-mediated environments reconfigure symbolic convergence in three ways: by accelerating the production of symbolic materials, expanding their circulation, and reinforcing their stabilization through repetition and prioritization. These changes matter not only at the level of communication, but also at the level of cognition. When meanings are increasingly encountered in prestructured, repeated, and personalized forms, interpretation may become narrower, more dependent on familiar cues, and more shaped by shifting cognitive authority. Rather than treating AI simply as an artificial social actor, the article proposes that AI should also be understood as part of a transformed symbolic environment within which human cognition operates. This perspective offers a conceptually grounded account of how AI reshapes shared meaning formation and, in doing so, alters the conditions under which human interpretation and judgment take shape.

Indexed as

algorithmic mediationartificial intelligencecognitionhuman-AI interactioninterpretationshared meaningsocial meaningsymbolic convergence

Identifiers

PMID42312048
PMCPMC13269058

What OpenQuestion holds

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LicenceCC BY
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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.