Evidence map›Paper›PMID 38746113›Full record

ArticleResearch square2024

Can human intelligence safeguard against artificial intelligence? Exploring individual differences in the discernment of human from AI texts.

Jason Chein, Steven Martinez, Alexander Barone

Abstract readPreprint
In one paragraph

Article in Research square, 2024. 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

5 · Who and what money

Authors and funding

3 authors.

Jason CheinTemple University.
Steven MartinezTemple University.
Alexander BaroneTemple University.

Funding

Origins And Outcomes Of Smartphone And Social Media Habits Across Development SupplementR01HD098097 · NICHD · TEMPLE UNIV OF THE COMMONWEALTH · PI CHEIN, JASON M · 2021 to 2025
$2.1M
NICHD NIH HHS R01 HD098097
6 · The paper itself

Abstract

Artificial intelligence (AI) models can produce output that closely mimics human-generated content. We examined individual differences in the human ability to differentiate human- from AI-generated texts, exploring relationships with fluid intelligence, executive functioning, empathy, and digital habits. Overall, participants exhibited better than chance text discrimination, with substantial variation across individuals. Fluid intelligence strongly predicted differences in the ability to distinguish human from AI, but executive functioning and empathy did not. Meanwhile, heavier smartphone and social media use predicted misattribution of AI content (mistaking it for human). Determinations about the origin of encountered content also affected sharing preferences, with those who were better able to distinguish human from AI indicating a lower likelihood of sharing AI content online. Word-level differences in linguistic composition of the texts did not meaningfully influence participants' judgements. These findings inform our understanding of how individual difference factors may shape the course of human interactions with AI-generated information.

Identifiers

PMID38746113
PMCPMC11092869

What OpenQuestion holds

Textmetadata
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.