A Relevance Rate Model of the affective mechanisms linking social media use to later attentional disruption
Abstract
Social media use is increasingly suspected of harming attention, but the evidence remains too limited to draw any firm conclusions. One obstacle is the lack of a framework specifying how such use might affect attention afterwards, without any device. To address this gap, we draw on emotional attention, the affective mechanism that prioritises what matters to individuals. The Relevance Rate Model rests on the relevance rate, the frequency with which an environment delivers concern-relevant stimuli. Drawing on predictive coding, we suggest that social media raises the rate that users expect, and that this expectation may persist after each session and, with repeated use, across daily life. When an activity delivers less concern-relevant information than expected, sustained attention to the ongoing task may be disrupted as emotional attention is recruited to address the shortfall. The model specifies four measurable parameters of use, from which we derive a research agenda.
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BibTeX
@misc{fournier2026relevance,
title = {{A Relevance Rate Model of the affective mechanisms linking social media use to later attentional disruption}},
author = {Hippolyte Fournier and eva pool and David Sander and Elise Dan-Glauser},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/7hn6d_v1},
url = {https://osf.io/preprints/psyarxiv/7hn6d_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Relevance Rate Model of the affective mechanisms linking social media use to later attentional disruption
AU - Hippolyte Fournier
AU - eva pool
AU - David Sander
AU - Elise Dan-Glauser
PY - 2026
DA - 2026/10/23
PB - PsyArXiv
DO - 10.31234/osf.io/7hn6d_v1
UR - https://osf.io/preprints/psyarxiv/7hn6d_v1/
AB - Social media use is increasingly suspected of harming attention, but the evidence remains too limited to draw any firm conclusions. One obstacle is the lack of a framework specifying how such use might affect attention afterwards, without any device. To address this gap, we draw on emotional attention, the affective mechanism that prioritises what matters to individuals. The Relevance Rate Model rests on the relevance rate, the frequency with which an environment delivers concern-relevant stimuli. Drawing on predictive coding, we suggest that social media raises the rate that users expect, and that this expectation may persist after each session and, with repeated use, across daily life. When an activity delivers less concern-relevant information than expected, sustained attention to the ongoing task may be disrupted as emotional attention is recruited to address the shortfall. The model specifies four measurable parameters of use, from which we derive a research agenda.
ER -