Evidence map›Paper›PMID 42160139›Full record

ArticleJournal of behavioral addictions2026

Animal models relevant to digital technology-based disorders.

Shu K E Tam, Benjamin Becker

Abstract read
In one paragraph

Article in Journal of behavioral addictions, 2026. 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

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.

Shu K E Tam1Duke Kunshan University-The First People's Hospital of Kunshan Joint Brain Sciences Laboratory, Kunshan, Jiangsu, People's Republic of China.ORCID https://orcid.org/0000-0003-3419-896X
Benjamin Becker3Department of Psychology, The University of Hong Kong, Hong Kong, People's Republic of China.ORCID https://orcid.org/0000-0002-9014-9671

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Animal models utilizing Pavlovian and instrumental conditioning paradigms have advanced our understanding of the mechanisms underlying drug addiction and certain behavioral addiction such as gambling disorder, but so far there is no animal model for digital technology-based disorders such as (Internet) gaming disorder, which is an emerging issue in our digital society. Casile et al. (2025) introduced a rat touchscreen paradigm recapitulating several key phenotypes relevant to gaming disorder, including preoccupation with the touchscreen and behavioral persistence despite non-reinforcement. However, the paradigm's reliance on food reinforcers limits translational relevance. We suggest integrating Casile et al. touchscreen paradigm with non-food operant paradigms (e.g., light self-administration), to dissociate any intrinsic reinforcing effects of touchscreen interaction and response-contingent sensory feedback from hedonic values of food. Potential differences in sensory habituation between rats and mice highlight the need for cross-species comparisons, to distinguish species-specific responses from general behavioral mechanisms in non-human animals and humans. While animal models cannot fully capture the multifaceted nature of digital technology-based disorders, they provide simplified models to examine how behavioral persistence can be shaped and maintained by non-food, non-drug reinforcement contingencies without the influence of cultural and social factors.

Indexed as

Behavior, AnimalConditioning, OperantDisease Models, AnimalInternet Addiction DisorderReinforcement, PsychologyAnimalsDigital MediaHumansMiceRatsdigital devicesgaming disorderinstrumental conditioninglightmicerats

Identifiers

PMID42160139
PMCPMC13371727

What OpenQuestion holds

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LicenceCC BY-NC
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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.