Evidence map›Paper›PMID 41608879›Full record

ArticlePerspectives on psychological science : a journal of the Association for Psychological Science2026

Artificial Intelligence and the Psychology of Human Connection.

Ryan L Boyd, David M Markowitz

Abstract read
In one paragraph

Article in Perspectives on psychological science : a journal of the Association for Psychological Science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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.

Ryan L BoydDepartment of Psychology, University of Texas at Dallas.ORCID 0000-0002-1876-6050
David M MarkowitzDepartment of Communication, Michigan State University.ORCID 0000-0002-7159-7014

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As artificial intelligence (AI) becomes increasingly embedded in social life, understanding its interpersonal and psychological implications is urgent yet undertheorized. This article introduces the machine-integrated relational adaptation (MIRA) model, a transdisciplinary, middle-range theoretical framework that provides a foundational account of when, how, and why AI functions as a relational entity in human ecosystems. MIRA distinguishes two crucial roles of AI: relational partner (direct-interaction companion) and relational mediator (shaping human-to-human communication). Synthesizing psychosocial theories of human relationships, interpersonal communication theory, psycholinguistics, and human-computer interaction, MIRA structures AI's relational impact within antecedents, processes, moderators, and outcomes. Central to MIRA are four principles describing how AI fosters social adaptation: linguistic reciprocity, psychological proximity, interpersonal trust, and relational substitution versus enhancement. These principles illuminate how adaptive AI language and behavior can elicit emotional investment, simulate mutual understanding, or even supplant human interaction. MIRA integrates established theories-attachment theory, social exchange theory, and epistemic trust frameworks-and proposes a research agenda that bridges foundational psychology with emerging sociotechnical contexts. Rather than offering a deterministic view, MIRA provides a generative, testable structure for investigating the evolving role of AI in relational life and guiding future human-AI-connection research.

Indexed as

Artificial IntelligenceCommunicationInterpersonal RelationsSocial InteractionHumansModels, PsychologicalAI-mediated communicationartificial intelligencemachine-integrated relational adaptationrelationshipsverbal behavior

Identifiers

PMID41608879
PMCPMC12960742

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.