Evidence map›Paper›PMID 41224852›Full record

ArticleScientific reports2025

Modeling gaze behavior with continuous-time markov chains to investigate social attention dynamics in autism.

R Bruschetta, F I Famà, L Spadaro, E Leonardi, C Carrozza, S Aiello, A Campisi, M Mastrogiuseppe, S Campisi, B Chakrabarti and 6 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

16 authors.

R Bruschetta *National Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
F I Famà *National Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
L SpadaroNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
E LeonardiNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
C CarrozzaNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
S AielloNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
A CampisiNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
M MastrogiuseppeNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
S CampisiNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
B ChakrabartiCentre for Autism, School of Psychology & Clinical Language Sciences, University of Reading, Reading, UK.
L RutaNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy. liliana.ruta@irib.cnr.it.
G PioggiaNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy. giovanni.pioggia@irib.cnr.it.
A De GaetanoNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
A BorriNational Research Council of Italy, IASI, Rome, Italy.
G TartariscoNational Research Council of Italy, IRIB, Istituto Marino, Via Leanza, 98164, Mortelle, Messina, Italy.
NEST Team

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study introduces a novel methodological framework combining continuous-time Markov chains and principal component analysis (PCA) to model and investigate gaze behavior in young children observing naturalistic social interactions. By quantifying transition propensities between areas of interest (AOIs), this approach enables a dynamic, data-driven analysis of gaze patterns beyond static fixation metrics. We applied this framework to eye-tracking data from children with autism spectrum condition (ASC) and neurotypical (NT) peers as they watched scenes of a child and an adult engaged in interactive play, involving turn-taking and reciprocal imitation. The stimuli, designed to ensure ecological validity, depicted sensory social routines (SSRs) with songs and shared play with musical instruments, allowing exploration of gaze dynamics in both dyadic and triadic social contexts. Results revealed distinct gaze transition profiles in ASC children, characterized by more frequent disengagement from socially salient AOIs and reduced re-orientation to faces following non-social fixations. In contrast, NT children exhibited frequent gaze alternation between faces and triangulation with objects, supporting joint attention and reciprocal engagement. Additionally, ASC participants were more likely to enter and persist in non-social states, especially during object-centered trials, whereas NT peers showed consistent transitions toward socially meaningful targets. These findings highlight the relevance of capturing the temporal patterns of visual engagement in autism, revealing how moment-to-moment gaze transition dynamics reflect underlying differences in social motivation, attentional control, and sensory processing. The proposed framework provides a powerful tool for characterizing individual differences in gaze organization and holds promise for advancing biomarker identification in neurodevelopmental research.

Indexed as

AttentionAutistic DisorderFixation, OcularSocial BehaviorChildChild, PreschoolEye-Tracking TechnologyFemaleHumansMaleMarkov ChainsPrincipal Component AnalysisAutismEye-trackingGaze patternsMarkov chainsPrincipal component analysisSocial attention

Identifiers

PMID41224852
PMCPMC12612279

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.