Decoding Social Authenticity: Distributed Neural Patterns for Laughter Emerge in the First Year of Life

Decoding Social Authenticity: Distributed Neural Patterns for Laughter Emerge in the First Year of Life | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 October 2025 V1 Latest version Share on Decoding Social Authenticity: Distributed Neural Patterns for Laughter Emerge in the First Year of Life Authors : Addison Billing 0000-0003-3547-2983 [email protected] , Addison D N Billing , Eleanor S Smith , Bowen Xiao , Emilia Butters , Oskar Lacina- Moser , Rebecca P Lawson , Robert J Cooper , and Sophie K Scott Authors Info & Affiliations https://doi.org/10.22541/au.176125900.09139093/v1 280 views 110 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The infant brain's ability to distinguish authentic from posed emotional inputs is a cornerstone of social development. Using a pioneering application of multivariate pattern analysis (MVPA) to high-density diffuse optical tomography (HD-DOT) data, we provide the first evidence that distinct, distributed patterns of cortical activity encode the authenticity of laughter in 6-8-month-old infants. We recorded cortical responses using HD-DOT while infants listened to recordings of spontaneous (authentic) and volitional (social) laughter. A linear support-vector machine (SVM) classifier, trained on the distributed patterns of hemodynamic activity, successfully decoded the stimulus category with 77% accuracy. Feature importance mapping revealed that information within the superior temporal and prefrontal cortices was most critical for this classification. This study provides a powerful proof-of-concept for applying MVPA to infant HD-DOT data, opening new avenues for investigating the high-dimensional neural representations underlying cognitive development. Scientifically, our findings offer the earliest evidence that the infant brain is already tuned to the nuanced social information that distinguishes authentic emotional expressions, providing a critical precursor to the mature social perception network. Supplementary Material File (decoding social authenticity_ab.pdf) Download 2.20 MB Information & Authors Information Version history V1 Version 1 23 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords fnirs hd-dot infancy laughter Authors Affiliations Addison Billing 0000-0003-3547-2983 [email protected] View all articles by this author Addison D N Billing Department of Clinical Neurosciences, University of Cambridge DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London View all articles by this author Eleanor S Smith Department of Psychology, University of Cambridge View all articles by this author Bowen Xiao Department of Psychology, University of Cambridge View all articles by this author Emilia Butters Department of Electrical Engineering, University of Cambridge View all articles by this author Oskar Lacina- Moser View all articles by this author Rebecca P Lawson Department of Psychology, University of Cambridge NHS View all articles by this author Robert J Cooper DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London View all articles by this author Sophie K Scott Institute of Cognitive Neuroscience, University College London View all articles by this author Metrics & Citations Metrics Article Usage 280 views 110 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Addison Billing, Addison D N Billing, Eleanor S Smith, et al. Decoding Social Authenticity: Distributed Neural Patterns for Laughter Emerge in the First Year of Life. Authorea . 23 October 2025. DOI: https://doi.org/10.22541/au.176125900.09139093/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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