Matthew J Vowels

Dados Biográficos

ID768603
NOMEMatthew J Vowels
PRENOMESMatthew J
SOBRENOMEVowels
ASSINATURAVOWELS M J
AFILIAÇÕESThe Sense Innovation and Research Center
ORCID0000-0002-8811-1156
VERIFICADOSim
TOTAL DE OBRAS7
TOTAL DE CITAÇÕES8
TOTAL COMO AUTOR7
TOTAL COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO2021
ANO MAIS RECENTE DE PUBLICAÇÃO2026
ÍNDICE H2

  • From Friends to Lovers

    Open AccessLaura M Vowels, Valérie Lapointe et al.ARTICLEArchives of Sexual Behavior2026

    Artificial intelligence (AI) companions are increasingly used for social, emotional, and sometimes romantic fulfillment, raising questions about how people perceive and engage with these technologies. This study explored attitudes toward AI companionship and whether interviewer type (AI, human, or unmoderated) affects disclosure or engagement. A mixed-methods design was employed with 135 adult participants, who completed structured interviews inc…

  • User-avatar bond as diagnostic indicator for gaming disorder

    Open AccessAlexandre Infanti, Alessandro Giardina et al.ARTICLEJournal of Behavioral Addictions2024

    In their study, Stavropoulos et al. (2023) capitalized on supervised machine learning and a longitudinal design and reported that the User-Avatar Bond could be accurately employed to detect Gaming Disorder (GD) risk in a community sample of gamers. The authors suggested that the User-Avatar Bond is a “digital phenotype” that could be used as a diagnostic indicator for GD risk. In this commentary, our objectives are twofold: (1) to underscore the …

  • Disentangling the good, the bad, and the neutral of co-present mobile phone use

    Open AccessMichal Frackowiak, Pascale Sophie Russell et al.ARTICLEMobile Media & Communication2024Referências: 4

    Studies show that the use of smartphones in the presence of a partner may result in lower relationship quality, intimacy, and increased conflict. Such co-present phone use, often referred to as “phubbing” or “technoference,” has been problematized in the literature. However, few studies have hitherto explored the possibility that using phones in each other's presence may not cause any relational harm and might even support relationship maintenanc…

  • A Machine Learning Approach to Predicting Perceived Partner Support From Relational and Individual Variables

    Open AccessLaura M Vowels, Matthew J Vowels et al.ARTICLESocial Psychological and…2023

    Perceiving one’s partner as supportive is considered essential for relationships, but we know little about which factors are central to predicting perceived partner support. Traditional statistical techniques are ill-equipped to compare a large number of potential predictor variables and cannot answer this question. This research used machine learning analysis (random forest with Shapley values) to identify the most salient self-report predictors…

  • Typical Yet Unlikely and Normally Abnormal

    Open AccessMatthew J VowelsARTICLEStatistics Politics and Policy2023Referências: 3

    Normality, in the colloquial sense, has historically been considered an aspirational trait, synonymous with ideality. The arithmetic average and, by extension, statistics including linear regression coefficients, have often been used to characterize normality, and are often used as a way to summarize samples and identify outliers. We provide intuition behind the behavior of such statistics in high dimensions, and demonstrate that even for dataset…

  • Identifying the strongest self-report predictors of sexual satisfaction using machine learning

    Open AccessLaura M Vowels, Matthew J Vowels et al.ARTICLEJournal of Social and Personal…2022Citada por: 6Referências: 48

    Sexual satisfaction has been robustly associated with relationship and individual well-being. Previous studies have found several individual (e.g., gender, self-esteem, and attachment) and relational (e.g., relationship satisfaction, relationship length, and sexual desire) factors that predict sexual satisfaction. The aim of the present study was to identify which variables are the strongest, and the least strong, predictors of sexual satisfactio…

  • Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity

    Open AccessLaura M Vowels, Matthew J Vowels et al.ARTICLEThe Journal of Sex Research2021Citada por: 2Referências: 7

    Infidelity can be a disruptive event in a romantic relationship with a devastating impact on both partners' well-being. Thus, there are benefits to identifying factors that can explain or predict infidelity, but prior research has not utilized methods that would provide the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and …