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Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen’s d, the power analysis indicated that a high sample size ( $$N> 150$$ ) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.

Primal world beliefs (“primals”) capture individuals’ basic understanding of what sort of world is this. For example, is the world dangerous or safe? Dull or enticing? Theory and research on primals have advanced over the last 10 years in personality and social psychology (e.g., Clifton et al., 2019), but the concept of primals is nascent in the study of development. How do primals form? Machine learning analytic techniques that can simultaneously examine, evaluate, and parse the predictive power of numerous individual, parent, family, and cultural factors at once, and therefore provide greater predictive flexibility than traditional analytic techniques, can overcome limitations of traditional inferential approaches (Dwyer et al., 2018). Utilizing machine learning techniques could allow us to identify the unique combination of individual, parent, family, and cultural characteristics experienced in childhood and adolescence that predict primals in early adulthood across cultures.

Despite the large body of literature on the relationship between perceived faces' aesthetic and trustworthiness, little is known about (1) the implication of the presence of a face in a surrounding context, (2) the role of ethnicity, gender, and age on the strength of the relationship, and (3) if are there any differences between implicit and explicit estimation of the strength of the effect.

The goal of this study is to empirically evaluate how different components of the alexithymia construct and the interoceptive construct are related to each other. More specifically, we want to investigate new evidence regarding the magnitude and the directionality (positive or negative association) of the relationship between alexithymia and interoception. Following this, we will conduct a cross-sectional study within three different samples: a sample of young adults (18-35) from Italy, a sample of Singaporean university students, and a sample of young adults (25-30), residing in U.S.A. and recruited through the MTurk platform. Each sample will be assessed through different self-report scales for levels of alexithymia, interoceptive accuracy and interoceptive attention. Our study aims at clarifying the role of interceptive information in building subjective emotional awareness, which seem to be disrupted in individuals with high alexithymia. It is expected that the magnitude and directionality of the relationship between alexithymia and interoception to be independent from demographic variables.

Human faces capture attention, provide information about group belonging, and elicit automatic prepared responses. Early experiences with other-race faces play a critical role in acquiring face expertise, but the exact mechanism through which early experience exerts its influence is still to be elucidated. Genetic factors and a multi-ethnic context are likely involved, but their specific influences have not been explored. This study investigated how oxytocin receptor gene (OXTR) genotypes and childcare experience interacted to regulate face categorization in adults. Information about single nucleotide polymorphisms of OXTR (rs53576) and experiences with own- and other-race child caregivers was collected from 89 Singaporean adults, who completed a visual categorization task with own- versus other-race faces. Participants were grouped into A/A homozygotes and G carriers and assigned a score to account for their type of child caregiver experience. A multivariate linear regression model was used to estimate the effect of genetic group, child caregiver experience, and their interaction on categorization reaction time. A significant interaction of genetic group and child caregiver experience (t = 2.48, p = 0.015), as well as main effects of both genetic group (t = -2.17, p = 0.033) and child caregiver experience (t = -4.29, p

Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen's d, the power analysis indicated that a high sample size ([Formula: see text]) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.