Social gating of sensory information during ongoing communication

The extended neural architecture of human attachment: An fMRI coordinate-based meta-analysis of affiliative studies

Functional imaging studies and clinical evidence indicate that cortical areas relevant to social cognition are closely integrated with evolutionarily conserved basal forebrain structures and neighboring regions, enabling human attachment and affiliative emotions. The neural circuitry of human affiliation is continually being unraveled as functional magnetic resonance imaging (fMRI) becomes increasingly prevalent, with studies examining human brain responses to various attachment figures. However, previous fMRI meta-analyses on affiliative stimuli have encountered challenges, such as low statistical power and the absence of robustness measures. To address these issues, we conducted an exhaustive coordinate-based meta-analysis of 79 fMRI studies, focusing on personalized affiliative stimuli, including one's infants, family, romantic partners, and friends. We employed complementary coordinate-based analyses (Activation Likelihood Estimation and Signed Differential Mapping) and conducted a robustness analysis of the results. Findings revealed cluster convergence in cortical and subcortical structures related to reward and motivation, salience detection, social bonding, and cognition. Our study thoroughly explores the neural correlates underpinning affiliative responses, effectively overcoming the limitations noted in previous meta-analyses. It provides an extensive view of the neural substrates associated with affiliative stimuli, illuminating the intricate interaction between cortical and subcortical regions. Our findings significantly contribute to understanding the neurobiology of human affiliation, expanding the known human attachment circuitry beyond the traditional basal forebrain regions observed in other mammals to include uniquely human isocortical structures.

The Neuroscience of Human and Artificial Intelligence Presence

Two decades of social neuroscience and neuroeconomics research illustrate the brain mechanisms that are engaged when people consider human beings, often in comparison to considering artificial intelligence (AI) as a nonhuman control. AI as an experimental control preserves agency and facilitates social interactions but lacks a human presence, providing insight into brain mechanisms that are engaged by human presence and the presence of AI. Here, I review this literature to determine how the brain instantiates human and AI presence across social perception and decision-making paradigms commonly used to realize a social context. People behave toward humans differently than they do toward AI. Moreover, brain regions more engaged by humans compared to AI extend beyond the social cognition brain network to all parts of the brain, and the brain sometimes is engaged more by AI than by humans. Finally, I discuss gaps in the literature, limitations in current neuroscience approaches, and how an understanding of the brain correlates of human and AI presence can inform social science in the wild.

Imitation encourages empathic capacity toward other individuals with physical disabilities

Many people have difficulty empathizing with others who have dissimilar characteristics, such as physical disabilities. We hypothesized that people with no disabilities imitating the movements of individuals with disabilities could improve the empathic capacity toward their difficulties. To evaluate this hypothesis, we used functional magnetic resonance imaging to measure the neural activity patterns of 26 healthy participants while they felt the difficulties of individuals with hemiplegia by adopting their perspective. The participants initially either imitated or observed hemiplegic hand movements shown in video clips. Subsequently, the videos were rewatched and their difficulties were rated. Analysis of the subjective rating scores indicated that after imitating the hemiplegic movements, the participants felt into the difficulties of hemiplegia better than if they simply observed them. The cross-validation approach of multivoxel pattern analyses demonstrated that the information regarding the effect of imitation on empathizing with the difficulties was represented in specific activation patterns of brain regions involved in the mirror neuron system and cognitive empathy by comparing to other conditions that did not contain the information. The cross-classification approach detected distinct activation patterns in the brain regions involved in affective and cognitive empathy, commonly while imitating the hemiplegic movements and subsequently feeling them. This indicated that the common representation related to these two types of empathy existed between imitating and feeling the hemiplegic movements. Furthermore, representational similarity analysis revealed that activity patterns in the anterior cingulate cortex linked to affective empathy tuned to the subjective assessment of hemiplegic movements. Our findings indicate that imitating the movements of individuals with hemiplegia triggered the affective empathic response and improved the cognitive empathic response toward them. The affective empathic response also linked the subjective assessment to the difficulties of hemiplegia, which was especially modulated by the experience of imitation. Imitating the movements of individuals with disabilities likely encourages empathic capacity from both affective and cognitive aspects, resulting in people with no disabilities precisely feeling what they are feeling.

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross-covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.