Social neuroscience: the second phase

Establishing a role of the semantic control network in social cognitive processing: A meta-analysis of functional neuroimaging studies

The contribution and neural basis of cognitive control is under-specified in many prominent models of socio-cognitive processing. Important outstanding questions include whether there are multiple, distinguishable systems underpinning control and whether control is ubiquitously or selectively engaged across different social behaviours and task demands. Recently, it has been proposed that the regulation of social behaviours could rely on brain regions specialised in the controlled retrieval of semantic information, namely the anterior inferior frontal gyrus (IFG) and posterior middle temporal gyrus. Accordingly, we investigated for the first time whether the neural activation commonly found in social functional neuroimaging studies extends to these 'semantic control' regions. We conducted five coordinate-based meta-analyses to combine results of 499 fMRI/PET experiments and identified the brain regions consistently involved in semantic control, as well as four social abilities: theory of mind, trait inference, empathy and moral reasoning. This allowed an unprecedented parallel review of the neural networks associated with each of these cognitive domains. The results confirmed that the anterior left IFG region involved in semantic control is reliably engaged in all four social domains. This supports the hypothesis that social cognition is partly regulated by the neurocognitive system underpinning semantic control.

Sources of mu activity and their functional connectivity in perceiving complexities in reciprocal social interactive motion: An exploratory study using the 'Namaste' task

Cognitive processes underlying reciprocal social interactions are understood by the mechanism of embodiment, which is closely related to the mirror neuron system. Electroencephalographic (EEG) mu activity is a neural marker of the mirror neuron system. This study investigated the mu activity, localization of its sources and functional connectivity, which was induced while watching reciprocal social interactive motion across various degrees of complexity. Eighteen healthy participants underwent high-resolution EEG recording using 256-channels while they watched a specifically designed, culture specific, video task that showed two persons interacting socially using body gestures. Task complexity was determined by (1) whether there was an identical gestural response or a non-identical one; (2) whether the participant watched two persons interacting or was virtually involved in the interaction. Source localization and functional connectivity analysis was conducted for mu activity across various tasks. We also correlated mu activity and functional connectivity measures with serum BDNF. We found that spectral densities in various brain sources of mu activity and their increased functional connectivity distinguished identical and non-identical reciprocal expression observations, while mu suppression alone did not discriminate various degrees of complexities. These findings might have important implications in the understanding of mechanisms underlying mirror neuron dysfunction in various psychiatric disorders.

Male = Science, Female = Humanities

The gender-science stereotype of associating males, rather than females, with science is pervasive and influential. The present study challenged the common-sense assumption that it is environment that leads to the gender-science stereotype by conducting a genetically informative study. A total of 304 pairs of twins (152 monozygotic [MZ] and 152 dizygotic [DZ]) completed explicit and implicit gender-science stereotype measures twice across 2 years. Results showed that both explicit and implicit gender-science stereotypes were heritable, with significant nonshared environmental influence. Moreover, genetic and nonshared environmental factors influencing the explicit gender-science stereotype also affected the implicit gender-science stereotype to some extent. These findings have important implications for understanding the nature of the gender-science stereotype and implicit social cognition.

Systematic analysis of video data from different human-robot interaction studies: a categorization of social signals during error situations

Human-robot interactions are often affected by error situations that are caused by either the robot or the human. Therefore, robots would profit from the ability to recognize when error situations occur. We investigated the verbal and non-verbal social signals that humans show when error situations occur in human-robot interaction experiments. For that, we analyzed 201 videos of five human-robot interaction user studies with varying tasks from four independent projects. The analysis shows that there are two types of error situations: social norm violations and technical failures. Social norm violations are situations in which the robot does not adhere to the underlying social script of the interaction. Technical failures are caused by technical shortcomings of the robot. The results of the video analysis show that the study participants use many head movements and very few gestures, but they often smile, when in an error situation with the robot. Another result is that the participants sometimes stop moving at the beginning of error situations. We also found that the participants talked more in the case of social norm violations and less during technical failures. Finally, the participants use fewer non-verbal social signals (for example smiling, nodding, and head shaking), when they are interacting with the robot alone and no experimenter or other human is present. The results suggest that participants do not see the robot as a social interaction partner with comparable communication skills. Our findings have implications for builders and evaluators of human-robot interaction systems. The builders need to consider including modules for recognition and classification of head movements to the robot input channels. The evaluators need to make sure that the presence of an experimenter does not skew the results of their user studies.

Social neuroscience in psychiatry: unravelling the neural mechanisms of social dysfunction

Social neuroscience is a flourishing, interdisciplinary field that investigates the underlying biological processes of social cognition and behaviour. The recent application of social neuroscience to psychiatric research advances our understanding of various psychiatric illnesses that are characterized by impairments in social cognition and social functioning. In addition, the upcoming line of social neuroscience research provides new techniques to design and evaluate treatment interventions that are aimed at improving patients' social lives. This review provides a contemporary overview of social neuroscience in psychiatry. We draw together the major findings about the neural mechanisms of social cognitive processes directed at understanding others and social interactions in psychiatric illnesses and discuss their implications for future research and clinical practice.

On social neuroscience methodologies and their applicability to group processes and intergroup relations

Group processes and intergroup relations are one of the most important topics examined by social psychologists. Recent advancements in social neuroscience methodologies provide valuable insight into these processes by allowing researchers to examine different psychological phenomena via neural processes that instantiate them while individuals interact with ingroup and outgroup members. This includes responses that occur outside conscious awareness or are deemed undesirable to overtly express. The purpose of this review is to provide an overview of the different social neuroscience methodologies that afford these possibilities. Specifically, functional magnetic resonance imaging (fMRI), electroencephalography (EEG), functional near infrared spectroscopy (fNIRS), transcranial magnetic stimulation (TMS), and genetic approaches will be discussed. Each section includes a discussion of what the methodology is and how it is used to assess neural function. A secondary goal of the review is to highlight recent studies that have utilized the aforementioned tools to better understand intergroup processes and interactions. Throughout, advantages and limitations of each approach are discussed, particularly with respect to the study of group processes and intergroup relations.

Capturing the essence of decision making should not be oversimplified

Bentley et al. propose a thought-provoking approach to the question of causal factors underlying human choice behavior. Their map model is interesting, but too simplified to capture the essence of decision making. They disregard, among other matters, qualitative differences between various subcategories of social influences, and the role of neurobiological factors engaged in interdependent individual and social decision-making processes.

Addiction science: Uncovering neurobiological complexity

Until very recently addiction-research was limited by existing tools and strategies that were inadequate for studying the inherent complexity at each of the different phenomenological levels. However, powerful new tools (e.g., optogenetics and designer drug receptors) and high throughput protocols are starting to give researchers the potential to systematically interrogate "all" genes, epigenetic marks, and neuronal circuits. These advances, combined with imaging technologies (both for preclinical and clinical studies) and a paradigm shift toward open access have spurred an unlimited growth of datasets transforming the way we investigate the neurobiology of substance use disorders (SUD) and the factors that modulate risk and resilience. This article is part of a Special Issue entitled 'NIDA 40th Anniversary Issue'.

Toward understanding social cues and signals in human-robot interaction: effects of robot gaze and proxemic behavior

As robots are increasingly deployed in settings requiring social interaction, research is needed to examine the social signals perceived by humans when robots display certain social cues. In this paper, we report a study designed to examine how humans interpret social cues exhibited by robots. We first provide a brief overview of perspectives from social cognition in humans and how these processes are applicable to human–robot interaction (HRI). We then discuss the need to examine the relationship between social cues and signals as a function of the degree to which a robot is perceived as a socially present agent. We describe an experiment in which social cues were manipulated on an iRobot Ava^TM mobile robotics platform in a hallway navigation scenario. Cues associated with the robot’s proxemic behavior were found to significantly affect participant perceptions of the robot’s social presence and emotional state while cues associated with the robot’s gaze behavior were not found to be significant. Further, regardless of the proxemic behavior, participants attributed more social presence and emotional states to the robot over repeated interactions than when they first interacted with it. Generally, these results indicate the importance for HRI research to consider how social cues expressed by a robot can differentially affect perceptions of the robot’s mental states and intentions. The discussion focuses on implications for the design of robotic systems and future directions for research on the relationship between social cues and signals.

Implicit and explicit social mentalizing: dual processes driven by a shared neural network

Recent social neuroscientific evidence indicates that implicit and explicit inferences on the mind of another person (i.e., intentions, attributions or traits), are subserved by a shared mentalizing network. Under both implicit and explicit instructions, ERP studies reveal that early inferences occur at about the same time, and fMRI studies demonstrate an overlap in core mentalizing areas, including the temporo-parietal junction (TPJ) and the medial prefrontal cortex (mPFC). These results suggest a rapid shared implicit intuition followed by a slower explicit verification processes (as revealed by additional brain activation during explicit vs. implicit inferences). These data provide support for a default-adjustment dual-process framework of social mentalizing.