Affective Social Interactions Research Articles

Neural Correlates of Decisions in Quasi-Realistic, Affective Social Interactions in Individuals With Violence-Related Socialization.

Appropriate social behavior in aggressive-provocative interactions is a prerequisite for a peaceful life. In previous research, the dysfunctions of the control of aggression were suggested to be modulated by enhanced bottom-up (sub-cortically driven) and reduced top-down (iso-cortical frontal) processing capability. In the present study, two groups of individuals with enhanced (EG) and normal (NG) experiences of violent acts during their socialization made binary behavioral decisions in quasi-realistic social interactions. These interactions were presented in short video clips taken from a first-person perspective. The video clips showed social interaction scenarios oriented on realistic everyday life situations. The behavioral data supported the distinct affective qualities of three categories of social interactions. These categories were labeled as aggressive–provocative, social–positive, and neutral–social interactions. Functional neuroimaging data showed extended activation patterns and higher signal intensity for the NG compared to the EG in the lateral inferior frontal brain regions for the aggressive provocative interactions. Furthermore, the peri-aqueductal gray (PAG) produced enhanced activations for the affective interaction scenarios (i.e., aggressive-provocative and social-positive) in both groups and as a trend with the medium effect size for the neutral interactions in the EG. As the individuals in the EG did not show open aggression during the functional MRIA (fMRI) investigation, we concluded that they applied individual self-control strategies to regulate their aggressive impulses immediately. These strategies appeared to be top-down regulated through the dorsal frontal brain areas. The predominant recruitment of the heteromodal cortices during the neural processing of complex social interactions pointed to the important role of the learning history of individuals and their socialization with differing levels of violent experiences as crucial modulators in convicts. Our data suggest that building or strengthening the association between prototypical social contexts (e.g., aggressive-provocative interactions) and appropriate behaviors as a response to it provides a promising approach to successfully re-socialize people with a delinquent history.

Comparación de los efectos en tratamientos de autismo a partir de modelos conductuales y psicoanalíticos

In this paper we reviewed the autism treatments efficiency results based on behavioral and psychoanalytic models reported within the last six years. Since both models are based on different conceptions of treatment direction in autism cases, the results were compared between them to determinate their general differences. Psychoanalytic models have multiple autism meanings, some of these theories consider it as part of psychoses while others think about it as a different and autonomous clinical structure. Nonetheless, generally these models are based on symbolic conditions and family history to focus treatment on affective social interactions and alterity acknowledge. Meanwhile, behavioral models are based on environmental, genetic and social conditions to decrease the presence of self-injurious behavior through social rewards during therapy and to impact the family context thanks to socioemotional development. Coincidentally psychoanalytic and behavioral models ensure in their interventions to modify the presence of angst in patients and in their relatives.

Affective and cooperative social interactions modulate effective connectivity within and between the mirror and mentalizing systems.

Decoding the meaning of others' actions, a crucial step for social cognition, involves different neural mechanisms. While the "mirror" and "mentalizing" systems have been associated with, respectively, the processing of biological actions versus more abstract information, their respective contribution to intention understanding is debated. Processing social interactions seems to recruit both neural systems, with a different weight depending on cues emphasizing either shared action goals or shared mental states. We have previously shown that observing cooperative and affective social interactions elicits stronger activity in key nodes of, respectively, the mirror (left posterior superior temporal sulcus (pSTS), superior parietal cortex (SPL), and ventral/dorsal premotor cortex (vPMC/dPMC)) and mentalizing (ventromedial prefrontal cortex (vmPFC)) systems. To unveil their causal organization, we investigated the effective connectivity underlying the observation of human social interactions expressing increasing cooperativity (involving left pSTS, SPL, and vPMC) versus affectivity (vmPFC) via dynamic causal modeling in 36 healthy human subjects. We found strong evidence for a model including the pSTS and vPMC as input nodes for the observed interactions. The extrinsic connectivity of this model undergoes oppositely valenced modulations, with cooperativity promoting positive modulations of connectivity between pSTS and both SPL (forward) and vPMC (mainly backward), and affectivity promoting reciprocal positive modulations of connectivity between pSTS and vmPFC (mainly backward). Alongside fMRI data, such divergent effective connectivity suggests that different dimensions underlying the processing of social interactions recruit distinct, although strongly interconnected, neural pathways associated with, respectively, the bottom-up visuomotor processing of motor intentions, and the top-down attribution of affective/mental states.

EEG sensorimotor correlates of translating sounds into actions

Understanding the actions of others is a necessary foundational cornerstone for effective and affective social interactions. Such understanding may result from a mapping of observed actions as well as heard sounds onto one's own motor representations of those events. To examine the electrophysiological basis of action-related sounds, EEG data were collected in two studies from adults who were exposed to auditory events in one of three categories: action (either hand- or mouth-based sounds), non-action (environmental sounds), and control sounds (scrambled versions of action sounds). In both studies, triplets of sounds of the same category were typically presented, although occasionally, to ensure an attentive state, trials containing a sound from a different category were presented within the triplet and participants were asked to respond to this oddball event either covertly in one study or overtly in another. Additionally, participants in both studies were asked to mimic hand- and mouth-based motor actions associated with the sounds (motor task). Action sounds elicited larger EEG mu rhythm (8–13 Hz) suppression, relative to control sounds, primarily over left hemisphere, while non-action sounds showed larger mu suppression primarily over right hemisphere. Furthermore, hand-based sounds elicited greater mu suppression over the hand area in sensorimotor cortex compared to mouth-based sounds. These patterns of mu suppression across cortical regions to different categories of sounds and to effector-specific sounds suggest differential engagement of a mirroring system in the human brain when processing sounds.

Toward incorporating emotions with rationality into a communicative virtual agent

This paper addresses the problem of human–computer interactions when the computer can interpret and express a kind of human-like behavior, offering natural communication. A conceptual framework for incorporating emotions with rationality is proposed. A model of affective social interactions is described. The model utilizes the SAIBA framework, which distinguishes among several stages of processing of information. The SAIBA framework is extended, and a model is realized in human behavior detection, human behavior interpretation, intention planning, attention tracking behavior planning, and behavior realization components. Two models of incorporating emotions with rationality into a virtual artifact are presented. The first one uses an implicit implementation of emotions. The second one has an explicit realization of a three-layered model of emotions, which is highly interconnected with other components of the system. Details of the model with implicit implementation of emotional behavior are shown as well as evaluation methodology and results. Discussions about the extended model of an agent are given in the final part of the paper.