How do we understand the intentions of other people? There has been a longstanding controversy over whether it is possible to understand others’ intentions by simply observing their movements. Here, we show that indeed movement kinematics can form the basis for intention detection. By combining kinematics and psychophysical methods with classification and regression tree (CART) modeling, we found that observers utilized a subset of discriminant kinematic features over the total kinematic pattern in order to detect intention from observation of simple motor acts. Intention discriminability covaried with movement kinematics on a trial-by-trial basis, and was directly related to the expression of discriminative features in the observed movements. These findings demonstrate a definable and measurable relationship between the specific features of observed movements and the ability to discriminate intention, providing quantitative evidence of the significance of movement kinematics for anticipating others’ intentional actions.

The coordination of a leader with group members is very important for an effective leadership given that this figure is the person who actually manages the team members to achieve a desired goal. Investigating the leadership and especially the leadership style is a prominent research topic in social and organizational psychology. However, this is a new problem in social signal processing which can actually make valuable contributions by analyzing multi-modal data in a more effective and efficient way. In this work, we identify the leadership style of an emergent leader (i.e. the leader who naturally arises from a group, not designated) as autocratic or democratic. The proposed method is applied to a dataset in-the-wild, in other words there is no role-playing, which is novel for this problem. Multiple kernel learning (MKL) using multi-modal nonverbal features is utilized to predict leadership styles which proved to achieve better predictions as compared to traditional learning methods. Thanks to MKL and a simple heuristic proposed, the best performing features are also identified, showing that better predictions can be reached only using those features. Additionally, correlation analysis between the extracted nonverbal features and the results of social psychology questionnaire is also performed. This shows that significantly high correlations exist for speaking activity based and prosodic nonverbal features.

Aim: The aim of the study was to investigate multidimensional Theory of Mind (ToM) abilities in subjects with alcohol use disorder (AUD). Method: A semi-structured interview and a set of brief stories were used to investigate different components of the participants' ToM, namely first- vs. third-person, egocentric vs. allocentric, first- vs. second-order ToM in 22 persons with AUD plus an equal number of healthy controls. Participants were administered the Theory of Mind Assessment Scale (Th.o.m.a.s., Bosco et al., 2009a) and the Strange Stories test ( Happé et al., 1999). Results: Persons with AUD performed worse than controls at all ToM dimensions. The patterns of differences between groups varied according to the Th.o.m.a.s. dimension investigated. In particular persons with AUD performed worse at third-person than at first-person ToM, and at the allocentric than at the egocentric perspective. Conclusion: These findings support the hypothesis that the ability to understand and ascribe mental states is impaired in AUD. Future studies should focus on the relevance of the different ToM impairments as predictors of treatment outcome in alcoholism, and on the possibility that rehabilitative interventions may be diversified according to ToM assessment.

The ability to attend to someone else's gaze is thought to represent one of the essential building blocks of the human sociocognitive system. This behavior, termed social attention, has traditionally been assessed using laboratory procedures in which participants' response time and/or accuracy performance indexes attentional function. Recently, a parallel body of emerging research has started to examine social attention during real life social interactions using naturalistic and observational methodologies. The main goal of the present work was to begin connecting these two lines of inquiry. To do so, here we operationalized, indexed, and measured the engagement and shifting components of social attention using covert and overt measures. These measures were obtained during an unconstrained real-world social interaction and during a typical laboratory social cuing task. Our results indicated reliable and overall similar indices of social attention engagement and shifting within each task. However, these measures did not relate across the two tasks. We discuss these results as potentially reflecting the differences in social attention mechanisms, the specificity of the cuing task's measurement, as well as possible general dissimilarities with respect to context, task goals, and/or social presence.

Can social gaze behavior reveal the leader during real-world group interactions? To answer this question, we developed a novel tripartite approach combining (1) computer vision methods for remote gaze estimation, (2) a detailed taxonomy to encode the implicit semantics of multi-party gaze features, and (3) machine learning methods to establish dependencies between leadership and visual behaviors. We found that social gaze behavior distinctively identified group leaders. Crucially, the relationship between leadership and gaze behavior generalized across democratic and autocratic leadership styles under conditions of low and high time-pressure, suggesting that gaze can serve as a general marker of leadership. These findings provide the first direct evidence that group visual patterns can reveal leadership across different social behaviors and validate a new promising method for monitoring natural group interactions.