Is It Me or the Robot? A Critical Evaluation of Human Affective State Recognition in a Cognitive Task.

A key goal in human-robot interaction (HRI) is to design scenarios between humanoid robots and humans such that the interaction is perceived as collaborative and natural, yet safe and comfortable for the human. Human skills like verbal and non-verbal communication are essential elements as humans tend to attribute social behaviors to robots. However, aspects like the uncanny valley and different technical affinity levels can impede the success of HRI scenarios, which has consequences on the establishment of long-term interaction qualities like trust and rapport. In the present study, we investigate the impact of a humanoid robot on human emotional responses during the performance of a cognitively demanding task. We set up three different conditions for the robot with increasing levels of social cue expressions in a between-group study design. For the analysis of emotions, we consider the eye gaze behavior, arousal-valence for affective states, and the detection of action units. Our analysis reveals that the participants display a high tendency toward positive emotions in presence of a robot with clear social skills compared to other conditions, where we show how emotions occur only at task onset. Our study also shows how different expression levels influence the analysis of the robots' role in HRI. Finally, we critically discuss the current trend of automatized emotion or affective state recognition in HRI and demonstrate issues that have direct consequences on the interpretation and, therefore, claims about human emotions in HRI studies.

Affordance processing in segregated parieto-frontal dorsal stream sub-pathways.

The concept of affordances indicates "action possibilities" as characterized by object properties the environment provides to interacting organisms. Affordances relate to both perception and action and refer to sensory-motor processes emerging from goal-directed object interaction. In contrast to stable properties, affordances may vary with environmental context. A sub-classification into stable and variable affordances was proposed in the framework of the ROSSI project (Borghi et al., 2010; Borghi and Riggio, 2015, 2009). Here, we present a coordinate-based meta-analysis of functional imaging studies on object interaction targeting consistent anatomical correlates of these different types of affordances. Our review revealed the existence of two parallel (but to some extent overlapping) functional pathways. The network for stable affordances consists of predominantly left inferior parietal and frontal cortices in the ventro-dorsal stream, whereas the network for variable affordances is localized preferentially in the dorso-dorsal stream. This is in line with the proposal of differentiated affordances: stable affordances are characterized by the knowledge of invariant object features, whereas variable affordances underlie adaptation to changing object properties.

Multimodal emotional state recognition using sequence-dependent deep hierarchical features.

Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5% reported in the literature to 91.3% for a benchmark dataset on spontaneous emotion expressions.

Grasping language--a short story on embodiment.

The new concept of embodied cognition theories has been enthusiastically studied by the cognitive sciences, by as well as such disparate disciplines as philosophy, anthropology, neuroscience, and robotics. Embodiment theory provides the framework for ongoing discussions on the linkage between "low" cognitive processes as perception and "high" cognition as language processing and comprehension, respectively. This review gives an overview along the lines of argumentation in the ongoing debate on the embodiment of language and employs an ALE meta-analysis to illustrate and weigh previous findings.The collected evidence on the somatotopic activation of motor areas, abstract and concrete word processing, as well as from reported patient and timing studies emphasizes the important role of sensorimotor areas in language processing and supports the hypothesis that the motor system is activated during language comprehension.