Deficit in the Emotional Embodiment in Alexithymia

Alexithymia refers to difficulties in recognizing one’s own emotions and others emotions. Theories of emotional embodiment suggest that, in order to understand other peoples’ feelings, observers re-experience, or simulate, the relevant component (i.e. somatic, motor, visceral) of emotion’s expressed by others in one’s self. In this way, the emotions are “embodied”. Critically, to date, there are no studies investigating the ability of alexithymic individuals in embodying the emotions conveyed by faces. In the present dissertation different implicit paradigms and techniques falling within the field of affective neuroscience have been employed in order to test a possible deficit in the embodiment of emotions in alexithymia while subjects were requested to observe faces manifesting different expression: fear, disgust, happiness and neutral. The level of the perceptual encoding of emotional faces and the embodiment of emotions in the somato-sensory and sensory-motor system have been investigated. Moreover, non-communicative motor reaction to emotional stimuli (i.e. visceral reactions) and interoceptive abilities of alexithymic subjects have been explored. The present dissertation provided convergent evidences in support of a deficit in the processing of fearful expression in subjects with high alexithymic personality traits. Indeed, the pattern of fear induced changes in the perceptual encoding, in the somato-sensory and in the somato-motor system (both the communicative and non communicative one) is widely and consistently altered in alexithymia. This support the hypothesis of a diminished responses to fearful stimuli in alexithymia. In addition, the overall results on happiness and disgust, although preliminary, provided interesting results. Indeed, the results on happiness revealed a defective perceptual encoding, coupled with a slight difficulty (i.e. delayed responses) at the level of the communicative somato-motor system, and the emotion of disgust has been found to be abnormally embodied at the level of the somato-sensory system.

Ethics in the flesh: formalizing moral values in embodied cognition

Values are beliefs or principles that are deemed significant or desirable within a specific society or culture, serving as the fundamental underpinnings for ethical and socio-behavioral norms. The objective of this research is to explore the domain encompassing moral, cultural, and individual values. To achieve this, we employ an ontological approach to formally represent the semantic relations within the value domain. The theoretical framework employed adopts Fillmore’s frame semantics, treating values as semantic frames. A value situation is thus characterized by the co-occurrence of specific semantic roles fulfilled within a given event or circumstance. Given the intricate semantics of values as abstract entities with high social capital, our investigation extends to two interconnected domains. The first domain is embodied cognition, specifically image schemas, which are cognitive patterns derived from sensorimotor experiences that shape our conceptualization of entities in the world. The second domain pertains to emotions, which are inherently intertwined with the realm of values. Consequently, our approach endeavors to formalize the semantics of values within an embodied cognition framework, recognizing values as emotional-laden semantic frames. The primary ontologies proposed in this work are: (i) ValueNet, an ontology network dedicated to the domain of values; (ii) ISAAC, the Image Schema Abstraction And Cognition ontology; and (iii) EmoNet, an ontology for theories of emotions. The knowledge formalization adheres to established modeling practices, including the reuse of semantic web resources such as WordNet, VerbNet, FrameNet, DBpedia, and alignment to foundational ontologies like DOLCE, as well as the utilization of Ontology Design Patterns. These ontological resources are operationalized through the development of a fully explainable frame-based detector capable of identifying values, emotions, and image schemas generating knowledge graphs from from natural language, leveraging the semantic dependencies of a sentence, and allowing non trivial higher layer knowledge inferences.