Computational semiotics

Programming languages represent a class of semiotic systems that has mostly escaped semiotic literature, as they have been typically regarded as mechanical and, thus, far from the complexity of man–made systems. The contribution aims at introducing some general aspects of programming language by proposing a theoretical semiotic framework. Enunciation is taken into account and its effects are investigated in relation to three programming language paradigms.

Given the affective psychological and cognitive dynamics prevalent during human-robot-interlocution, the vulnerability to cultural-political influences of the design aesthetics of a social humanoid robot has far-reaching ramifications. Building upon this hypothesis, I explicate the relationship between the structure of the constitution social ontology and computational semiotics, and venture a theoretical framework which I propose as a thesis that impels a moral responsibility on engineers of social humanoids. In distilling this thesis, the implications of the intersection between the socio-aesthetics of racialised and genderised humanoids and the phenomenology of human-robot-interaction are illuminated by the figuration of the experience of a typical black rural African woman as the user, that is, an interlocutor with an industry-standard socially-situated humanlike robot. The representation of the gravity of the psycho-existential and socio-political ramifications of such woman's life with humanoids is abstracted and posited as grounds that illustrate the imperative for roboticists to take socio-ethical considerations seriously in their designs of humanoids.

Mentalization describes the process through which we understand the mental states of oneself and others. In this paper, I present a computational semiotic model of mentalization and illustrate it through a worked-out example. The model draws on classical semiotic ideas, such as abductive inference and hypostatic abstraction, but pour them into new ideas and tools from natural language processing, machine learning and neural networks, to form a novel model of language-mediated-mentalization.