Interactive storytelling is a form of digital entertainment that has gained attention with the development of creative computational methodologies. However, one of the main problems this field is facing is the poor control that the content creator (e.g. film director or game designer) has over the experience of the user (e.g. viewer or player) once the story starts. Hence, we leverage artificial intelligence to increase the creative control of the content creator by designing a system that guides the user’s emotions towards a particular state as the story unfolds. Specifically, we have developed an EEG-based emotion recognition system trained on EEG recordings acquired from 5 participants watching a selection of 384 videos. The system is able to operate a binary classification on both valence and arousal with an accuracy of 62% and 57%, respectively. A short film was then created, where each scene automatically adapts to the user’s emotion, based on a set of predefined interactions established by the content creator (i.e. the actual film director). The analysis shows that the system not only improves the engagement of the user, but also induces an emotion closer to the one intended and specified ahead of time by the content creator for the story. Our results indicate that there is a practical application of emotion-based studies for future content creators to better control an intended emotional response delivered and received by the audience.

Chromatics, or the science of color, not only studies the description of colors in terms of the physics of electromagnetic radiations, but also their perception through the human eye and cognitive apparatus. Although in purely physical terms colors may be described by as few as three dimensions – such as hue, saturation, and brightness – an open debate remains about how our cognition maps colors and in how many dimensions they encode the distinction between colors according to our perspective. In this work, we study the trade-off between finding an embedding for color perception with the minimal number of dimensions, while maximizing the discrimination between colors. To do so, we designed an experiment where thirteen subjects reported the similarity between twenty colors randomly generated using the Munsell color system. For each subject, we mapped perceived colors in an n-dimensional space, where distances between two colors reflect how different they are according to the subject. We used a least squares optimization to minimize the difference between subject-reported and mapped distances between colors with that dimensionality. We then repeated the process for values from one to nine dimensions. Our results showed an optimal number of dimensions of three when using a cosine similarity measure, which indicates a resemblance to the way the perception of colors is cognitively encoded from mere physical properties of color maps. We discuss the implications and limitations of these results in the light of color theory, and their relevance in both our understanding of the topology of mental concepts and major applications in fields where color theory is important, including composing color scales for designer tools, color psychology in marketing, color matching in interior architecture, and chromatic treatments in post-production of film-making.