Measuring cognitive spaces for learning processes

Purpose - The purpose of this paper is to shed a fresh light on cognitive landscapes in which humans and their organizations learn, decide, operate and improve their creative circles. According to Berthoz, human brain manages the complexity of an environment, by creating "virtual worlds" and making "complex simplifications" (defined as simplexity). Essential parts of this process are: a) redundancy of available solutions; b) active intention; c) creation of virtual worlds, with internal Newtonian laws. This ability deeply wired in the brain has been widely analyzed in the recent literature. We intend to contribute to this debate highlighting that also space has fundamental aspects for human cognition and learning; and that measuring and tuning the outside space, as well as the internal, virtualized and social cognitive space, can give us valuable insights. Design/methodology/approach - Building on a previous work where we measured distances in organizations (people, teams, offices, tasks), in this paper we aim to measure and compare physical distances in buildings (offices, museums, public places) with virtual distances, as they are perceived by the people that use those places - permanently (as employees) or temporarily (as guests and customers). Measures of spaces are then obtained both through physical measurements and surveys. We intend to build then oriented graphs and adjacency matrixes, in order to further analyze how the architectural features of the place can be positively or negatively correlated to the cognitive and learning effort of people. Originality/value - Very little is known about the way non-conventional places, including both virtual and social places, may be creatively managed to foster cognitive and learning processes of people. Learning processes can be modeled as a set of layered closed loops that start from the biological level and evolve towards the most abstract levels. We intend first to map and measure the space where cognitive and learning processes develop. Then we aim to introduce a novel approach to define "distances" among these spaces, starting from the pure physical places, up to virtualized and social cognitive spaces. To this purpose we will use a wide array of mathematical tools, up to the latest tools included in the connectionist field of Artificial Intelligence; i.e. Artificial Neural Networks. Indeed, ANNs have gained recent popularity due to outstanding success in so called "Big Data Deep Learning". In our final section, we'll present some relevant examples of them. Practical implications - Our study, focused on mathematical treatment of distance matrices between formal and informal systems, may help to make visible many valuable, hidden information, in a pure bottom-up, data-driven fashion, that can help to develop learning processes, in their own well-engineered space.