Understanding in the Human and the Machine

This Research Topic will examine current views on the function and emergence of human understanding and the underlying neuronal mechanisms in the brain, seeking to set the stage for integrating disparate fields in systems neuroscience, cognitive science, and machine intelligence. Attempts to ‘understand understanding’ date back to the dawn of science and philosophy, and continued throughout history, and are of relevance today as we try to operationalize a ‘general intelligence’. Aristotle’s Metaphysics attributed to humans an innate thirst for understanding, i.e., for knowing things and apprehending their causes. Immanuel Kant formulated Pure Concepts of Understanding, treating it as a transcendental property of the mind that serves to organize sensory experiences but is neither affected by nor can be derived from them. In the second part of the last century, cognitive science viewed understanding through the lens of computational metaphor, reducing it to possession of data and algorithms, while analysis rooted in modern physics was advancing the notion that mathematical understanding cannot be modeled in terms of computational procedures.

In the last several decades, developments in theory and technology offered new ideas, new questions, and new data – where a new engineering field of Artificial Intelligence attempted to create artifacts inspired by the theories and data concerning human cognitive capacities and biophysical processes in the brain. As a result of these accomplishments, a unifying theoretical framework might be within reach that takes advantage of the recent findings in neuroscience, cognitive science, physics, information science, other disciplines and reconciles different views on the mechanisms and function of understanding.

This Research Topic suggests that such a unifying theoretical framework can derive from the principles of active inference and variational free energy minimization in the brain, and invites contributions that support that suggestion, contest it and/or formulate constructive alternatives. Contributions in this Research Topic will help advance the state of the art in cognitive neuroscience and in the design of intelligent artifacts that aim to have a degree of human understanding.

Articles addressing neuronal mechanisms of inference and mental modeling, the impact of biophysical constraints in the brain on the evolution and development of cognitive functions, metabolic costs of cognitive processes and their impact on learning and understanding, cognitive control and sensory-motor coordination, consciousness and cognitive effort, representation of relations, neuronal underpinnings of language understanding, representation of causality will be relevant to these objectives.