Mind versus machine: can biological models of the mind inspire machine intelligence design?

Abstract

Models of human cognition that look biologically plausible might in principle be a source of insights that could prove applicable to machine intelligence. The Modular Cognition Framework fits that description in terms of its biological credentials and its potential to identify the challenges confronting GenAI developers. However, it is an account of cognition in the mind and not in the brain. Accordingly, the account includes a clear delineation of the two intimately related concepts, mind and brain, and a definition of their relationship. If either brain or mind can contribute to this debate, a model of the mind could offer a more immediate promising source of inspiration than its complex neural underpinnings. Key features of the theoretical framework include its fundamentally heterarchical architecture having no single locus of control. In addition, general and domain-specific processing principles permit rapid, highly flexible, adaptable interaction between its network of modular systems as they collaborate in the execution of multiple tasks in a constantly changing environment. Eleven types of knowledge are constructed from experience using a similar number of inherited toolkits. Questions are raised as to whether the adaptable and highly creative character of evolved intelligence is reproducible in artificial systems, the best strategies for deriving benefit from studying biological systems and whether neural organisation is really the only place to look for biological insights.