Brain’s mistakes provide insight for human & machine learning

Brain-machine interfaces are devices that allow their subjects to control external devices using only their thoughts. Using these interfaces, a team of researchers at Carnegie Mellon has found that the brain makes mistakes because its conception of the world isn’t always an accurate depiction of how the world really works. The project primarily focuses on the process of learning while using a brain-machine interface. Learning can be defined as an organism becoming increasingly able to adapt to its environment. This type of learning is nondeclarative, meaning that it cannot be expressed in words. Nondeclarative information can be skills or certain motor abilities, in contrast to declarative memory, which is comprised of facts and information. Nondeclarative memory can easily be understood as “knowing how,” where declarative memory is “knowing that.” In…


Link to Full Article: Brain’s mistakes provide insight for human & machine learning