Training brain networks and states.

Area of Science:

• Cognitive Neuroscience
• Neuroplasticity
• Behavioral Psychology

Background:

• Brain training aims to enhance cognitive functions and performance beyond trained tasks.
• Existing brain training methods can be broadly categorized into network training and state training.
• Working memory training (WMT) and meditation training (MT) are prevalent examples with distinct underlying mechanisms.

Purpose of the Study:

• To differentiate between network training and state training within the broader field of brain training.
• To elucidate the similarities and differences between WMT and MT as exemplars of network and state training, respectively.
• To propose a conceptual framework distinguishing network and state training for a clearer understanding of brain enhancement.

Main Methods:

• Comparative analysis of working memory training (WMT) and meditation training (MT).
• Examination of the neural networks and cognitive processes engaged by each training type.
• Discussion of the generalization patterns observed in WMT and MT.

Main Results:

• WMT primarily targets specific neural networks through repetitive practice.
• MT induces a change in brain state, influencing multiple neural networks.
• The two methods differ in the brain regions activated and the extent of cognitive generalization.

Conclusions:

• A clear distinction exists between network training and state training methods.
• Recognizing these differences is crucial for optimizing brain training interventions.
• This conceptual distinction aids in understanding and developing more effective cognitive enhancement strategies.