The article explores the concept of information decomposition in the brain, highlighting how information is not a monolithic entity but can be broken down into synergistic, unique, and redundant components. This decomposition provides a framework to understand how the brain processes and integrates information for cognition. The authors review evidence that synergistic interactions, which enhance cognitive flexibility, are more prevalent in the human brain compared to redundancy, which ensures robustness in sensory and motor functions. They discuss the evolutionary and developmental origins of these information types, noting that synergy is selectively increased in humans, particularly in regions associated with complex cognition. The article also examines the role of synergy and redundancy in neural computation and behavior, suggesting that synergy is crucial for flexible processing, while redundancy ensures reliability. Finally, it explores the potential of information decomposition to bridge the gap between biological and artificial intelligence, highlighting the importance of understanding different types of information in developing more human-like AI systems.The article explores the concept of information decomposition in the brain, highlighting how information is not a monolithic entity but can be broken down into synergistic, unique, and redundant components. This decomposition provides a framework to understand how the brain processes and integrates information for cognition. The authors review evidence that synergistic interactions, which enhance cognitive flexibility, are more prevalent in the human brain compared to redundancy, which ensures robustness in sensory and motor functions. They discuss the evolutionary and developmental origins of these information types, noting that synergy is selectively increased in humans, particularly in regions associated with complex cognition. The article also examines the role of synergy and redundancy in neural computation and behavior, suggesting that synergy is crucial for flexible processing, while redundancy ensures reliability. Finally, it explores the potential of information decomposition to bridge the gap between biological and artificial intelligence, highlighting the importance of understanding different types of information in developing more human-like AI systems.