From bacteria to flies to artificial intelligence, a common information loop drives successful navigation. Researchers have introduced a new mathematical framework that measures the flow of information between an organism's senses and its actions—and this bidirectional flow reliably predicts how efficiently it navigates.
The Research
The study, led by Kevin S. Chen and colleagues from Yale University and the University of Chicago, was published on arXiv in July 2026. They used an information-theory tool called transfer entropy to quantify two directions of information flow: from sensory inputs to behavior (the 'reactive' part) and from behavior back to sensory inputs (the 'active' part).
By analyzing trajectories from bacteria, worms, flies, and reinforcement-learning agents, they found that the total bidirectional information flow predicted navigation efficiency across all systems. The framework also revealed distinct strategies: bacterial chemotaxis was more reactive, fly olfactory navigation was spatially dependent, and the AI agent adopted a learned policy balancing both flows.
'Across systems, bidirectional information flow reliably predicts navigation efficiency, revealing a common behavioral-environment feedback loop,' the authors write.
Why It Matters
This research suggests that the way we gather and use information is fundamental to how we move through the world—and likely how we think. Understanding your own cognitive strengths and weaknesses can help you tailor strategies for learning and problem-solving.
What You Can Do
You can apply this idea by being more mindful of how your actions influence your environment. When learning something new, ask: 'Am I just reacting, or am I actively shaping my experiences?' Try adjusting your approach based on what you observe, and track what works.
Source: arXiv q-bio.NC
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