Tuning movement for sensing in an uncertain world

Area of Science:

• Behavioral Ecology
• Neuroscience
• Robotics

Background:

• Animals exhibit unexplained sensory organ movements during target tracking.
• Existing theories (infotaxis, gain adaptation, etc.) inadequately explain these movements.
• Previous models show poor agreement between predicted and measured animal trajectories.

Purpose of the Study:

• To propose and validate a new theory for sensory organ movements during animal navigation.
• To unify metabolic costs with information theory for predicting sensor motion.
• To provide a framework for designing robotic sensor movement strategies.

Main Methods:

• Developed the energy-constrained proportional betting (ECPB) theory.
• Modeled sensory organ movement probability based on information gain and energy cost.
• Validated ECPB predictions against empirical data from multiple species and sensory modalities.

Main Results:

• ECPB theory demonstrated strong agreement with measured trajectories of fish, mammals, insects, and moths.
• The model successfully predicts sensory organ movements across diverse animal tracking behaviors.
• The theory integrates energetic constraints with information-theoretic principles.

Conclusions:

• Energy-constrained proportional betting offers a unified explanation for animal sensory organ movements.
• This theory bridges the gap between metabolic costs and information processing in biological systems.
• ECPB provides a foundation for optimizing robotic sensor movement for enhanced performance.