Sensing and decision-making in random search

TL;DR

This paper introduces a framework for modeling search and decision-making in organisms when sensory signals are infrequent, fluctuating, and uninformative, showing that even minimal signals can significantly enhance search efficiency.

Contribution

The paper presents a novel framework combining movement behavior with sensory response modeling, demonstrating how simple signal responses improve search performance in challenging environments.

Findings

Signal response models outperform traditional random search strategies.

Lack of signals can still inform searcher behavior effectively.

Area-restricted search behavior emerges naturally from the model.

Abstract

While microscopic organisms can use gradient-based search to locate resources, this strategy can be poorly suited to the sensory signals available to macroscopic organisms. We propose a framework that models search-decision making in cases where sensory signals are infrequent, subject to large fluctuations, and contain little directional information. Our approach simultaneously models an organism's intrinsic movement behavior (e.g. Levy walk) while allowing this behavior to be adjusted based on sensory data. We find that including even a simple model for signal response can dominate other features of random search and greatly improve search performance. In particular, we show that a lack of signal is not a lack of information. Searchers that receive no signal can quickly abandon target-poor regions. Such phenomena naturally give rise to the area-restricted search behavior exhibited by many searching organisms.