Decisions, decisions

Temnothorax albipennis workers with RFID tags

Temnothorax albipennis workers with RFID tags

Animals constantly make decisions. Habitat selection, mate selection and foraging require investigation of, and choice between, alternatives that may determine an animal's reproductive success. These kinds of problems confront organisms at all levels of biological complexity, and require a compromise between speed and accuracy in decision-making.

Striking parallels are evident between decision-making in primate brains and collective decision-making in social insect colonies. By analysing models from neuroscience and insect socio-biology, an inter-disciplinary team of researchers from University of Bristol show how colonies of house-hunting social insects could collectively compromise between the speed and accuracy of decision-making, using mechanisms similar to those used by neurons in the primate brain.

The study, published in the Journal of the Royal Society Interface, draws the first formal parallels between decision-making circuits in the primate visual cortex and social insect colonies, including the rock ant Temnothorax albipennis which lives in colonies of up to a few hundred individuals. Both ‘systems’ make choices that reflect an optimal compromise between speed and accuracy of decision-making, by assessing competing streams of evidence.

Neurons in the brain and social insect colonies must reach a point at which a decision is initiated. Notwithstanding their impressive individual abilities, neurons are simple in comparison with individually sophisticated social insects. Nevertheless, at a simple level, both systems can implement robust, efficient decisions, regardless of how sophisticated their individual components are.

Dr James Marshall, Senior Lecturer in Computer Science said: “The analysis we present represents the first step in establishing a common theoretical framework for the study of decision-making in biological systems. This framework should prove applicable to diverse biological systems at many levels of biological complexity, including humans.”

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