ELSC Seminar Series
Prof. Nir Gov
Spin models for decision-making in real and abstract space
Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. We present a spin-based model that describes the decision-making process while the animal is moving through space and assessing the different options. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we test the predicted interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. The theoretical model reveals the occurrence of spontaneous and abrupt “critical” transitions, whereby organisms spontaneously switch from averaging vectorial information to suddenly excluding one among, the remaining options. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa there exist fundamental geometric principles that determine how, and why, animals move the way they do.The same model, with global inhibition, is proposed for abstract decision-making, and provides an extension of the successful Drift-Diffusion model. The model exhibits natural trade-off between speed and accuracy, along the transition lines between the ordered and disordered phases. By comparing to recent experimental data we propose that the region near the tri-critical point of the spin model provides optimal conditions for the decision-making process
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