Bird flocks and fish schools move together using local interaction rules whereby they respond to the movements and positions of their neighbours.

The collective motion of animal groups provides one key example. While the design of a machine that accurately simulates a human is still some way off, models of other aspects of animal behaviour are becoming increasingly realistic. The test is designed to assess the ability of a model (the machine) to reproduce the real world (human behaviour). In the Turing test, if a human observer could not determine between which one of two interacting players was a machine (the other a human), then the machine had passed the test and exhibited intelligent behaviour. Our results demonstrate that we can use ‘citizen science’ to cross-validate and improve model fitting not only in the field of collective behaviour, but also across a broad range of biological systems.Īlan Turing provided a means of assessing whether a machine's behaviour was equivalent or indistinguishable from that of a human. Even though the statistical properties of the real data and the model were consistent with each other, the public could still distinguish between the two, highlighting the need for model refinement. We then asked members of the public to play an online game (a modified Turing test) in which they attempted to distinguish between the movements of real fish schools or those generated by the model.

We first built a self-propelled particle model whose properties (order and cohesion) statistically matched those of real fish schools. But even if a model captures the large-scale statistical properties of the data, should we be satisfied with it? We developed a method, inspired by Alan Turing, to assess the effectiveness of model fitting. A widespread problem in biological research is assessing whether a model adequately describes some real-world data.

Download the turing test online game