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Signal Detection Theory is a popular and well-established idea that has influenced behavioral science for around 50 years. Essentially,
the theory holds that in a predator-prey relationship, prey animals will show more wariness and be more prone to flee as predators become more common. Danger signals are ambiguous, so in what appears to be a threatening situation, animals are better off running than hanging around to see if a predator really does strike.
Now Pete Trimmer, a postdoctoral research at UC Davis, has taken a fresh look at signal detection theory and come up with what at first look like counterintuitive results. In many cases, he says, animals should actually become less cautious as the risk of predation rises.
The problem with conventional signal detection theory, Trimmer says, is that it only considers one decision at a time, in isolation. But in reality, animals may have to make multiple connected decisions and have to take into account the effects of decisions over time.
Trimmer, graduate student Sean Ehlman and Professor Andy Sih at UC Davis, with mathematician John McNamara at the University of Bristol, U.K. developed a new model that they call state-dependent detection theory or SDDT. The work is published Oct. 18 in the journal
Proceedings of the Royal Society B.
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