Animals are more successful if they learn or evolve to predict locations of food, mates and predators. Prediction of anything relies on correlations over time in the environment. These correlations may be positive or negative. Learning is more difficult if the sign of the correlation switches over time, which occurs in nature due to resource depletion, learning and evolution.

If a herbivore eats a tasty patch of plants or a predator a nest full of eggs, then the next day that food is not there (negative correlation), but the next year at the same time it is probably there again (positive correlation) because the plants regrow from roots or seeds, and if the prey found the nesting spot attractive one year, then other members of the prey species will likely prefer it the next year as well. However, over many generations, if the plants in that location get eaten before dispersing seeds or the young in that nest before breeding, then the prey will either learn or evolve to avoid that location, or go extinct. This makes the autocorrelation negative again on sufficiently long timescales.

Positive correlation is the easiest to learn – just keep doing the same thing and achieve the same successful outcome. Negative correlation is harder, because the absence of success at one time predicts success from the same action at another time, and vice versa. Learning a changing correlation requires a multi-parameter mental model of the superimposed different-frequency oscillations of resource abundance.

There is a tradeoff between exploiting known short-period correlations and experimenting to learn longer-period correlations. There may always be a longer pattern to discover, but finite lifetimes make learning very low-frequency events not worthwhile.