Probably a mathematical theory of surprise!

Computer scientists in California have built a mathematical theory of surprise, working from first principles of probability theory applied to a digital environment, according to this news release from the University of Southern California (USC).

And the results of experiments recording eye movements of volunteers watching video seem to confirm it. Beyond vision applications, this new Bayesian theory of surprise could lead to new developments in data mining, as it can in principle be applied to any type of data, including visual, auditory or text."

This new mathematical theory of surprise has been developed by Laurent Itti, of the USC’s Viterbi School of Engineering, his colleagues at his lab, and by Pierre Baldi, of the University of California Irvine’s Institute for Genomics and Bioinformatics.

Before looking at their theory, here are some key definitions given by the computer engineers.

"By analyzing streams of electronic data making up a video image,]
researchers can isolate stimuli with visual attributes that are unique in the
mix by breaking down the signal into "feature channels," each describing a
particular attribute (i.e,, color) in the mix. Such features are called
"salient."

"A parallel analysis performs similar operations, but does so
over time, not space, looking for new elements suddenly appearing. This approach
is said to model "novelty."

"Finally, an analysis can be done purely in
terms of Shannon’s original equations, which can measure the level of
organization or detail found in the data flow, its entropy."

If you want to take a closer look at the theory of surprise, then you will find the full article here.....