We are looking for ways to improve the analysis of avoidance behavior of laboratory rodents in an arena. Currently we are using as a measure the time an animal spent in a defined area around a stimulus which is avoided. However, this measure is highly variable and I heard that there are better but more complex measures ("algorithms") which can be used to characterize avoidance. The animals are tracked by a video-tracking software which also recognizes the nose of the animals. Any advice is appreciated.
We use avoidance paradigms too, we also measure the time spent before stepping into the forbidden compartment. By variable outcomes I guess you mean very big standard deviations. I have used avoidance paradigms and Morris Water Maze for 4 years now, and my experience is that individual results of animals vary a lot no matter what you are measuring. In behavioral tests this is a common problem because no too animals are the same. If you establish certain criteria to exclude animals with extreme deviations from the analysis is a way. Also, try reading the " methods of behavioral analisys in neuroscience" by Jerry J. Buccafusco. It explains avoidance paradigms in depth and will help you costumize it to suit your needs. I hope my answer helped, good luck.
I would not remove extreme individuals, unless they are really rare, as they are part of the population as well. Outliers should be rare, otherwise they are not outliers.
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