An algorithm generating long sequences of stimuli in behavioral science: A suitable test for biosensors

Many behavioral and cognitive researches as well as animal learning studies need to construct sequences of stimuli presentation to test subject's performance. The major problem in sequences building is to meet "randomness" in stimuli succession with "balance" among stimulus kinds and to prevent guessing trials and behavioral habits. Starting from series of Gellermann, who first dealt with the problem, the existing systems are limited in sequence length and in stimuli number, or they require special-purpose computer programs useful only under well-specified experimental designs. We have developed a simple C++ algorithm generating long sequences of multiple presentations of n stimuli for two-choice discrimination tasks, accomplishing to three Gellermann's criteria (constraints); moreover flexible enough to be employed with a variety of experimental protocols different from subject to subject in sequence length, in stimuli number and in stimuli ratio. Finally, the p resent algorithm could represent a suitable tool for quality control tests of biosensor applications.