In computerized adaptive testing (CAT) procedures within the framework of probabilistic test theory the difficulty of an item is adjusted to the ability of the respondent, with the aim of maximizing the amount of information generated per item, thereby also increasing test economy and test reasonableness.However, earlier research indicates that respondents might feel over-challenged by a constant success probability of p=0.5 and therefore cannot come to a sufficiently high answer certainty within a reasonable timeframe. Consequently response time per item increases, which – depending on the test material – can outweigh the benefit of administering optimally informative items. Instead of a benefit, the result of using CAT procedures could be a loss of test economy.Based on this problem, an adaptive success control algorithm was designed and tested, adapting the success probability to the working style of the respondent. Persons who need higher answer certainty in order to come to a decision are detected and receive a higher success probability, in order to minimize the test duration (not the number of items as in classical CAT). The method is validated on the re-analysis of data from the Adaptive Matrices Test (AMT, Hornke, Etzel & Rettig, 1999) and by the comparison between an AMT version using classical CAT and an experimental version using Adaptive Success Control.The results are discussed in the light of psychometric and psychological aspects of test quality.