Abstract
Not everyone is equally well suited for every endeavor—individuals differ in their strengths and weaknesses, which makes some people better at performing some tasks than others. As such, it might be possible to predict individuals’ peak competence (i.e., ultimate level of success) on a given task based on their early performance in that task. The current study leveraged “big data” from the mobile game, Airport Scanner (Kedlin Company), to assess the possibility of predicting individuals’ ultimate visual search competency using the minimum possible unit of data: response time on a single visual search trial. Those who started out poorly were likely to stay relatively poor and those who started out strong were likely to remain top performers. This effect was apparent at the level of a single trial (in fact, the first trial), making it possible to use raw response time to predict later levels of success.
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