A simple-rule learning trap occurs when people show suboptimal category learning due to insufficient exploration of the learning environment. By combining experimental methods and computational modeling, the current study investigated the impact of two key factors believed to play essential roles in the development of a simple-rule learning trap: early learning experience and selective attention. Our results showed that, in a learning environment where the true category mapping was determined by conjunctions of two predictive dimensions, the likelihood of falling into a single-dimensional learning trap increased when early learning experience involved a large loss that could be predicted from a single feature dimension. In addition, using a model-based measurement of attention bias, we observed that early experience affected trap formation by narrowing the distribution of attention to exemplar features. These findings provide the first direct empirical evidence of how early learning experience shapes the formation of a simple-rule learning trap, as well as a more granular understanding of the role of selective attention and its interaction with early learning experience in trap formation.