Is statistical learning trainable?

Statistical learning (SL) is the ability to implicitly extract regularities in the environment, and likely supports various higher-order behaviors, from language to music and vision. While specific patterns experience are likely to influence SL outcomes, this ability is tacitly conceptualized as a fixed construct, and few studies to date have investigated how experience may shape statistical learning. We report one experiment that directly tested whether SL can be modulated by previous experience. We used a prepost treatment design allowing us to pinpoint what specific aspects of “previous experience” matter for SL. The results show that performance on an artificial grammar learning task at post-test depends on whether the grammar to be learned at post-test matches the underlying grammar structures learned during treatment. Our study is the first to adopt a pre-post test design to directly modulate the effects of learning on learning itself