The role of metacognition in recognition of the content of statistical learning

Abstract

Despite theoretical debate on the extent to which statistical learning is incidental or modulated by explicit instructions and conscious awareness of the content of statistical learning, no study has ever investigated the metacognition of statistical learning. We used an artificial language-learning paradigm and a segmentation task that required splitting a continuous stream of syllables into discrete recurrent constituents. During this task, statistical learning potentially produces knowledge of discrete constituents as well as about statistical regularities that are embodied in familiarization input. We measured metacognitive sensitivity and efficiency (using hierarchical Bayesian modelling to estimate metacognitive sensitivity and efficiency) to probe the role of conscious awareness in recognition of constituents extracted from the familiarization input and recognition of novel constituents embodying the same statistical regularities as these extracted constituents. Novel constituents are conceptualized to represent recognition of statistical structure rather than recognition of items retrieved from memory as whole constituents. We found that participants are equally sensitive to both types of learning products, yet subject them to varying degrees of conscious processing during the postfamiliarization recognition test. The data point to the contribution of conscious awareness to at least some types of statistical learning content