Reconsidering the Automaticity of Visual Statistical Learning

Statistical learning refers to the process of extracting regularities from the world without feedback. What are the necessary conditions for statistical learning to arise? It has been argued that visual statistical learning (VSL) is “automatic”, such that subjects will passively and even unconsciously extract statistical regularities from streams of visual input as long as they attend to the stimuli. In contrast, our data indicate that simply attending to stimuli is not, on its own, sufficient for learning. In Experiments 1 & 2, we provided incidental exposure to regularities in a stream of images and observed little to zero VSL across a range of conditions. In Experiment 3, we found that explicitly instructing participants to seek regularities dramatically improved their performance on direct measures of learning, but not on an indirect response time measure. Finally, in Experiments 4 & 5, we demonstrated that a methodological confound in prior work using the indirect response time measure could account for some previous evidence of automatic and implicit VSL.Overall, we found very little evidence of learning using direct measures of VSL, and no evidence of learning using an indirect response time measure. Participants who recognized visual sequence regularities in a forced-choice task could also often recreate the sequences when explicitly probed, indicating their knowledge was not entirely implicit. We suggest that some form of active engagement with stimuli may be needed to extract sequential regularities, and that VSL does not occur automatically.