U-shape in learning faces reveals universal non-grammar

The ability to extract information that concisely expresses dependencies among language components is fundamental to human language cognition. Among quantitative characterizations of language acquisition processes, the human sensitivity to non-adjacent syllable dependences has been described by a U-shaped curve, as a function of the variability of an intervening irrelevant utterance. In artificial language learning experiments, it was indeed easier to learn pairs of monosyllabic non-words if the middle two-syllable non-word was either constant, or varied among a large set, irrespective of the co-occurring pair, than if its variability was limited. This finding might characterize a possibly uniquely human cross-over between statistical learning and rule extraction. We find, however, that a similar curve describes learning in an unrelated domain, that of facial configurations. When trained on a corpus of face drawings in which the middle component (eyes-nose) was unrelated to the fixed combinations of upper (hair-ears) and lower (mouth-chin) components, subjects were better at rejecting 'ungrammatical' upper-lower combinations if the eyes-nose component was either always the same, or varied independently in a large set. Subjects' performance on a subsequent face grouping task indicates an inability to extract the underlying rule. The U-shape may then universally characterize incidental statistical learning, unrelated to the specific language domain.