Regularity of unit length boosts statistical learning in verbal and nonverbal artificial languages

Humans have remarkable statistical learning abilities for verbal speech-like materials and for nonverbal music-like materials. Statistical learning has been shown with artificial languages (AL) that consist of the concatenation of nonsense word-like units into a continuous stream. These ALs contain no cues to unit boundaries other than the transitional probabilities between events, which are high within a unit and low between units. Most AL studies have used units of regular lengths. In the present study, the ALs were based on the same statistical structures but differed in unit length regularity (i.e., whether they were made out of units of regular vs. irregular lengths) and in materials (i.e., syllables vs. musical timbres), to allow us to investigate the influence of unit length regularity on domain-general statistical learning. In addition to better performance for verbal than for nonverbal materials, the findings revealed an effect of unit length regularity, with better performance for languages with regular- (vs. irregular-) length units. This unit length regularity effect suggests the influence of dynamic attentional processes (as proposed by the dynamic attending theory; Large & Jones (Psychological Review 106: 119–159, 1999)) on domain-general statistical learning.