Cross-Phoneme Generalisation of Dimension-Based Statistical Learning for Stop Voicing: Probing Subject Design and...

Adult listeners rapidly reweight their reliance on acoustic dimensions of their native language during spoken word recognition following exposure to statistical deviations in the covariance between two cues to a contrast. This form of perceptual learning, referred to as dimension-based statistical learning (DBSL), has been shown to be highly context-sensitive for stop voicing cued by voice onset time and F0. The present study probes generalisation of DBSL across place of articulation, when learning conditions from previous studies are enhanced: introducing an additional contrast in training, and removing word frame variability. Experiment 1 serves as a robust conceptual replication of same-place learning, using a between-subjects design and two separate word-frame contexts. Experiment 2 follows the same design, probing if learning generalises across place of articulation. Experiment 3 also examines cross-phoneme generalisation, but using a within-subjects design. We find no evidence of learning across the four generalisation sub-experiments, demonstrating that DBSL for stop voicing is very place-specific. In the between-subjects experiments, we find larger learning effect estimates than in our previous study with variable word frames, and larger estimates for -ALE compared to -ILL frames, providing further evidence of context-sensitivity of DBSL, in the form of lexical/word-frame context.