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Abstract:

To learn words, humans extract statistical regularities from speech. Multiple species use statistical learning, also to process speech, but the neural underpinnings of speech segmentation in non-humans remain largely unknown. Here we investigated computational and neural markers of speech segmentation in dogs, a phylogenetically distant mammal that efficiently navigates humans’ social and linguistic environment. Using EEG, we compared ERPs for artificial words previously presented in a continuous speech stream with different distributional statistics. Results revealed an early effect (220-470 ms) of transitional probability, and a late component (590-790 ms) modulated by both word frequency and transitional probability. Using fMRI, we searched for brain regions sensitive to statistical regularities in speech. Structured speech elicited lower activity in the basal ganglia, a region involved in sequence learning; and repetition enhancement in the auditory cortex. Speech segmentation in dogs, similarly to humans, involves complex computations, engaging both domain-general and modality-specific brain areas.