A classic problem in cognitive science concerns how listeners perceive and understand speech as comprised of discrete words. We propose a Syllable Inference account of spoken word recognition and segmentation, under which alternative hierarchical models of syllables, words, and phonemes are dynamically posited from cues that include current and past speech rate, with a goal of maximal prediction of sensory input. Three experiments using the Visual World eye‐tracking paradigm provide evidence supporting our proposal.

Logopenic variant primary progressive aphasia is a neurodegenerative language disorder primarily characterized by impaired phonological processing. Sentence repetition and comprehension deficits are observed in lvPPA and linked to impaired phonological working memory, but recent evidence also implicates impaired speech perception. Currently, neural encoding of the speech envelope, which forms the scaffolding for perception, is not clearly understood in lvPPA. We leveraged recent analytical advances in electrophysiology to examine speech envelope encoding in lvPPA. We assessed cortical tracking of the speech envelope (...) and in-task comprehension of two spoken narratives in individuals with lvPPA and age-matched controls. Despite markedly reduced narrative comprehension relative to controls, individuals with lvPPA had increased cortical tracking of the speech envelope in theta oscillations, which track low-level features, but not delta oscillations, which track speech units that unfold across a longer time scale. This neural signature was highly correlated across narratives. Results indicate an increased reliance on acoustic cues during speech encoding. This may reflect inefficient encoding of bottom-up speech cues, likely as a consequence of dysfunctional temporoparietal cortex. (shrink)