Abstract

Rhythm is key to language acquisition. Across languages, rhythmic features highlight fundamental linguistic elements of the sound stream and structural relations among them. A sensitivity to rhythmic features, which begins in utero, is evident at birth. What is less clear is whether rhythm supports infants' earliest links between language and cognition. Prior evidence has documented that for infants as young as 3 and 4 months, listening to their native language supports the core cognitive capacity of object categorization. This precocious link is initially part of a broader template: listening to a non-native language from the same rhythmic class as and to vocalizations of non-human primates provide English-acquiring infants the same cognitive advantage as does listening to their native language. Here, we implement a machine-learning approach to ask whether there are acoustic properties, available on the surface of these vocalizations, that permit infants' to identify which vocalizations are candidate links to cognition. We provided the model with a robust sample of vocalizations that, from the vantage point of English-acquiring 4-month-olds, either support object categorization or fail to do so. We assess whether supervised ML classification models can distinguish those vocalizations that support cognition from those that do not, and which class of acoustic features best support that classification. Our analysis reveals that principal components derived from rhythm-relevant acoustic features were among the most robust in supporting the classification. Classifications performed using temporal envelope components were also robust. These new findings provide in principle evidence that infants' earliest links between vocalizations and cognition may be subserved by their perceptual sensitivity to rhythmic and spectral elements available on the surface of these vocalizations, and that these may guide infants' identification of candidate links to cognition.