SRCD 2019: fNIRS decoding

Here is our poster for SRCD 2019.

Verbal prediction is claimed to be an important mechanism supporting language development (Elman, 1990; Dell & Chang, 2014). In line with this view, prior findings indicate that infants can predict upcoming words during language processing (Reuter et al., under revision) and that prediction and vocabulary size are positively correlated (e.g., Mani & Huettig, 2012). However, previous research investigating verbal prediction has relied on behavioral methods and it’s unclear whether infants use top-down connections to pre-activate lexical representations. In two ongoing experiments, we extend prior fNIRS decoding methods (Emberson, Zinszer, et al., 2017) to decode infants’ lexical representations and to investigate whether infants are able to pre-activate (i.e., predict) lexical representations. These experiments make both methodological and theoretical contributions: First, it is possible to decode infants’ lexical representations using fNIRS decoding methods. Second, these methods provide a promising means of evaluating verbal prediction in infancy and determining to what extent infants pre-activate lexical representations. Future work will use fNIRS decoding to investigate verbal prediction among infants at-risk for language impairment in order to uncover the cognitive and neural origins of language delays.

We thank all participant families. We also thank Carolyn Mazzei and Jean Bellamy for their assistance with stimuli, and Princeton Baby Lab research assistants for their help recruiting and scheduling participants.

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