SRCD 2019: language input and prediction

Here are my slides for SRCD 2019.

A large body of research indicates that environmental factors like SES play a role in children’s language development (for review see Fernald & Weisleder, 2011). These findings reveal that children’s day-to-day language experiences vary tremendously, as do their developmental trajectories. Factors like SES, language input, and language processing abilities interact to shape children’s emerging linguistic abilities (e.g., Weisleder & Fernald, 2013).

At the same time, a related body of work has investigated whether a particular aspect of language processing – prediction – supports development, and a number of findings are in line with this view (e.g., Reuter et al., under revision). However, the origins of individual differences in prediction are unknown. Given prior findings linking language input and processing efficiency, it seems likely that language input also supports predictive language processing. Importantly, evaluating the link between input and prediction is essential for evaluating prediction-based theories of learning: These theories claim that accurate predictions are the result of accumulated language processing experiences. Children who receive more input have more processing experiences and therefore should be able to generate more accurate predictions during language processing (e.g., Dell & Chang, 2014).

We therefore hypothesized that home language input supports children’s emerging language processing abilities, including predictive language processing. Using a combination of eye-tracking tasks, LENA recordings, and vocabulary measures (PPVT), we find that:  High-SES toddlers receive marginally more language input from caregivers. High-input toddlers have larger vocabularies, and, importantly, high-input toddlers generate more robust predictions during language processing. These findings add to a body of literature linking SES-based disparities in input and processing (e.g., Weisleder & Fernald, 2013) and provide further support for prediction-based theories of language development (e.g., Dell & Chang, 2014).

We thank all participant families and we’re grateful to all Princeton Baby Lab research assistants for their help with data collection. We also thank Monica Ellwood-Lowe and Mahesh Srinivasan for organizing our SRCD 2019 symposium on SES-based disparities in language development.

SRCD 2019: prediction vs. repetition

Here is our poster for SRCD 2019.

Recent theories claim that prediction supports language development, and previous findings are in line with this view (Dell & Chang, 2014; Reuter et al., under revision). However, because behavioral measures of prediction are indirect, it’s unclear what representations are activated during processing, and how specific those representations are (Rabagliati et al., 2016). Is predicting a word like cookie via semantically related words like eat, yum, and mouth distinct from comprehending the word cookie itself? We used an eye-tracking paradigm to evaluate how infants processed different types of sentences (neutral, prediction, and repetition). Findings indicate that infants predict and comprehend words, replicating prior results (Reuter et al., under revision). Findings further suggest that infants can activate and pre-activate (predict) specific lexical representations during language processing: The behavioral dynamics of prediction are distinct from those of repeated comprehension. In sum, these findings further suggest that prediction supports both language processing and language development in infancy.

We thank all participant families. We also thank Chandra Greenberg and Claire Robertson for their assistance with stimuli and data collection, and Princeton Baby Lab research assistants for their help recruiting and scheduling participants.

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.

CogSci 2018

Here is my  poster for CogSci 2018.

Our study evaluated whether listeners can use spatial deixis (e.g., this, that, these, and those) to predict the plurality and proximity of a speaker’s referent. In an eye-tracking task, L1 adults, L1 children and L2 adults viewed scenes while listening to deictic sentences (e.g., Look at that beautiful baby) and neutral sentences (e.g., Look at the beautiful baby). We found that L1 adults, L1 children and L2 adults all used deixis to predict the plurality of the referent (e.g., using this to anticipate a singular referent). However, only L1 adults used deixis to predict the proximity of the referent to the speaker (e.g., using this to anticipate a referent proximal to the speaker). Thus, our findings suggest that language processing experience influences verbal prediction. We argue that, beyond determining whether listeners predict, determining how listeners predict is crucial to understand prediction’s role in language processing and learning.

Big thanks to all participant families, to Claire Robertson for her assistance with stimuli, and to Mia Sullivan to her assistance with ongoing data collection and CHILDES coding!

I’m looking forward to spending time at my alma mater and catching up with family and friends in Madison!

ICIS 2018

Here are my abstracts and posters for ICIS 2018.

Our first study evaluated the developmental emergence of verbal prediction and language comprehension. We find that prediction and comprehension emerge concurrently over the second postnatal year. These findings add to a growing body of literature suggesting that prediction is a language learning mechanism, and further suggest that prediction supports language development from the earliest stages, as infants learn their first words. Here’s the abstract and the poster.

Our second study evaluated whether variation in home language input influences children’s verbal prediction abilities. We found that children who hear more language input from caregivers generate predictions, but children who receive less input do not do so robustly. This pattern of results suggests that the quantity/quality of language experience learners receive influences the extent to which they generate predictions during language processing. Here’s the abstract and the poster.

Looking forward to seeing lots of exciting talks and posters and catching up with old friends from the UW Infant Learning Lab!

rochester fNIRS workshop

It’s been a long time since my last post (tisk tisk!) so here I am, catching up.

Last week one of my advisors, Lauren Emberson, hosted an fNIRS workshop at the University of Rochester. fNIRS (functional near-infrared spectroscopy) is a non-invasive imaging technique used to measure the metabolic activity in the cortex. (For a great review, see Aslin, 2012.) Basically, when areas of the cortex are more active, this requires additional metabolic support, so more oxygenated hemoglobin is transferred to the location of activation. Light is absorbed differentially for oxygenated hemoglobin and deoxygenated hemoglobin, so our measure is essentially how much light the cortex in an approximate area is absorbing during x measurement time. (Note: I say “approximate” because one of the downsides of current fNIRS systems is low spatial resolution, as compared to fMRI, so we can’t make super exact claims about cortical areas.) fNIRS is an excellent method to use with infants, because the imaging doesn’t require rigid head stabilization. Infants wear a cap (similar to EEG) and can sit on their parent’s lap while watching+/listening to audio+/visual stimuli.

This was a great opportunity – both to learn more about the fNIRS methodology and recent literature, but also to bond with my future labmates. Here are a few pictures from the trip:

IMG_6900  IMG_6935