The ability to predict upcoming input may be a language learning mechanism. This idea is
supported by research on individual differences in verbal prediction. Two-year-olds and threeyear-olds with larger vocabularies (MCDI, PPVT) generate verbal predictions in sentence
processing, and flexibly update inaccurate predictions in light of new linguistic information.
However, the nature of this relation is currently unknown.
One possibility is that differences in verbal prediction arise from differences in language
abilities. That is, children with larger vocabularies are more efficient language processors, and
therefore have the capacity to generate and update verbal predictions. Alternatively, this
correlation may signal a difference in information processing, beyond the domain of language.
To investigate the latter possibility, we evaluated whether infants’ nonverbal predictions are also
correlated with vocabulary size (MCDI). Just as children with larger vocabularies generate and
flexibly update verbal predictions, we expected infants with larger vocabularies would generate
and flexibly update nonverbal predictions.
We assessed nonverbal prediction in 12-24-month-old infants (n=50) with a looking-whilelistening task. In each trial, infants saw a central fixation, an 800ms delay, and a peripheral target
(Figure 1). To quantify how infants generated nonverbal predictions, we measured infants’
anticipatory eye movements (AEMs) to the target location. Trials appeared in two blocks, such
that infants saw 8 trials with a right peripheral target, followed by 8 trials with a left peripheral
target, or vice-versa. To quantify how infants flexibly updated nonverbal predictions, we
measured the proportion of infants’ AEMs to the novel target location in the second block. We
expected that infants with larger vocabularies would generate more nonverbal predictions
overall, and would be faster to update nonverbal predictions in the second block.
We found that high-MCDI infants did not generate more AEMs overall (r=-.12, p=.39).
However, high-MCDI infants did have a larger proportion of AEMs correct in the second block
(r=.39, p=.01). When high-MCDI infants made AEMs in the second block, they were more
likely to generate AEMs to the novel target location. In stark contrast, infants with smaller
vocabularies failed to update predictions to the novel target side, and continued to generate
inaccurate predictions (Figure 2). Critically, in the first trial of block 2, all infants’ AEMs were
to the block 1 target location. This suggests infants performed equally well in block 1, and block
2 differences are not driven by differences in initial learning.
This research makes two novel contributions to our understanding of prediction and language
learning. First, the link between flexibility in nonverbal prediction and vocabulary suggests that
individual differences in a domain-general capacity, prediction, have implications for domainspecific learning. Second, this link is apparent even in infancy, and may underlie differences in
verbal prediction years later. Given the vast opportunities to make inaccurate predictions in early
language acquisition, the ability to update predictions may be crucial for learning. In ongoing
experiments, we explore complex relations between nonverbal prediction, verbal prediction, and
language learning.
(494 words)
Figure 1: Prediction task schematic. Infants saw 2 blocks of trials. Each block included 8 trials,
with block 1 target side counterbalanced across participants. We defined an anticipatory eye
movement (AEM) as any periphery saccade between 1300 and 2500ms from trial onset.
Figure 2: Proportion of AEMs correct (i.e., to the novel target side) by trial for infants with
higher MCDI standard scores (n=22, M=64, SD=19) and infants with lower MCDI standard
scores (n=19, M=17, SD=9). In trial 9, all AEMs were to the block 1 target location. Some
infants (n=9) made no AEMs in block 2. Infants with larger vocabularies flexibly updated
nonverbal predictions, whereas infants with smaller vocabularies were slower to do so.