One startling statistic computed by Haith (1980) is that the average 2-month-old infant has sampled its visual environment with over 250,000 fixations (looking

times between saccades) since birth. Despite the logical advantage of the foregoing constraints—which surely must assist in dealing with Problem 2—it is nevertheless the case that laboratory demonstrations of statistical learning are highly simplified compared to what an infant is actually confronted with in the natural environment. Thus, we should be concerned that such demonstrations are little more than proof of concept that under ideal conditions a statistical-learning Raf inhibition mechanism can solve certain tasks. But does this mechanism “scale up” to more natural and complex learning tasks? There are two answers to this question, at least

for studies of statistical selleck learning in the language domain. First, a variety of corpus analyses (Frank, Goldwater, Griffiths, & Tenenbaum, 2010; Swingley, 2005) have shown that, to a first approximation, the same types of statistical information manipulated in the laboratory are present in real language input to infants. Yet in real corpora, these statistical cues are less reliable, and thus, one worries that no one cue alone is sufficient. It is important to note, for historical purposes, that initial claims about statistical learning made precisely this point: “Although experience with speech in the real world is unlikely to be as concentrated Florfenicol as it was in these studies, infants in more natural settings presumably benefit from other types of cues correlated with statistical information (p. 1928)” (Saffran et al.,

1996). Laboratory studies that eliminate all potentially useful cues except one serve the purpose of showing that the sole cue present in the input is sufficient for learning. But such studies cannot confirm that in the natural environment, where many cues are correlated, any given cue plays a necessary role in learning. The second answer to the “scale up” question is to conduct laboratory experiments in which two or more cues are presented in combination to see which one “wins” or how each cue is “weighted” in the statistical-learning process. Early work that followed this strategy suggested that statistical cues “trump” prosodic cues (Thiessen & Saffran, 2003), at least at the level of lexical prosody (i.e., whether 2-syllable words have a strong-weak or a weak-strong stress pattern). The reason that lexical prosody might take a back seat to statistics is that prosody is language-specific, whereas syllable statistics, at least in most languages, are not. Yet there are other levels of prosody that are language-general and so could reasonably serve as universal constraints on which statistics are computed.

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