Statistical Learning of Syntax: The Role of Transitional Probability (2007)

Click to increase image sizeClick to decrease image size 1ACKNOWLEDGMENTS This research was supported in part by NIH Grant DC00167 to Elissa Newport, NIH Training Grant DC00035 to the University of Rochester, and NSF Grant SBR-9873477 to Richard Aslin and Elissa Newport. We thank Dick Aslin, Marie Coppola, Mike Tanenhaus, and Jeff Runner for helpful comments at all phases of this research, and to Susan Goldin-Meadow and three anonymous reviewers for their comments on this article. Notes There are also other conditionalized statistics, such as backward transitional probability, mutual information, conditional entropy, and correlation. All of these statistics are functionally equivalent in the research conducted thus far (including the research presented in this article), in that they normalize the frequency of co-occurrence of two elements by the frequency of one or both of those elements individually. Aslin et al. (1998) demonstrated that one of this class of conditionalized statistics predicts learning. Transitional probability was first used for psycholinguistic materials by CitationMiller and Selfridge (1950), and, for the sake of simplicity, we refer to this statistic throughout the article. But it is important to note that our findings are also compatible with the claim that learners are computing another closely related statistic, such as mutual information or conditional entropy. We discuss these issues further in the General Discussion. The 18 CVCs (hereafter, words) were selected based on two criteria. First, each word scored between 70 and 80 on an index of the meaningfulness (to English speakers) of all possible CVC trigrams (CitationArcher, 1960). Second, with respect to the neighborhood density of their phonotactics, none of the words appeared on the lists of either low-probability or high-probability nonwords in CitationVitevitch and Luce (1999). Hence, each CVC had a medium-probability neighborhood density of phonotactics. The only cue to this phrasal grouping is the pattern in the order of the words and the statistics created by this pattern. There are no other cues to phrase structure, such as pauses between phrases or intonation variations. Table 2 shows the major transitional probabilities for each condition-that is, the transitional probabilities that characterize the baseline sentences for each language. Of course, participants are exposed to other nonzero transitional probabilities created by the specific manipulations of each condition, but these are always relatively small. For example, the transition from B to C (as shown in Table 2) is 0.8, but B can also be followed by E (in sentences where the CD phrase is optional), with a transitional probability of 0.2. We allowed the AF pair to be optional, even though A and F are not adjacent. This was necessary for the endpoint classes to have statistical distributions similar to the other form classes. Several reviewers have asked whether our results could arise instead from serial position effects; that is, could better learning of beginnings and ends of sentences (sometimes seen in other miniature language experiments; see CitationMorgan et al., 1987) interact with our syntactic manipulations to produce the overall better learning in the experimental condition? We discuss this hypothesis more fully in the General Discussion section, but for now it is important to note that there are no significant differences in any of our experiments between learning of the beginnings and ends of sentences and those of sentence middles, and no significant interactions between this serial position contrast and condition. Across experiments and conditions, the different syntactic features (e.g., optional versus repeated phrases) created different length sentences. We therefore equated conditions for the total number of words (and phrases) in the presentation sets rather than for the number of sentences. We then adjusted the isi slightly to equate conditions for overall time of exposure. This variation in the number of words per class creates classes of words that are high in frequency, namely, the B, D, and F classes. (Because there are only two words in each of these classes, these words will be relatively frequent in the input set.) This may facilitate learning (for evidence that having high frequency markers in a miniature language facilitates grammar learning, see CitationValian & Coulson, 1988). Even though the class size variation condition had a different assignment of words to word classes, we were able to use the same Sentence Test because it used only those items (15/18 words) that had identical assignments in all languages. The Phrase Test, however, had to be slightly altered for the class size condition (this modified Phrase Test was also used for the all-combined conditions in Experiments 3 and 4). The other experimental conditions also had peaks and dips in transitional probability between individual words; however, these peaks and dips were quite similar to those between word classes. Specifically, they were each approximately one third of the transitional probabilities between word classes (because there were three words per class). The all-combined 5% control condition does show a significant negative correlation on Day 5, indicating that participants chose as the "better group or unit" those pairs of words to which they had been exposed less frequently in the input set. Of course, learners must first (or concurrently) induce the word classes, as these are not transparent in the stream of words to which listeners are exposed. Other researchers have suggested how this might be accomplished via distributional analyses (e.g., CitationMintz et al., 2002). One possibility is that these processes are interleaved: Learners might initially track the distributions of a small number of individual words, form word classes from these, and then begin to track transitional probabilities among these word classes, while continuing to add individual words to the classes. Unfortunately, with only three words per class and two classes per phrase, we could not do the analogous procedure in test items for the Phrase Test; to make sure that participants learned the word classes, they were exposed to all legal combinations of words within each phrase. In future studies, it might be helpful to use a larger number of words per class and to leave some of the word combinations for presentation as novel items in the Phrase Test as well.