Abstract
One of the first tasks in language acquisition is word segmentation, a process to extract word forms from continuous speech streams. Statistical approaches to word segmentation have been shown to be a powerful mechanism, in which word boundaries are inferred from sequence statistics. This approach requires the learner to represent the frequency of units from syllable sequences, though accounts differ on how much statistical exposure is required. In this study, we examined the computational limit with which words can be extracted from continuous sequences. First, we discussed why two occurrences of a word in a continuous sequence is the computational lower limit for this word to be statistically defined. Next, we created short syllable sequences that contained certain words either two or four times. Learners were presented with these syllable sequences one at a time, immediately followed by a test of the novel words from these sequences. We found that, with the computationally minimal amount of two exposures, words were successfully segmented from continuous sequences. Moreover, longer syllable sequences providing four exposures to words generated more robust learning results. The implications of these results are discussed in terms of how learners segment and store the word candidates from continuous sequences.
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Notes
The computation of TPs makes use of the absolute frequency for different units: p(b|a) = count (ab)/count (a).
Incidentally, both of these studies report null results, and that learning did not change by lengthening the exposure period.
Many statistical word-segmentation studies use fade-in or fade-out to avoid providing word boundary information to learners, as sequence boundaries can potentially be regarded as cues for word boundaries. We examined this issue empirically in our analysis; see Results section.
This is another way in which our design differed from the Batterink (2017) design. In Batterink (2017), all the words in syllable streams have equal frequency (a characteristic from Saffran et al., 1996), which makes it difficult to disentangle transitional probability and frequency as the source of the learning effect: these factors can only be disentangled if the words in the sequence have a lower frequency than part-words, as Aslin et al. (1998) explained. We designed all of our sequences with an unbalanced frequency design from Aslin et al. (1998).
In the example sequence in Fig. 1, a counterbalance sequence is constructed by using [8,1] and [2,7] as low-frequency words, and [3,4] and [5,6] as part words, which means that the four words making up this counterbalancing sequence are [4,5], [6,3], [8,1] and [2,7], with a frequency of 4, 4, 2, and 2 respectively.
For all the syllables used in the study, we use a pinyin representation where the syllables are followed by a number from 1 through 4, such that the number represents the tone the syllable is in. In this example, both syllables xian and mo are in the second tone.
In Stata syntax, the equation here is: mixed key i.rc || langrecode: || subject:, where rc codes for word/part-word given the current sequence and counterbalancing condition.
In Stata syntax, the equation here is: mixed key i.rc##i.times || langrecode: || subject: , where rc codes for word/part-word given the current sequence and counterbalancing condition.
In Stata syntax, the equation here is: mixed key i.rc##i.langt || langrecode: || subject:
This was a linear regression for each condition, which in Stata syntax is: reg effect_size time_points.
In Stata syntax, the equations here are:
mixed key i.firstword || langrecode: || subject: if rc == 1
mixed key i.lastword || langrecode: || subject: if rc == 1
Though as we discussed in the Introduction, more exposure does not generate more learning in many cases. In the current experimental conditions, the reason for the increase in learning from more exposures may stem from the fact that the minimal exposure amount (two exposures) generated a very modest amount of learning to begin with.
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Author Note
The reported experiments were not preregistered. The data reported in this paper is available, at https://osf.io/che4q/?view_only=cca901e1ecb748178ecae2b6a5f1c31c. This work was supported by the Natural Science Foundation of China (No. 3217051) to Suiping Wang.
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Felix Hao Wang: Conceptualization, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. Meili Luo: Project administration, Writing ‐ review & editing. Suiping Wang: Resources, Writing ‐ review & editing.
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Hao Wang, F., Luo, M. & Wang, S. Statistical word segmentation succeeds given the minimal amount of exposure. Psychon Bull Rev (2023). https://doi.org/10.3758/s13423-023-02386-z
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DOI: https://doi.org/10.3758/s13423-023-02386-z