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While no one has explored the role of effort with sensitive period effects in mind, studies have explored the role of attention in word segmentation. Two lines of evidence suggest that learning in these kinds of experiments is largely procedural. First, young infants and other mammals who are less likely to be consciously exerting attentional resources can perform word segmentation using TPs [33] — [35].

Second, both children and adults can do this use TPs for word segmentation when engaged in a non-attentionally taxing alternate cover task with the stimuli playing in the background [36]. They cannot be computing TPs because they are actively trying to, given that they are unaware that they will later be tested on their knowledge of aspects of the stimuli involving TPs. Given this, one might think that attention is not helpful for learning aspects of language involving these kinds of computations. Although the studies just discussed show that attention is not necessary, other studies show that it can be beneficial for statistical learning.

For instance, directing adults' attention towards a distractor task can impair learning [37]. Likewise, directing adults' attention to a subset of stimuli results in successful segmentation of the attended, but not unattended, items [38]. Moreover, manipulating attention has a greater impact on the segmentation when words have lower less predictive versus higher TPs [39]. In sum, when attention is directed toward the stimulus, learning is better than when it is not.

When it is taxed for another purpose and turned away from the to-be-learned stimulus, learning is impaired, and, when attention is not taxed, but another non-taxing cover task is used, learning occurs as normal. These studies therefore show that learning is improved with attention. This stands in contrast to the above-cited studies on other forms of procedural learning where effort appears to harm the learning of a markov chain grammar or a complex alternating sequence. The difference, we suggest, is in the nature of the material to be learned.

Word segmentation involves the learning or extraction of specific items and the relationships between them. Attention is beneficial in this kind of simple task.


As discussed above, however, while aspects of complex patterns can be learned without effort and attention, when effort is directed at them it can be harmful if the learner is left to search a limitless hypothesis space and left with no information about what kind of pattern they are looking for [30] , [40] cf. We therefore ask whether effort facilitates or impairs adults' learning of certain aspects of language that 1 are learned similarly by children and adults, and 2 that adults are known to have difficulty learning.

Word segmentation may not seem like a natural contrast to grammar. From the perspective of the sensitive period however, word segmentation ability is relatively age-invariant [36] in the absence of attention, and is item-based, making it ideal given our hypothesis. Adults may also learn word meanings differently than children [42] and so vocabulary learning might not be as age-invariant as has been previously assumed.

If our ideas are correct, effort should facilitate word-segmentation, but harm the learning of novel grammatical categories and their behavior. Although studies have demonstrated that both word segmentation and grammar learning can occur based purely on distributional information in artificial languages [43] — [46] [even simultaneously, [47] ] , to our knowledge, no one has compared learning of the two kinds of information, particularly from the perspective of understanding the sensitive period for language acquisition. Therefore in the first experiment, adult learners were exposed to a continuous speech stream containing TP-defined words organized into categories, which occurred in a consistent order.

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We assessed whether participants had 1 segmented the words, and 2 learned the categories. Learners' attention was not directed towards the stimulus allowing us to assess the outcome of implicit learning. Twenty-two native English-speaking undergraduates mean age: Written consent was obtained from these and all participants in the study. The institutional review board at the University of California, Berkeley approved this study. The exposure speech stream lasted just under 10 minutes and was constructed using nine two-syllable words strung together without pauses or other acoustic cues to word boundaries.

Each word belonged to one of three categories A, B, C. Category members shared a phonological structure as well as distribution Figure 1. All words and syllables were consistent with English phonotactics but were not meaningful words in English. A words were followed by B words, which were followed by C words, which were then followed by A words, and so on.

Each word occurred 45 times. Since different TPs have been shown to be differentially affected by attention [39] , TP variability was created by constraining the presentation order such that one word from each category never followed another particular word from the preceding category. Words were otherwise presented randomly.

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TPs were 1. However, category-to-category TPs were 1. Thus, word order was much more predictable at the level of categories than at the level of syllables or words. This kind of shared phonological structure mimics tendencies in real languages in exactly the kinds of categories that adults have difficulty learning e. It is also known to assist adult learners in acquiring categories in similar experiments, as compared to the use of purely distributional information [48].

Thus, the phonological cue should make category learning and therefore ordering, since you need the category to learn its ordering easier. Importantly, this is the very kind of abstract category structure that adults have difficulty learning, and so might be susceptible to the effort effect we are examining. An example stimulus stream is as follows: …mukuhbehodfeynoytdubah kahulmufop…. The artificial language stimulus stream and test items were generated with text-to-speech software that uses terminal analog formant synthesis and not pre-recorded di-phones [49].

This was chosen over natural speech and diphone based methods to eliminate segmentation cues that were not experimentally relevant including those that indicate a segment's location in the syllable; i.

All of the vowels in all of the stimuli and tests were the same length ms and consonants ranged from 60 ms to ms but were always the same for that phone regardless of their location. These lengths were automatically generated using the average speaking rate setting in the software.

click After exposure, participants completed two forced choice tests: 1 a word-level test in which they were asked which of two words was more likely to belong in the language they just listened to, and 2 a sentence-level test in which they were asked which sentence was more likely to belong in the language they just listened to. They always completed the word-level test first and test items for each test were randomized separately for each subject.

In these 2 tests, there were three test types of interest: word segmentation, order and category structure. All word segmentation items occurred in the first, word-level, test and all of the order and category structure occurred in the second, sentence-level, test. The word segmentation test assessed whether participants had extracted the words defined by TPs from the speech stream.

TPs for non-words were always 0 and part words were. There were 9 of each type 18 total. In the order test, participants were asked to compare strings that followed the correct order with strings that did not the lack of pauses combined with the TP structure at the category-level means that there is no real start or end to a multi-item string; C—A—B is just as correct as A—B—C.

Test items comprised two strings with the same words and same first word, but in different orders, e. A mu kuh -B kah ul -C ti behd vs. A mu kuh -C ti behd -B kah ul. The across-word TPs for the ungrammatical strings was 0—0 both at the level of the syllable and at the level of the category C never comes after A, nor B after C. From here forward test items are described only in terms of word- or syllable-level TPs, since the category-level TPs are always 1—1 for grammatical and 0—0 for ungrammatical strings.

The across-word TPs in the grammatical strings was either.

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There were 21 test items. The category structure test probed learning with the use of novel words.

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These novel words were put into grammatical and ungrammatical locations, in strings with and without TP cues, creating 3 subtypes: Novel-with-TP , Novel-no-TP and Novel-good-vs. In novel-with-TP items, a category-congruent novel word was placed in either the correct or incorrect order and TP cues were present in the grammatical string i. These test items indicate how learners deal with novelty when a distributional cue is present. In Novel-no-TP items, learners were asked to compare strings with a category-congruent novel word in one of two places as before , but with no TP cue A-B novel -C vs.

Finally, Novel-good-vs. Correct performance on the latter two types requires knowledge of which phonological structures belong in which relative positions. Table 1 lists the structure of each of the test items by category and sub-type.

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See Methods S1 for further details on the methods and stimuli. Exposure and testing were conducted individually. Participants were told to listen to an artificial language and neither over-think nor ignore it. To encourage this and following the procedure in the original paper showing that statistical learning can occur incidentally [36] , participants were asked to color during exposure. After exposure, participants completed the forced choice tests.

All tests were administered on a computer using E-Prime software [50]. Performance on the word-segmentation items is shown in Figure 2a. Therefore, more fine-grained TP comparisons. Performance on word segmentation a,d , order b,e and c,f category-structure sub tests. Violin plots a—c depict the minimum bottom of shape and maximum top of shape observed values. Black circles indicate the group mean, and width indicates the probability density of the value on the corresponding y axis. Chance performance is indicated with the dotted line. Bar graphs d—f depict the group mean. Error bars reflect standard error of the group mean and chance performance is indicated with the dotted line.

More fine-grained TP comparisons. Performance on this second measure without a TP cue indicates participants have learned the abstract phonologically-defined category structure, as well as how the categories are ordered, and that they do not need familiar sequences of words to distinguish a grammatical from an ungrammatical string. Experiment 1 established that individuals can segment word-like units, extract information about the order of categories, and learn something about the phonological structure of said categories under typical implicit learning conditions.

In the next experiment we explore effortful learning. Sixty-six native-English speaking undergraduates mean age: Working memory ability was measured via the reading span task [51] , [52]. All stimuli and tests were the same as Experiment 1. As in Experiment 1, all tests were administered using E-prime software for the first and second conditions described below, effort towards words and effort towards kinds respectively and using Psychopy software [53] for the final condition effort toward order, described below. All were warned that there were no pauses between the words.

To ensure continued attention, participants were given a task during exposure. They were asked to press one of two buttons over the course of learning to indicate their knowledge of the aspect of the language they were trying to learn. They could do this as many times as they like, but were asked to do this each time they had a strong idea or hypothesis about what one might be. They were asked to be more conservative with this button, but they were also told that it was fine to press this more than the number of items that they were trying to learn so, e.

This manipulation had the effect of focusing subjects on the learning task at hand. Indeed, many indicated that pressing the button was quite rewarding during an otherwise rather boring task.