Learning Of Nonadjacent Dependencies Research Articles

Successfully learning non-adjacent dependencies in a continuous artificial language stream

Much of the statistical learning literature has focused on adjacent dependency learning, which has shown that learners are capable of extracting adjacent statistics from continuous language streams. In contrast, studies on non-adjacent dependency learning have mixed results, with some showing success and others failure. We review the literature on non-adjacent dependency learning and examine various theories proposed to account for these results, including the proposed necessity of the presence of pauses in the learning stream, or proposals regarding competition between adjacent and non-adjacent dependency learning such that high variability of middle elements is beneficial to learning. Here we challenge those accounts by showing successful learning of non-adjacent dependencies under conditions that are inconsistent with predictions of previous theories. We show that non-adjacent dependencies are learnable without pauses at dependency edges in a variety of artificial language designs. Moreover, we find no evidence of a relationship between non-adjacent dependency learning and the robustness of adjacent statistics. We demonstrate that our two-step statistical learning model can account for all of our non-adjacent dependency learning results, and provides a unified learning account of adjacent and non-adjacent dependency learning. Finally, we discussed the theoretical implications of our findings for natural language acquisition, and argue that the dependency learning process can be a precursor to other language acquisition tasks that are vital to natural language acquisition.

Individual differences in non-adjacent statistical dependency learning in infants.

There is considerable controversy over the factors that shape infants' developing knowledge of grammar. Work with artificial languages suggests that infants' ability to track statistical regularities within the speech they hear could, in principle, support grammatical development. However, little work has tested whether infants' performance on laboratory tasks reflects factors that are relevant in real-world language learning. Here we tested whether the language that infants hear at home, and their receptive language skills, predict their performance on tasks assessing the ability to learn non-adjacent statistical dependencies (NADs) at 15 months, and whether that in turn predicts sensitivity to native-language NADs at 18 months. We found evidence for some (though not all) of these relations, and primarily for females. The results suggest that performance on the artificial language-learning task reveals something about the mechanisms of grammatical development, and that females and males may be learning NADs differently.

Concurrent Learning of Adjacent and Nonadjacent Dependencies in Visuo-Spatial and Visuo-Verbal Sequences.

Both adjacent and non-adjacent dependencies (AD and NAD) are present in natural language and other domains, yet the learning of non-adjacent sequential dependencies generally only occurs under favorable circumstances. It is currently unknown to what extent adults can learn AD and NAD, presented concurrently in spatial and verbal sequences during a single session, and whether a second session improves performance. In addition, the relationship between AD and NAD learning and other theoretically related cognitive and language processes has not yet been fully established. In this study, participants reproduced two types of sequences generated from an artificial grammar: visuo-spatial sequences with stimuli presented in four spatial locations, and visuo-verbal sequences with printed syllables. Participants were tested for incidental learning by reproducing novel sequences, half consistent with the grammar and half containing violations of either AD or NAD. The procedure was repeated on a second day. Results showed that both AD and NAD were learned in both visuo-spatial and visuo-verbal tasks, although AD learning was better than NAD and learning of NAD decreased over time. Furthermore, NAD learning for both spatial and verbal tasks was positively correlated with a language measure, whereas AD learning for both spatial and verbal tasks was negatively associated with working memory measures in the opposite domain. These results demonstrate that adults can learn both AD and NAD within a single session, but NAD learning is more easily disrupted than AD and both types of learning are sub-served by partially distinct cognitive processes. These findings increase our understanding of the processes governing the learning of AD and NAD in verbal and spatial domains.

Auditory statistical learning in children: Novel insights from an online measure

AbstractNonadjacent dependency learning is thought to be a fundamental skill for syntax acquisition and often assessed via an offline grammaticality judgment measure. Asking judgments of children is problematic, and an offline task is suboptimal as it reflects only the outcome of the learning process, disregarding information on the learning trajectory. Therefore, and following up on recent methodological advancements in the online measurement of nonadjacent dependency learning in adults, the current study investigates if the recording of response times can be used to establish nonadjacent dependency learning in children. Forty-six children (mean age: 7.3 years) participated in a child-friendly adaptation of a nonadjacent dependency learning experiment (López-Barroso, Cucurell, Rodríguez-Fornells, & de Diego-Balaguer, 2016). They were exposed to an artificial language containing items with and without nonadjacent dependencies while their response times (online measure) were measured. After exposure, grammaticality judgments (offline measure) were collected. The results show that children are sensitive to nonadjacent dependencies, when using the online measure (the results of our offline measure did not provide evidence of learning). We therefore conclude that future studies can use online response time measures (perhaps in addition to the offline grammaticality judgments) to further investigate nonadjacent dependency learning in children.

Learning nonadjacent dependencies embedded in sentences of an artificial language: When learning breaks down.

The structure of natural languages give rise to many dependencies in the linear sequences of words, and within words themselves. Detecting these dependencies is arguably critical for young children in learning the underlying structure of their language. There is considerable evidence that human adults and infants are sensitive to the statistical properties of sequentially adjacent items. However, the conditions under which learners detect nonadjacent dependencies (NADs) appears to be much more limited. This has resulted in proposals that the kinds of learning mechanisms learners deploy in processing adjacent dependencies are fundamentally different from those deployed in learning NADs. Here we challenge this view. In 4 experiments, we show that learning both kinds of dependencies is hindered in conditions when they are embedded in longer sequences of words, and facilitated when they are isolated by silences. We argue that the findings from the present study and prior research is consistent with a theory that similar mechanisms are deployed for adjacent and nonadjacent dependency learning, but that NAD learning is simply computationally more complex. Hence, in some situations NAD learning is only successful when constraining information is provided, but critically, that additional information benefits adjacent dependency learning in similar ways. (PsycINFO Database Record

Top-down structure influences learning of nonadjacent dependencies in an artificial language.

Because of the hierarchical organization of natural languages, words that are syntactically related are not always linearly adjacent. For example, the subject and verb in the child always runs agree in person and number, although they are not adjacent in the sequences of words. Since such dependencies are indicative of abstract linguist structure, it is of significant theoretical interest how these relationships are acquired by language learners. Most experiments that investigate nonadjacent dependency (NAD) learning have used artificial languages in which the to-be-learned dependencies are isolated, by presenting the minimal sequences that contain the dependent elements. However, dependencies in natural language are not typically isolated in this way. We report the first demonstration to our knowledge of successful learning of embedded NADs, in which silences do not mark dependency boundaries. Subjects heard passages of English with a predictable structure, interspersed with passages of the artificial language. The English sentences were designed to induce boundaries in the artificial languages. In Experiment 1 & 3 the artificial NADs were contained within the induced boundaries and subjects learned them, whereas in Experiment 2 & 4, the NADs crossed the induced boundaries and subjects did not learn them. We take this as evidence that sentential structure was "carried over" from the English sentences and used to organize the artificial language. This approach provides several new insights into the basic mechanisms of NAD learning in particular and statistical learning in general. (PsycINFO Database Record

Bridging the gap: Learning of acoustic nonadjacent dependencies by a songbird.

Many animal species can detect dependencies between adjacent visual or auditory items in a string. Compared with adjacent dependencies, detecting nonadjacent dependencies, as present in linguistic constructions, is more challenging as this requires detecting a relation between items irrespective of the number and nature of the intervening items. There is limited evidence that nonhuman animals can detect such dependencies. An animal group in which such abilities might be expected is songbirds, which have learned songs consisting of a series of vocal elements given in specific sequences. So far no songbird (or other bird species) has been tested for its ability to detect nonadjacent dependencies. We examined whether zebra finches can detect the dependencies between items at the edges of artificially arranged strings of song elements. Zebra finches were trained to discriminate 2 sets of dependent song elements that always appeared in the same order (A and B; C and D), from other element combinations (AD, AC, BD, CB, CA, DB). The element combinations were separated by intervening (I) elements. Subsequent tests revealed that the finches could generalize the learned dependencies over different numbers and types of intervening items. Our findings show that the ability for detecting nonadjacent dependencies is not limited to humans or primates, and lend support to theories that suggest that nonadjacent dependencies can be learned by a nonlinguistic associative learning process. (PsycINFO Database Record

Nonadjacent Dependency Learning in Cantonese-Speaking Children With and Without a History of Specific Language Impairment

This study investigated nonadjacent dependency learning in Cantonese-speaking children with and without a history of specific language impairment (SLI) in an artificial linguistic context. Sixteen Cantonese-speaking children with a history of SLI and 16 Cantonese-speaking children with typical language development (TLD) were tested with a nonadjacent dependency learning task using artificial languages that mimic Cantonese. Children with TLD performed above chance and were able to discriminate between trained and untrained nonadjacent dependencies. However, children with a history of SLI performed at chance and were not able to differentiate trained versus untrained nonadjacent dependencies. These findings, together with previous findings from English-speaking adults and adolescents with language impairments, suggest that individuals with atypical language development, regardless of age, diagnostic status, language, and culture, show difficulties in learning nonadjacent dependencies. This study provides evidence for early impairments to statistical learning in individuals with atypical language development.

Supplemental Material for Bridging the Gap: Learning of Acoustic Nonadjacent Dependencies by a Songbird

Supplemental Material for Bridging the Gap: Learning of Acoustic Nonadjacent Dependencies by a Songbird

Gleaning Structure from Sound: The Role of Prosodic Contrast in Learning Non-adjacent Dependencies.

The ability to detect non-adjacent dependencies (i.e. between a and b in aXb) in spoken input may support the acquisition of morpho-syntactic dependencies (e.g. The princess is kiss ing the frog). Functional morphemes in morpho-syntactic dependencies are often marked by perceptual cues that render them distinct from lexical elements. We use an artificial grammar learning experiment with adults to investigate the role of perceptual cues in non-adjacent dependency learning, by manipulating the perceptual/prosodic properties of the a / b elements in aXb strings and testing participants’ incidental learning of these dependencies. Our results show that non-adjacent dependencies are learned both when the dependent elements are perceptually prominent, and when they are perceptually reduced compared to the intervening material (in the same way that functional words are reduced compared to lexical words), but only if integrated into a natural prosodic contour. This result supports the idea that the prosodic properties of natural languages facilitate non-adjacent dependency learning.

Prosodic cues enhance rule learning by changing speech segmentation mechanisms

Prosody has been claimed to have a critical role in the acquisition of grammatical information from speech. The exact mechanisms by which prosodic cues enhance learning are fully unknown. Rules from language often require the extraction of non-adjacent dependencies (e.g., he plays, he sings, he speaks). It has been proposed that pauses enhance learning because they allow computing non-adjacent relations helping word segmentation by removing the need to compute adjacent computations. So far only indirect evidence from behavioral and electrophysiological measures comparing learning effects after exposure to speech with and without pauses support this claim. By recording event-related potentials during the acquisition process of artificial languages with and without pauses between words with embedded non-adjacent rules we provide direct evidence on how the presence of pauses modifies the way speech is processed during learning to enhance segmentation and rule generalization. The electrophysiological results indicate that pauses as short as 25 ms attenuated the N1 component irrespective of whether learning was possible or not. In addition, a P2 enhancement was present only when learning of non-adjacent dependencies was possible. The overall results support the claim that the simple presence of subtle pauses changed the segmentation mechanism used reflected in an exogenously driven N1 component attenuation and improving segmentation at the behavioral level. This effect can be dissociated from the endogenous P2 enhancement that is observed irrespective of the presence of pauses whenever non-adjacent dependencies are learned.

Concurrent Statistical Learning of Adjacent and Nonadjacent Dependencies

When children learn their native language, they have to deal with a confusing array of dependencies between various elements in an utterance. The dependent elements may be adjacent to one another or separated by intervening material. Prior studies suggest that nonadjacent dependencies are hard to learn when the intervening material has little variability, which may be due to a trade‐off between adjacent and nonadjacent learning. In this study, we investigate the statistical learning of adjacent and nonadjacent dependencies under low intervening variability using a modified serial reaction time (SRT) task. Young adults were trained on mixed sets of materials comprising equally probable adjacent and nonadjacent dependencies. Offline tests administered after training showed better performance for adjacent than nonadjacent dependencies. However, online SRT data indicated that the participants developed sensitivity to both types of dependencies during training, with no significant differences between dependency types. The results demonstrate the value of online measures of learning and suggest that adjacent and nonadjacent learning can occur together even when there is low variability in the intervening material.

Is an attention-based associative account of adjacent and nonadjacent dependency learning valid?

Pacton and Perruchet (2008) reported that participants who were asked to process adjacent elements located within a sequence of digits learned adjacent dependencies but did not learn nonadjacent dependencies and conversely, participants who were asked to process nonadjacent digits learned nonadjacent dependencies but did not learn adjacent dependencies. In the present study, we showed that when participants were simply asked to read aloud the same sequences of digits, a task demand that did not require the intentional processing of specific elements as in standard statistical learning tasks, only adjacent dependencies were learned. The very same pattern was observed when digits were replaced by syllables. These results show that the perfect symmetry found in Pacton and Perruchet was not due to the fact that the processing of digits is less sensitive to their distance than the processing of syllables, tones, or visual shapes used in most statistical learning tasks. Moreover, the present results, completed with a reanalysis of the data collected in Pacton and Perruchet (2008), demonstrate that participants are highly sensitive to violations involving the spacing between paired elements. Overall, these results are consistent with the Pacton and Perruchet's single-process account of adjacent and nonadjacent dependencies, in which the joint attentional processing of the two events is a necessary and sufficient condition for learning the relation between them, irrespective of their distance. However, this account should be completed to encompass the notion that the presence or absence of an intermediate event is an intrinsic component of the representation of an association.

Impaired statistical learning of non-adjacent dependencies in adolescents with specific language impairment.

Being able to track dependencies between syntactic elements separated by other constituents is crucial for language acquisition and processing (e.g., in subject-noun/verb agreement). Although long assumed to require language-specific machinery, research on statistical learning has suggested that domain-general mechanisms may support the acquisition of non-adjacent dependencies. In this study, we investigated whether individuals with specific language impairment (SLI)—who have problems with long-distance dependencies in language—also have problems with statistical learning of non-adjacent relations. The results confirmed this hypothesis, indicating that statistical learning may subserve the acquisition and processing of long-distance dependencies in natural language.

The development of nonadjacent dependency learning in natural and artificial languages.

Nonadjacent dependencies occur over one or more intervening units and require learners to track discontinuous sequential relationships. These discontinuous relationships are present at multiple levels in language (e.g., as seen in morphosyntactic dependencies and at the phonological level in vowel harmony). Experiments suggest that these dependencies are acquired using statistical learning mechanisms and that this learning is also affected by perceptual biases. Artificial and natural language studies have shown that infants are sensitive to these statistical regularities but there appear to be developmental constraints on learning. Developmental investigations have also examined how knowledge and processing of the intervening elements affect learning, and whether categories can be acquired using nonadjacent dependency information. WIREs Cogn Sci 2013, 4:511-522. doi: 10.1002/wcs.1244 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.

All together now: concurrent learning of multiple structures in an artificial language.

Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics influenced learning of both adjacent and non-adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants' knowledge of the deterministic non-adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition.

Non-adjacent dependency learning in infants at familial risk of dyslexia

This study tests the hypothesis that developmental dyslexia is (partly) caused by a deficit in implicit sequential learning, by investigating whether infants at familial risk of dyslexia can track non-adjacent dependencies in an artificial language. An implicit learning deficit would hinder detection of such dependencies, which mark grammatical relations (e.g. between 'is' and '-ing' in 'she is happily singing'). In a head-turn experiment with infants aged 1;6, family risk and typically developing infants were exposed to one of two novel languages containing dependencies of the type a-X-c, b-X-d or a-X-d, b-X-c, with fixed first and third elements and twenty-four different X elements. During test, typically developing children listened longer to ungrammatical strings (i.e. that did not correspond to their training language). However, family-risk children did not discriminate between grammatical and ungrammatical strings, indicating deficient implicit learning. The implications of these findings in relation to dyslexia and other language-based disorders are discussed.

Statistical learning of probabilistic nonadjacent dependencies by multiple-cue integration

Previous studies have indicated that dependencies between nonadjacent elements can be acquired by statistical learning when each element predicts only one other element (deterministic dependencies). The present study investigates statistical learning of probabilistic nonadjacent dependencies, in which each element predicts several other elements with a certain probability, as is more common in natural language. Three artificial language learning experiments compared statistical learning of deterministic and probabilistic nonadjacent dependencies. In Experiment 1, participants listened to sequences of three non-words containing either deterministic or probabilistic dependencies between the first and the last non-words. Participants exposed to deterministic dependencies subsequently distinguished correct sequences from sequences that violated the nonadjacent dependencies; participants exposed to probabilistic dependencies did not. However, when visual (Experiment 2) and phonological cues (Experiment 3) were added participants learned both kinds of dependencies, demonstrating statistical learning of probabilistic nonadjacent dependencies when additional cues are present.

Statistical segmentation of streams of syllables: a pilot EEG study

Statistical segmentation of streams of syllables: a pilot EEG study

Mass counts: ERP correlates of non-adjacent dependency learning under different exposure conditions

Miniature language learning can serve to model real language learning as high proficiency can be reached after very little exposure. In a previous study by Mueller et al. [18] German participants acquired non-adjacent syntactic dependencies by mere exposure to correct Italian sentences, but their ERP pattern differed from the one shown by native speakers. The present study follows up on that experiment using a similar design and material and is focused on two important issues: the influence of acoustic cues in the material and the impact of the learning procedure. With respect to the latter we compared alternating learning and test phases to a continuous learning and test phase. In addition, a splicing procedure eliminated prosodic cues in order to ensure that non-adjacent dependencies were learned instead of adjacent ones. Results for the continuous phase design showed a native-like biphasic ERP pattern, an N400 followed by a left-focused positivity. In the alternating design behavioural accuracy was lower and only an N400 was found. The results suggest an advantage of continuous learning phases for adult learners, possibly due to the absence of ungrammatical items present in the test phases in the alternating learning procedure. Furthermore, the replication of the earlier study with prosodically controlled material adds evidence to the general finding that syntactic non-adjacent dependencies can be learned from mere exposure to correct examples.