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      Non‐adjacent Dependency Learning in Humans and Other Animals


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          Learning and processing natural language requires the ability to track syntactic relationships between words and phrases in a sentence, which are often separated by intervening material. These nonadjacent dependencies can be studied using artificial grammar learning paradigms and structured sequence processing tasks. These approaches have been used to demonstrate that human adults, infants and some nonhuman animals are able to detect and learn dependencies between nonadjacent elements within a sequence. However, learning nonadjacent dependencies appears to be more cognitively demanding than detecting dependencies between adjacent elements, and only occurs in certain circumstances. In this review, we discuss different types of nonadjacent dependencies in language and in artificial grammar learning experiments, and how these differences might impact learning. We summarize different types of perceptual cues that facilitate learning, by highlighting the relationship between dependent elements bringing them closer together either physically, attentionally, or perceptually. Finally, we review artificial grammar learning experiments in human adults, infants, and nonhuman animals, and discuss how similarities and differences observed across these groups can provide insights into how language is learned across development and how these language‐related abilities might have evolved.


          Wilson et al. focus on one class of AGL tasks: the cognitively demanding task of detecting non‐adjacent dependencies (NADs) among items. They provide a typology of the different types of NADs in natural languages and in AGL tasks. A range of cues affect NAD learning, ranging from the variability and number of intervening elements to the presence of shared prosodic cues between the dependent items. These cues, important for humans to discover non‐adjacent dependencies, are also found to facilitate NAD learning in several nonhuman animal species.

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          Statistical learning by 8-month-old infants.

          Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.
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            Statistical learning of tone sequences by human infants and adults.

            Previous research suggests that language learners can detect and use the statistical properties of syllable sequences to discover words in continuous speech (e.g. Aslin, R.N., Saffran, J.R., Newport, E.L., 1998. Computation of conditional probability statistics by 8-month-old infants. Psychological Science 9, 321-324; Saffran, J.R., Aslin, R.N., Newport, E.L., 1996. Statistical learning by 8-month-old infants. Science 274, 1926-1928; Saffran, J., R., Newport, E.L., Aslin, R.N., (1996). Word segmentation: the role of distributional cues. Journal of Memory and Language 35, 606-621; Saffran, J.R., Newport, E.L., Aslin, R.N., Tunick, R.A., Barrueco, S., 1997. Incidental language learning: Listening (and learning) out of the corner of your ear. Psychological Science 8, 101-195). In the present research, we asked whether this statistical learning ability is uniquely tied to linguistic materials. Subjects were exposed to continuous non-linguistic auditory sequences whose elements were organized into 'tone words'. As in our previous studies, statistical information was the only word boundary cue available to learners. Both adults and 8-month-old infants succeeded at segmenting the tone stream, with performance indistinguishable from that obtained with syllable streams. These results suggest that a learning mechanism previously shown to be involved in word segmentation can also be used to segment sequences of non-linguistic stimuli.
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              Implicit learning and statistical learning: one phenomenon, two approaches.

              The domain-general learning mechanisms elicited in incidental learning situations are of potential interest in many research fields, including language acquisition, object knowledge formation and motor learning. They have been the focus of studies on implicit learning for nearly 40 years. Stemming from a different research tradition, studies on statistical learning carried out in the past 10 years after the seminal studies by Saffran and collaborators, appear to be closely related, and the similarity between the two approaches is strengthened further by their recent evolution. However, implicit learning and statistical learning research favor different interpretations, focusing on the formation of chunks and statistical computations, respectively. We examine these differing approaches and suggest that this divergence opens up a major theoretical challenge for future studies.

                Author and article information

                Top Cogn Sci
                Top Cogn Sci
                Topics in Cognitive Science
                John Wiley and Sons Inc. (Hoboken )
                08 September 2018
                July 2020
                : 12
                : 3 , Commentaries on Rafael Núñez's article, “What happened to cognitive science?” (Topic Continuation) Editor: Wayne D. Gray – Learning Grammatical Structures: Developmental, Cross‐species and Computational Approaches Editors: Carel ten Cate, Clara Levelt, Judit Gervain, Chris Petkov and Willem Zuidema – Best of Papers from the 17th International Conference on Cognitive Modeling Editors: Terrence C. Stewart and Christopher Myers ( doiID: 10.1111/tops.v12.3 )
                : 843-858
                [ 1 ] Institute of Neuroscience Newcastle University
                [ 2 ] Department of Cognitive Biology University of Vienna
                [ 3 ] Research Department Sealcentre Pieterburen
                [ 4 ] Artificial Intelligence Lab Vrije Universiteit Brussel
                [ 5 ] Institute of Cognitive Science University of Osnabrueck
                [ 6 ] Departments of Psychology and Linguistics University of Southern California
                [ 7 ] Utrecht Institute of Linguistics OTS Utrecht University
                [ 8 ] Department of Linguistics University of Potsdam
                [ 9 ] Centre for Language Evolution University of Edinburgh
                [ 10 ] CNRS Aix‐Marseille University
                Author notes
                [*] [* ]Correspondence should be sent to Benjamin Wilson, Institute of Neuroscience, Newcastle University Medical School, Newcastle Upon Tyne, NE2 4HH, UK. E‐mail: benjamin.wilson@ 123456newcastle.ac.uk
                © 2018 The Authors Topics in Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 3, Tables: 0, Pages: 16, Words: 7519
                Funded by: Wellcome Trust Sir Henry Wellcome Fellowship
                Award ID: WT110198/Z/15/Z
                Funded by: FWO Pegasus 2 Marie‐Curie Fellowship
                Award ID: 12N5517N
                Funded by: European Research Council (ERC)
                Award ID: 681942
                Funded by: DFG Forschergruppe
                Award ID: 2253
                Funded by: Netherlands Organisation for Scientific Research NWO
                Award ID: 360‐70-270
                Funded by: Chunked ANR‐project
                Award ID: ANR‐17-CE28‐0013-02
                Original Article
                Custom metadata
                July 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.0 mode:remove_FC converted:11.09.2020


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