Implicit learning of non-adjacent dependencies

Onnis, Luca; Destrebecqz, Arnaud; Christiansen, Morten H.; Chater, Nick; Cleeremans, Axel

doi:10.1075/sibil.48.10onn

Cited by 7 publications

(7 citation statements)

References 42 publications

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“…In their study, one group of participants (i.e., in the Learnable Condition) was give pre-exposure to non-adjacent dependencies presented in context where such learning is relatively easy (i.e., a high variation context). In this context, non-target elements (X) were highly variable, highlighting the relatively stable target structures (i.e., A_B dependencies); this thus constituted a supportive learning context (Goméz, 2002;Onnis et al, 2003;Onnis et al, 2004). While in two comparison groups, participants were briefly prior exposure to unreliable non-adjacencies in the high variation context (i.e., Non-Learnable Condition) or to the same words as the Learnable Condition but presented individually at random (i.e., Unstructured Condition).…”

Section: Facilitating Implicit Learning Of Multiple Non-adjacent Depe...mentioning

confidence: 99%

Facilitating Implicit Learning of Multiple Non-adjacent Dependencies Regardless of Language Experience: The Role of a Brief Pre-exposure

Ling,

Zhang,

Sun

et al. 2024

Preprint

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In natural language, multiple non-adjacent dependencies arise as syntactic complexity increases and play an essential role in generating and understanding complex syntactic structures (e.g., in cross-serial dependencies). In the lab, showing substantial learning of such dependencies is challenging. In this study, we explored whether a brief prior exposure of an easy-to-learn long distance dependency structure could facilitate the acquisition of difficult-to-learn multiple non-adjacent dependencies (Experiment 1) and whether the effect is constrained by prior long-term knowledge of such types of dependency (Experiment 2). In Experiment 1, we found that the subsequent implicit learning of the new multiple non-adjacent dependencies could benefit from brief prior exposure to learnable non-adjacent dependencies compared to Non-Learnable and Unstructured conditions (which have unreliable or neutral prior materials). This facilitative effect was neither cumulative learning over time nor a simple superimposition of the pre-exposure and exposure phases. In Experiment 2, we further found that the brief prior exposure could override biases from long-term experience to improve implicit learning of multiple non-adjacent rules which conflicted with native language experience. The results reported in both studies have been certified as computationally reproducible by an independent statistician. These findings demonstrated that implicitly acquiring multiple non-adjacent dependencies can be facilitated by brief prior exposure to a learnable structure. This powerful facilitative effect can override the constraints provided by long-term experience.

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Section: Facilitating Implicit Learning Of Multiple Non-adjacent Depe...mentioning

confidence: 99%

Facilitating Implicit Learning of Multiple Non-adjacent Dependencies Regardless of Language Experience: The Role of a Brief Pre-exposure

Ling,

Zhang,

Sun

et al. 2024

Preprint

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“…On task which rely mostly on statistical learning, such as the Artificial Grammar Learning paradigm, participants generally show relatively low (even if above-chance) accuracy. Furthermore, while statistical learning models generally deal well with complex regularities, such as non-adjacent regularities (Onnis et al, 2015) or complex print-to-speech correspondences (Seidenberg & McClelland, 1989), they cannot, by definition, provide the correct pronunciation for unpredictable items.…”

Section: Statistical Learning As the Process That Enables Readers To ...mentioning

confidence: 99%

Rules and Statistics: What If It's Both? A Basic Computational Model of Statistical Learning in Reading Acquisition

Schmalz

2023

Preprint

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“…repetition patterns) (Brooks & Vokey, 1991;Jamieson & Mewhort, 2009); or recursive rules (Dienes & Longuet-Higgins, 2004;Jiang et al, 2012). One way of uniting the different accounts of what is learned is through a computational model such as the Simple Recurrent Network (SRN; Elman, 1990) that can learn a range of such structures (as shown by e.g., Christiansen, Dale, & Reali, 2010;Onnis, Destrebecqz, Christiansen, Chater, & Cleeremans, 2015;Rodriguez, Wiles, & Elman, 1999) 1 . Leaning local associations or chunking involves minimal integration within or across stimuli; the way implicit learning can transcend chunking thus particularly sheds most light on the mechanisms by which it operates in storing and processing knowledge.…”

Section: Introductionmentioning

confidence: 99%

Simulations of implicit learning of symmetries: The importance of prior knowledge and the nature of the memory buffer

R¹,

F²,

Jiang³

et al. 2020

Preprint

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The current research aimed to investigate the role that prior knowledge played in what structures could be implicitly learnt and also the nature of the memory buffer required for learning such structures. It is already established that people can implicitly learn to detect an inversion symmetry (i.e. a cross-serial dependency) based on linguistic tone types. The present study investigated the ability of the Simple Recurrent Network (SRN) to explain implicit learning of such recursive structures. We found that the SRN learnt the symmetry over tone types more effectively when given prior knowledge of the tone types (i.e. of the two categories tones were grouped into). The role of prior knowledge of the tone types in learning the inversion symmetry was tested on people: When an arbitrary classification of tones was used (i.e. in the absence of prior knowledge of categories), participants did not implicitly learn the inversion symmetry (unlike when they did have prior knowledge of the tone types). These results indicate the importance of prior knowledge in implicit learning of symmetrical structures. We further contrasted the learning of inversion symmetry and retrograde symmetry and showed that inversion was learnt more easily than retrograde by the SRN, matching our previous findings with people, thus showing that the type of memory buffer used in the SRN is suitable for modeling the implicit learning of symmetry in people.

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Implicit learning of non-adjacent dependencies

Cited by 7 publications

References 42 publications

Facilitating Implicit Learning of Multiple Non-adjacent Dependencies Regardless of Language Experience: The Role of a Brief Pre-exposure

Facilitating Implicit Learning of Multiple Non-adjacent Dependencies Regardless of Language Experience: The Role of a Brief Pre-exposure

Rules and Statistics: What If It's Both? A Basic Computational Model of Statistical Learning in Reading Acquisition

Simulations of implicit learning of symmetries: The importance of prior knowledge and the nature of the memory buffer

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