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The human brain can detect statistical regularities in the environment across a wide variety of contexts. The importance of this process is well‐established not just in language acquisition but across different modalities; in addition, several neural correlates of statistical learning have been identified. A current technique for tracking the emergence of regularity learning and localizing its neural background is frequency tagging (FT). FT can detect neural entrainment not only to the frequency of stimulus presentation but also to that of a hidden structure. Auditory learning paradigms with linguistic and nonlinguistic stimuli, along with a visual paradigm using nonlinguistic stimuli, have already been tested with FT. To complete the picture, we conducted an FT experiment using written syllables as stimuli and a hidden triplet structure. Both behavioral and neural entrainment data showed evidence of structure learning. In addition, we localized two electrode clusters related to the process, which spread across the frontal and parieto‐occipital areas, similar to previous findings. Accordingly, we conclude that fast‐paced visual linguistic regularities can be acquired and are traceable through neural entrainment. In comparison with the literature, our findings support the view that statistical learning involves a domain‐general network. Frequency tagging has not yet been applied to written artificial grammar learning. We found that implicit behavioral correlates showed statistical learning with visually presented syllable triplets. Neural entrainment results proved the acquisition of written regularities and frontal areas were found to participate in visual linguistic statistical learning. Our results pose further evidence that statistical learning has a domain‐general center. [ABSTRACT FROM AUTHOR]

Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA () as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is optimized for desktop and mobile devices with a user-friendly interface, which can present visual and aural stimuli and elicit judgment responses with RT measures. Without limits in time and space, ENIGMA could help collect more data from participants with diverse personal and language backgrounds and variable cognitive skills. Such data are essential to explain complex factors influencing learners’ performance in AGL experiments and answer various research questions regarding L1/L2 acquisition. The introduction of the core features in ENIGMA is followed by an example study that partially replicated Chen (Lang Acquis 27(3):331–361, 2020) to illustrate possible experimental designs and examine the quality of the collected data. [ABSTRACT FROM AUTHOR]

A notable cross-linguistic gap exists in the typology of consonant harmony: Stem-controlled and suffix-controlled systems are known, but prefix-controlled consonant harmony remains unattested. To explore this gap, an artificial grammar learning (AGL) study was conducted, in which participants were passively trained and then tested on one of four possible sibilant harmony patterns, differing by direction and morphological locus of control. The effect of target-trigger distance was also tested by varying the stem length from one to four syllables. Statistical analysis found significant main effects of Direction and Target-Trigger Distance, with the progressive group outperforming the regressive one and shorter target-trigger distances yielding better performance than longer ones. Affix-controlled groups also learned the pattern more slowly than stem-controlled groups, although overall performance did not differ between affix-controlled and stem-controlled groups. Overall, the results indicate that the typological lack of prefix-controlled consonant harmony is not due to a lack of learnability, as there was no statistically significant difference in ultimate achievement between the four groups.

Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model's predictions corresponded to participants' decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn. [ABSTRACT FROM AUTHOR]

Past research has found that depression is associated with a multitude of deficits in processing social stimuli. These deficits might impair the implicit (unconscious) learning of socio-emotional regularities, an essential process for adaptive social functioning. In contrast, previous research on adaptive functions of depression suggests that dysphoric states can be associated, in some circumstances, with increased accuracy in detecting regularities. However, a direct assessment of implicit learning of socio-emotional regularities in depression has not yet been conducted. In the present studies, we adapted the Artificial Grammar Learning task to induce implicit and explicit learning of regularities that govern social emotional stimuli (facial emotional expressions in Experiment 1) and social stimuli without explicit emotional content (body movements in Experiment 2). We assessed participants' learning and awareness of these regularities, as well as their levels of depression. In both experiments, Bayesian analyses showed that the depressive symptomatology was neither associated with a learning deficit, nor with a learning advantage. This was the case for participants' overall performance, as well as for their implicit and their explicit learning performance. Our results contradict most previous findings and show that, even though depressive symptoms are associated with a variety of socio-cognitive deficits, they do not hinder the ability to implicitly or explicitly learn regularities within social contexts. Our results also show some constraints on the types of abilities that can be enhanced by depressive states. [ABSTRACT FROM AUTHOR]

This study investigates whether statistical learning ability, conceptualized as a cognitive ability to learn regularities implicitly, is a good predictor for L2 learners' online language processing performance. Native-English-speaking adults, as a control group, and native-Korean-speaking adult L2 learners of English participated. They completed: (a) an artificial grammar learning task containing nonadjacent dependencies in sequences of non-words, to test statistical learning ability; and (b) a self-paced English reading task containing relative clauses (RC) in which the "filler" and the "gap" formed a long-distance dependency, to test language processing. Both tasks' stimuli were presented element-by-element to mimic the incremental nature of online language processing. The results for the L1 group show that higher accuracy scores on the artificial grammar learning task did not predict higher sentence comprehension scores. The results for the L2 group, however, show a marginally significant correlation between accuracy scores on the artificial grammar learning task and sentence comprehension scores. For both groups, the reading time difference between grammatical and ungrammatical items in the artificial grammar learning task did predict the speed of reading times for items with RCs with a long-distance dependency in the sentence processing task: Larger differences in RTs in the artificial grammar task correlated with slower reading at the critical region of English RCs. These findings suggest a similar mechanism for online first and second language processing of core syntactic phenomena and for statistical learning ability that involves implicitly tracking distributional relations across elements. [ABSTRACT FROM AUTHOR]

The demonstration that unconscious learning supports instrumental behaviors (i.e., choosing the stimuli that lead to rewards) is central for the tenet that unconscious cognition sustains human adaptation. Recent studies, using reliable subliminal conditioning paradigms and improved awareness measurements have found evidence against unconscious knowledge sustaining accurate instrumental responses. The present preregistered study proposes a paradigm, in which unconscious processing is stimulated not by subliminally exposing the predictive (conditioned) stimuli, but by employing predictive regularities that are complex and difficult to detect consciously. Participants (N = 211) were exposed to letter strings that, unknown to them, were built from two complex artificial grammars: a "rewarded" or a "non-rewarded" grammar. On each trial, participants memorized a string, and subsequently had to discriminate the memorized string from a distractor. Correct discriminations were rewarded only when the identified string followed the rewarded grammar, but not when it followed the non-rewarded grammar. In a subsequent test phase, participants were presented with new strings from the rewarded and from the unrewarded grammar. Their task was now to directly choose the strings from the rewarded grammar, in order to collect more rewards. A trial-by-trial awareness measure revealed that participants accurately choose novel strings from the rewarded grammar when they had no conscious knowledge of the grammar. The awareness measure also showed that participants were accurate only when the unconsciously learned grammar led to conscious judgments. The present study shows that unconscious knowledge can guide instrumental responses, but only to the extent it supports conscious judgments. [ABSTRACT FROM AUTHOR]

Observers rapidly extract summary statistics from sets ofvisually presented items, like the mean size of a set of circles,or the mean expression of a set of faces. Their excellentability to report summary statistics stands in contrast to near-chance representation of any of the individuals. Here weasked to what extent this ‘ensemble perception’ signatureextends to a more abstract property: relations amongelements. Participants watched ten unique animations ofvisually patterned objects (hereafter, ‘shapes’) colliding witheach other and producing a new shape. Collisions conformedto ABA patterns, such that the result shape always matchedone of the collider shapes. Recognition tests showed thatparticipants accurately recalled the collisions they saw, butalso falsely accepted foils which conformed to the ABApattern but which were not in fact specifically seen (wererearrangements of the original shapes across collisions). Onthe other hand, they were much less likely to accept foilswhich did not conform to the pattern, but were equallydistinct rearrangements (e.g., AAB). This suggests thatparticipants represented the overall, common pattern betterthan the specifics of what they saw; the superior encoding ofthe summary relative to the individuals thus applies tosummaries of relations. However, in contrast to prior findingswith visual features, we did not find that recall of individualpatterns was entirely at chance. Our paradigm offers a way topursue future questions such as the pressures and motivationswhich might govern the trade-off between summarizingevidence vs. retaining individual experiences.

Language learners have to both segment words and discover grammatical rules connecting those words in sentences. In adult listeners, the presence of a prosodic cue in the speech stream, for example, a pause, appears to facilitate rule-learning of non-adjacent dependencies of the form AiXCi (Peña et al., 2002). Only when listening to the artificial language containing pauses, could participants identify rule-words of the form AiAjCi or AiCjCi, where intervening syllables were moved from A- or C-positions. Frost and Monaghan (2016) found in a similar study that participants who were tested with novel, rather than moved, intervening syllables in AiXCi items showed rule-learning even when the familiarisation stream contained no pauses. The present study re-examines the facilitative effect of pauses in discovering structural rules in speech in a novel population: children aged 7-11. We used the same artificial speech stimuli as Peña et al. (2002) and tested children in both a moved-syllable and novel-syllable forced-choice task. The results of 140 children show that pauses provide a facilitative effect on rule-learning – also for young learners. Regardless of syllable types, only children who listened to the familiarisation stream containing pauses chose words following the rule above chance-level.

The artificial grammar learning paradigm is a classic method of investigating the influence of universal constraints on shaping learning biases on language acquisition. While this method has been used extensively by linguists to test theoretical claims in generative grammar, one of the most prevalent frameworks in language acquisition, several studies have questioned whether artificial grammar learning reflects language acquisition enough to allow us to use it to draw inferences about the validity of universal constraints, particularly those arising from phonetic naturalness. The current study tests whether artificial grammar learning shows the effect of one robust phonetic naturalness constraint: the restriction on nasal harmony patterns arising from the sonority hierarchy. Nasal harmony is of particular interest because it is one of the few types of harmony that occurs between consonants and vowels, which is an under-researched topic. The results, contrary to the skeptical concerns, showed that participants (n = 120) could learn an artificial grammar involving a natural pattern, but could not learn one corresponding to an arbitrary/phonetically unmotivated pattern in the same way or to the same degree. This study contributes epistemic support to the large body of work using artificial grammar experiments to test phonological operations. [ABSTRACT FROM AUTHOR]

We conducted a close replication of the seminal work by Marcus and colleagues from 1999, which showed that after a brief auditory exposure phase, 7‐month‐old infants were able to learn and generalize a rule to novel syllables not previously present in the exposure phase. This work became the foundation for the theoretical framework by which we assume that infants are able to learn abstract representations and generalize linguistic rules. While some extensions on the original work have shown evidence of rule learning, the outcomes are mixed, and an exact replication of Marcus et al.'s study has thus far not been reported. A recent meta‐analysis by Rabagliati and colleagues brings to light that the rule‐learning effect depends on stimulus type (e.g., meaningfulness, speech vs. nonspeech) and is not as robust as often assumed. In light of the theoretical importance of the issue at stake, it is appropriate and necessary to assess the replicability and robustness of Marcus et al.'s findings. Here we have undertaken a replication across four labs with a large sample of 7‐month‐old infants (N = 96), using the same exposure patterns (ABA and ABB), methodology (Headturn Preference Paradigm), and original stimuli. As in the original study, we tested the hypothesis that infants are able to learn abstract "algebraic" rules and apply them to novel input. Our results did not replicate the original findings: infants showed no difference in looking time between test patterns consistent or inconsistent with the familiarization pattern they were exposed to. [ABSTRACT FROM AUTHOR]

In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher‐order regularities of a highly simplified input where only sequential‐order information marks the hierarchical structure. To this end, we implemented a sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self‐similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling for the use of low‐level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants' pattern of anticipation could not be accounted for by "flat" statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions. [ABSTRACT FROM AUTHOR]

This paper aims to answer the question whether there is any difference in learning allomorphs of a morpho-phonological pattern which differ in the likelihood of phonological processes. Broadly, there are two views on how the allomorphs are represented in the mental lexicon. The PHONOLOGY (P) view prioritizes the phonological analysis of the allomorphs and posits a single underlying representation whereas the MORPHEME LISTING (ML) view memorizes all the allomorphs and lists multiple representations. To investigate the question, artificial grammar learning experiments were conducted which compared how well learners could learn allomorphy patterns that were more or less amenable to a phonological analysis under the P view. The P view predicts that there will be a difference in learning the allomorphs, whereas the ML view predicts that there will be no difference in learning because listing the morphemes are the same in each grammar. The results of the series of experiments conducted consistently reject the view that speakers just memorize which allomorphs are used in which phonological environments. A more careful examination of the results points to a more complex interplay between phonological and morphological learning. [ABSTRACT FROM AUTHOR]

Implicit learning plays an important role in the acquisition of various skills that are often deficient in individuals with autism spectrum disorder (ASD). The present study examines the implicit learning ability of children and adolescents with ASD, by comparing it to that of a typical group of peers, using the Artificial Grammar Learning (AGL) task. In addition, this study investigates whether the above ability is associated with individual characteristics, namely intelligence quotient (IQ), ASD symptoms severity, and individual perception style (global/holistic or local/focused). The sample consisted of 20 individuals with mild to relatively severe ASD symptoms and 20 age- and gender-matched typically developing (TD) individuals. The unconscious (implicit) nature of learning was assessed via a subjective measure, the "no-loss gambling" method, which allows an unbiased evaluation of the confidence accompanying each judgement. The results provided evidence of implicit learning, which was preserved in the ASD group, although reduced relative to the typical group. Multiple linear regressions with interaction terms between group and participants' scores on the Wechsler Abbreviated Scale of Intelligence (WASI), the Childhood Autism Rating Scale (CARS), and performance on a Navon-type task examined whether the possible relationships between each of the above scores and AGL and implicit learning differ in the two groups. Implicit learning was not significantly associated with IQ, ASD symptoms severity, or individual perception style (except for perception style in terms of reaction times [RTs] for the TD group). These results confirm and extend earlier findings supporting the resilience of implicit learning to individual differences., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The role of phonetic naturalness in biasing the learning of sound patterns remains an unsettled question in phonology. The present study investigates naturalness bias in phonotactic learning using a novel experimental paradigm that tests whether learners reproduce a phonetically-motivated phonotactic implicational about the distribution of major place contrasts in stops. Stops differing in place of articulation are easier to distinguish word-initially than word-finally, so place contrasts in word-final position should also exist in word-initial position. The reverse is not necessarily true. This implicational is typologically supported as well as motivated by perceptual naturalness. In two artificial grammar learning experiments, I exposed participants to place contrasts in stops either word-initially or word-finally and tested whether they extended the contrasts to the other word-edge position. Participants successfully learned to recognize novel words that fit the phonotactic pattern they had been trained on, but they were equally willing to extend the place contrasts in both directions, yielding no evidence for naturalness bias. These results contrast with those of a similar study that found asymmetric extension of the stop voicing contrast, supporting an effect of naturalness bias. Confusion data suggests that the reduction in perceptibility from word-initial to word-final position may be greater for stop voicing than for stop place of articulation. This difference may underlie the divergent results of the two studies, leading to the hypothesis that the strength of a substantive bias depends on the magnitude of its phonetic precursor.

How do grammars assess the well-formedness of words with multiple phonotactic violations? Certain models predict that as the strength of phonotactic restrictions decrease, forms that violate multiple restrictions should be less acceptable than expected, in a pattern we term super-linear cumulativity. We test this prediction using a series of Artificial Grammar Learning experiments, in which we vary the number of exceptions to phonotactic patterns in artificial languages. We find that super-linear cumulativity is indeed observed in the conditions with the weakest restrictions. Strikingly, participants exhibit super-linear cumulativity even when the trained language does not contain evidence for it.

In previous research of tonal phonology, contour tones were assumed to be phonologically presented not only as a sequence of level tones but also as a single unit. Obligatory Contour Principle (OCP) constraints, by extension, were thought to prohibit identical level tones associated to adjacent terminal nodes (OCP-Terminal) and identical adjacent tonal units (OCP-Unit). This proposal has nevertheless been challenged theoretically and empirically. This study seeks to offer new experimental evidence to help solve the debate by comparing the learnability of the two OCP generalizations in an artificial grammar learning paradigm. In Exp I, we exposed disyllabic tonal patterns conforming either to OCP-Terminal or to OCP-Unit to two target groups of learners, who were then tested if they could extend the hidden generalization to their auditory acceptability judgment of novel disyllabic items. Exp II had the same exposure phase but required learners to produce novel disyllabic items with tonal combinations of their choice. In both experiments, the two target groups demonstrated signs of learning target OCP generalizations from different channels. Crucially, learners implicitly acquired the OCP-Terminal generalization but only learned the OCP-Unit pattern as explicit knowledge. These findings led us to conclude that OCP-Unit may not be involved in implicit and automatized phonological computation.

Structured sequence processing (SSP) refers to theneurocognitive mechanisms used to learn sequential patternsin the environment. SSP ability seems to be important forlanguage (Conway, Bauernschmidt, Huang, & Pisoni, 2010);however, there are few neural studies showing an empiricalconnection between SSP and language. The purpose of thisstudy was to investigate the association between SSP andlanguage processing by comparing the underlying neuralcomponents elicited during each type of task. Healthy adultsubjects completed a visual, non-linguistic SSP taskincorporating an artificial grammar and a visual morpho-syntactic language task. Both tasks were designed to causeviolations in expectations of items occurring in a series.Event-related potentials (ERPs) were used to examine theunderlying neural mechanisms associated with theseexpectancy violations. The results indicated the P3acomponent elicited by the SSP task and the P600 componentelicited by the language task shared similarities in theirtopographic distribution. These preliminary analyses suggestthat the P3a and P600 may reflect processes involvingdetection of sequential violations in non-language andlanguage domains, which is consistent with the idea thatlanguage processing relies on general-purpose SSPmechanisms.

Previous studies have shown that distributed practice—a training strategy that is known to facilitate memory—is likely to result in greater learning than massed practice. This effect has been demonstrated largely in explicit tasks. The purpose of this study was to test whether statistical learning of artificial grammar is affected by the lag between learning sessions overall, and by high and low complexity stimuli (as measure by chunk strength). Two groups (spaced-short and spaced-long) learned strings of letters created according to a set of rules and were required to produce new strings using given letter sets. For the spaced-short group, the two learning sessions, each including training and a test phase, took place sequentially with a 10-min break, whereas for the spaced-long group, learning sessions were distributed across two days (1-day lag). Overall results showed improved performance following spaced-long practice compared to spaced-short practice. The results also indicated that in the low chunk strength strings (indicating high complexity), both groups demonstrated similar improvement from first to second testing, while in the high chunk strength strings (indicating low complexity), improvement in letter reordering performance was significantly higher when the learning sessions were distributed across two days. This pattern of findings suggests that stimuli complexity affects the extent to which distributed practice enhance artificial grammar learning. [ABSTRACT FROM AUTHOR]

Contending with talker variability has been found to lead to processing costs but also benefits by focusing learners on invariant properties of the signal, indicating that talker variability acts as a desirable difficulty. That is, talker variability may lead to initial costs followed by long‐term benefits for retention and generalization. Adult participants learned an artificial grammar affording learning of multiple components in two experiments varying in difficulty. They learned from one, two, or eight talkers and were tested at three time points. The eight‐talker condition did not impact learning. The two‐talker condition negatively impacted some aspects of learning, but only under more difficult conditions. Generalization of the grammatical dependency was difficult. Thus, we discovered that high and limited talker variability can differentially impact artificial grammar learning. However, talker variability did not act as a desirable difficulty in the current paradigm as the few evidenced costs were not related to long‐term benefits. [ABSTRACT FROM AUTHOR]

Literacy affects many aspects of language and cognition, including the shift from a more holistic mode of processing to a more analytical part-based mode of processing. Here we examined whether this shift impacts the ability of preschool and primary school children to learn the rules underlying a finite-state grammar using an artificial grammar learning (AGL) paradigm implemented with either linguistic (letters) or non-linguistic (colors) materials to further examine if children's AGL performance was modulated by type of stimuli. Both tasks involved a training phase in which half of the preschool children and half of the primary school children were exposed to a set of either letter or color strings without any information about the rules underlying the construction of those strings. Later, in the test phase, they were asked to decide whether a new set of letter or color strings conformed to those rules to test grammar learning. Results showed that only primary school children showed evidence of learning, and, importantly, only with colors. These findings seem to support the view that learning to read promotes reliance on smaller linguistic units that might hinder the ability of first-graders to learn the rules underlying finite-state grammars implemented with linguistic materials. [ABSTRACT FROM AUTHOR]

The artificial grammar learning paradigm is a classic method of investigating the influence of universal constraints on shaping learning biases on language acquisition. While this method has been used extensively by linguists to test theoretical claims in generative grammar, one of the most prevalent frameworks in language acquisition, several studies have questioned whether artificial grammar learning reflects language acquisition enough to allow us to use it to draw inferences about the validity of universal constraints, particularly those arising from phonetic naturalness. The current study tests whether artificial grammar learning shows the effect of one robust phonetic naturalness constraint: the restriction on nasal harmony patterns arising from the sonority hierarchy. Nasal harmony is of particular interest because it is one of the few types of harmony that occurs between consonants and vowels, which is an under-researched topic. The results, contrary to the skeptical concerns, showed that participants (n = 120) could learn an artificial grammar involving a natural pattern, but could not learn one corresponding to an arbitrary/phonetically unmotivated pattern in the same way or to the same degree. This study contributes epistemic support to the large body of work using artificial grammar experiments to test phonological operations.

This study investigates the hypothesis that tone alternation directionality becomes a basis of structural bias for tone alternation learning, where “structural bias” refers to a tendency to prefer uni-directional tone deletions to bi-directional ones. Two experiments were conducted. In the first, Mandarin speakers learned three artificial languages, with bi-directional tone deletions, uni-directional, left-dominant deletions, and uni-directional, right-dominant deletions, respectively. The results showed a learning bias toward uni-directional, right-dominant patterns. As Mandarin tone sandhi is right-dominant while Cantonese tone change is lexically restricted and does not have directionality asymmetry, a follow-up experiment trained Cantonese speakers either on left- or right-dominant deletions to see whether the right-dominant preference was due to L1 transfer from Mandarin. The results of the experiment also showed a learning bias toward right-dominant patterns. We argue that structural simplicity affects tone deletion learning but the simplicity should be grounded on phonetics factors, such as syllables’ contour-tone bearing ability. The experimental results are consistent with the findings of a survey on other types of tone alternation’s directionality, i.e., tone sandhi across 17 Chinese varieties. This suggests that the directionality asymmetry found across different tone alternations reflects a phonetically grounded structural learning bias.

This study investigates the hypothesis that tone alternation directionality becomes a basis of structural bias for tone alternation learning, where "structural bias" refers to a tendency to prefer uni-directional tone deletions to bi-directional ones. Two experiments were conducted. In the first, Mandarin speakers learned three artificial languages, with bi-directional tone deletions, uni-directional, left-dominant deletions, and uni-directional, right-dominant deletions, respectively. The results showed a learning bias toward uni-directional, right-dominant patterns. As Mandarin tone sandhi is right-dominant while Cantonese tone change is lexically restricted and does not have directionality asymmetry, a follow-up experiment trained Cantonese speakers either on left- or right-dominant deletions to see whether the right-dominant preference was due to L1 transfer from Mandarin. The results of the experiment also showed a learning bias toward right-dominant patterns. We argue that structural simplicity affects tone deletion learning but the simplicity should be grounded on phonetics factors, such as syllables' contour-tone bearing ability. The experimental results are consistent with the findings of a survey on other types of tone alternation's directionality, i.e., tone sandhi across 17 Chinese varieties. This suggests that the directionality asymmetry found across different tone alternations reflects a phonetically grounded structural learning bias. [ABSTRACT FROM AUTHOR]

Artificial grammar learning (AGL) paradigms are used extensively to characterise (neuro)cognitive bases of language learning. However, despite their effectiveness in characterising the capacity to learn complex structured sequences, AGL paradigms lack ecological validity and typically do not account for cross-linguistic differences in sentence comprehension. Here, we describe a new modified miniature language paradigm – Mini Pinyin – that mimics natural language as it is based on an existing language (Mandarin Chinese) and includes both structure and meaning. Mini Pinyin contains a number of cross-linguistic elements, including varying word orders and classifier-noun rules. To evaluate the effectiveness of Mini Pinyin, 76 (mean age = 24.9; 26 female) monolingual native English speakers completed a learning phase followed by a sentence acceptability judgement task. Generalised mixed effects modelling revealed that participants attained a moderate degree of accuracy on the judgement task, with performance scores ranging from 25% to 100% accuracy depending on the word order of the sentence. Further, sentences compatible with the canonical English word order were learned more efficiently than non-canonical word orders. We controlled for inter-individual differences in statistical learning ability, which accounted for ~20% of the variance in performance on the sentence judgement task. We provide stimuli and statistical analysis scripts as open-source resources and discuss how future research can utilise this paradigm to study the neurobiological basis of language learning. Mini Pinyin affords a convenient tool for improving the future of language learning research by building on the parameters of traditional AGL or existing miniature language paradigms. [ABSTRACT FROM AUTHOR]

The starting-small effect is a cognitive advantage in language acquisition when learners begin by generalizing on regularities from structurally simple and shorter tokens in a skewed input distribution. Our study explored this effect as a potential explanation for the biased learning of opaque and transparent vowel harmony. In opaque vowel harmony, feature agreement occurs strictly between adjacent vowels, and an intervening “neutral vowel” blocks long-distance vowel harmony. Thus, opaque vowel harmony could be acquired even if learners start with structurally simpler and more frequent disyllabic tokens. Alternatively, transparent vowel harmony can only be observed in longer tokens demonstrating long-distance agreement by skipping a neutral vowel. Opaque vowel harmony is predicted to be learned more efficiently due to its compatibility with local dependency acquired via starting-small learning. In two artificial grammar learning experiments, learners were exposed to both vowel harmony patterns embedded in an equal number of disyllabic and trisyllabic tokens or a skewed distribution with twice as many disyllabic tokens. In Exp I, learners’ test performance suggests the consistently biased learning of local and opaque vowel harmony with starting-small learning. Furthermore, in Exp II, the acquired vowel harmony patterns varied significantly by working memory capacity with a balanced but not skewed input distribution, presumably because of the ease of cognitive demand with starting-small learning. [ABSTRACT FROM AUTHOR]

The current study was designed to explore whether statistical learning ability is affected by the diversity of the stimulus set used in the training phase. The effect of stimulus diversity was assessed by controlling and manipulating the number of exposures to a given set and the number of unique strings presented to the learner during the training phase. 147 students participated in two studies. In the unvaried stimulus study, 71 participants learned the same basic set of 15 exemplars, once(15 × 1 exposure), twice (15 × 2 exposures = 30 total strings) and 3 times (15 × 3 exposures = 45 total strings). In the varied stimulus study, 75 participants learned 15, 30 and 45, all of which were unique, unrepeated exemplars. All groups were asked to classify test strings for their grammaticality following training. Results of the d’ measures in the unvaried stimulus study indicate similar performance across the groups. Conversely, the results of the varied stimulus study show that the group presented with 45 unique strings performed significantly better than the baseline group (15 strings). Analysis of the differences across the equivalent groups in the two studies (15 × 2 exposures vs. 30 unique strings and 15 × 3 exposures vs. 45 unique strings) indicates differences in performance only between the group who was presented with the same 15 strings three times and the group presented with 45 unrepeated strings. Taken together, our results shed additional light on the central role of stimulus variation in Artificial Grammar Learning.

Human languages all have a grammar, that is, rules that determine how symbols in a language can be combined to create complex meaningful expressions. Despite decades of research, the evolutionary, developmental, cognitive, and computational bases of grammatical abilities are still not fully understood. "Artificial Grammar Learning" (AGL) studies provide important insights into how rules and structured sequences are learned, the relevance of these processes to language in humans, and whether the cognitive systems involved are shared with other animals. AGL tasks can be used to study how human adults, infants, animals, or machines learn artificial grammars of various sorts, consisting of rules defined typically over syllables, sounds, or visual items. In this introduction, we distill some lessons from the nine other papers in this special issue, which review the advances made from this growing body of literature. We provide a critical synthesis, identify the questions that remain open, and recognize the challenges that lie ahead. A key observation across the disciplines is that the limits of human, animal, and machine capabilities have yet to be found. Thus, this interdisciplinary area of research firmly rooted in the cognitive sciences has unearthed exciting new questions and venues for research, along the way fostering impactful collaborations between traditionally disconnected disciplines that are breaking scientific ground. [ABSTRACT FROM AUTHOR]

There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space. [ABSTRACT FROM AUTHOR]

Artificial grammar learning (AGL) has become an important tool used to understand aspects of human language learning and whether the abilities underlying learning may be unique to humans or found in other species. Successful learning is typically assumed when human or animal participants are able to distinguish stimuli generated by the grammar from those that are not at a level better than chance. However, the question remains as to what subjects actually learn in these experiments. Previous studies of AGL have frequently introduced multiple potential contributors to performance in the training and testing stimuli, but meta‐analysis techniques now enable us to consider these multiple information sources for their contribution to learning—enabling intended and unintended structures to be assessed simultaneously. We present a blueprint for meta‐analysis approaches to appraise the effect of learning in human and other animal studies for a series of artificial grammar learning experiments, focusing on studies that examine auditory and visual modalities. We identify a series of variables that differ across these studies, focusing on both structural and surface properties of the grammar, and characteristics of training and test regimes, and provide a first step in assessing the relative contribution of these design features of artificial grammars as well as species‐specific effects for learning. [ABSTRACT FROM AUTHOR]

Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in species ranging from mammals (primates and rats) to birds (pigeons, songbirds, and parrots) considering also cross‐species comparisons. The findings are contrasted with seminal studies in human infants that motivated the work in nonhuman animals. This synopsis identifies advances in knowledge and where uncertainty remains regarding the various strategies that nonhuman animals can adopt for processing sequencing dependencies. The paucity of evidence in the few species studied to date and the need for follow‐up experiments indicate that we do not yet understand the limits of animal sequence processing capacities and thereby the evolutionary pattern. This vibrant, yet still budding, field of research carries substantial promise for advancing knowledge on animal abilities, cognitive substrates, and language evolution. [ABSTRACT FROM AUTHOR]

This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling long‐distance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns. [ABSTRACT FROM AUTHOR]

The audiovisual speech signal contains multimodal information to phrase boundaries. In three artificial language learning studies with 12 groups of adult participants we investigated whether English monolinguals and bilingual speakers of English and a language with opposite basic word order (i.e., in which objects precede verbs) can use word frequency, phrasal prosody and co-speech (facial) visual information, namely head nods, to parse unknown languages into phrase-like units. We showed that monolinguals and bilinguals used the auditory and visual sources of information to chunk "phrases" from the input. These results suggest that speech segmentation is a bimodal process, though the influence of co-speech facial gestures is rather limited and linked to the presence of auditory prosody. Importantly, a pragmatic factor, namely the language of the context, seems to determine the bilinguals' segmentation, overriding the auditory and visual cues and revealing a factor that begs further exploration. [ABSTRACT FROM AUTHOR]

Over the past decades, research employing artificial grammar, sequence learning, and statistical learning paradigms has flourished, not least because these methods appear to offer a window, albeit with a restricted view, on implicit learning processes underlying natural language learning. But these paradigms usually provide relatively little exposure, use meaningless stimuli, and do not even necessarily target natural language structures. So the question arises whether they engage the same brain regions as natural language. The aim of this review is to use data from brain imaging, brain stimulation, and the effects of brain damage to identify the main brain regions that show sensitivity to structural regularities in implicit learning paradigms and to consider their relationship to natural language processing and learning. [ABSTRACT FROM AUTHOR]

• Statistical learning consists of two primary neurocognitive mechanisms. • Cortical plasticity results in heightened behavioral facilitation for patterns. • Top-down control of attention can modulate learning. • They have different neuroanatomical, developmental, and phylogenetic bases. Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors. [ABSTRACT FROM AUTHOR]

Implicit statistical learning (ISL) describes our ability to tacitly pick up regularities from our environment therefore, shaping our behavior. A broad understanding of ISL incorporates a great range of possible computations, which render it highly relevant to reading. In the light of this hypothesized relationship, ISL performance was explored in young (M = 8.47 years) typical readers (N = 31) across three different modalities (i.e., visual, auditory, and tactile) using the Artificial Grammar Learning (AGL) paradigm. Adopting repeated measures and correlational designs, the obtained data revealed modality constraints: (1) above-chance performance was observed on the visual and tactile tasks but not on the auditory task, (2) there was no significant correlation of ISL performance across modalities, and (3) split-half reliability of visual and auditory tasks was reasonably high, yet for the tactile task it was close to zero. Evaluating the relation between ISL ability and language skills, we observed a positive correlation between visual ISL performance and phonological awareness. We discuss these findings in view of current perspectives on the nature of ISL and its potential involvement in mastering successful (i.e., accurate and fluent) reading.

This paper reports on a series of artificial grammar learning experiments focused on locality relations in patterns of long-distance consonant agreement (harmony) and disagreement (dissimilation). Participants in experimental conditions were exposed to dependencies affecting stem-suffix pairs of liquids at either a short-range (transvocalic, CVCVLV-LV) or medium-range (beyond-transvocalic, CVLVCV-LV) distance. Two experiments used a poverty of stimulus paradigm, offering no information about the other distance level; participants interpreted short-range interaction as a strictly transvocalic dependency but medium-range interaction as unbounded, generalizing to other distances. Two experiments employed a ‘rich stimulus’ paradigm, where training data unambiguously indicated the absence of any dependency at the other distance; this enabled probing of specific locality patterns, in particular strictly beyond-transvocalic dependencies. The constraint-based Agreement by Correspondence model of non-adjacent consonant interactions predicts such patterns to be possible for dissimilation but not harmony. The results do not support this hypothesis: Participants seem to have serious difficulty learning strictly beyond-transvocalic dependencies of either kind. Our findings are more consistent with recent proposals that the space of learnable phonotactic restrictions is delimited by the Tier-based Strictly 2-Local class of formal languages. Strictly transvocalic and unbounded dependencies lie within this region, whereas strictly beyond-transvocalic dependencies are more complex, falling outside the learner’s hypothesis space.

This paper presents an experimental study investigating artificial grammar learning in monolingual and bilingual children, with and without dyslexia, using an original methodology. We administered a serial reaction time task, in the form of a modified Simon task, in which the sequence of the stimuli was manipulated according to the rules of a simple Lindenmayer grammar (more specifically, a Fibonacci grammar). By ensuring that the subjects focused on the correct response execution at the motor stage in presence of congruent or incongruent visual stimuli, we could meet the two fundamental criteria for implicit learning: the absence of an intention to learn and the lack of awareness at the level of resulting knowledge. The participants of our studies were four groups of 10-year-old children: 30 Italian monolingual typically developing children, 30 bilingual typically developing children with Italian L2, 24 Italian monolingual dyslexic children, and 24 bilingual dyslexic children with Italian L2. Participants were administered the modified Simon task developed according to the rules of the Fibonacci grammar and tested with respect to the implicit learning of three regularities: (i) a red is followed by a blue, (ii) a sequence of two blues is followed by a red, and (iii) a blue can be followed either by a red or by a blue. Results clearly support the hypothesis that learning took place, since participants of all groups became increasingly sensitive to the structure of the input, implicitly learning the sequence of the trials and thus appropriately predicting the occurrence of the relevant items, as manifested by faster reaction times in predictable trials. Moreover, group differences were found, with bilinguals being overall faster than monolinguals and dyslexics less accurate than controls. Finally, an advantage of bilingualism in dyslexia was found, with bilingual dyslexics performing consistently better than monolingual dyslexics and, in some conditions, at the level of the two control groups. These results are taken to suggest that bilingualism should be supported also among linguistically impaired individuals.

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.

A number of experiments support the hypothetical utility of statistical information for language learning and processing among both children and adults. However, tasks in these studies are often very general, and only a few include populations with developmental language disorder (DLD). We wanted to determine whether a stronger relationship might be shown when the measure of statistical learning is chosen for its relevance to the language task when including a substantial number of participants with DLD. The language ability we measured was sensitivity to verb bias – the likelihood of a verb to appear with a certain argument or interpretation. A previous study showed adults with DLD were less sensitive to verb bias than their typical peers. Verb bias sensitivity had not yet been tested in children with DLD. In Study 1, 49 children, ages 7–9 years, 17 of whom were classified as having DLD, completed a task designed to measure sensitivity to verb bias through implicit and explicit measures. We found children with and without DLD showed sensitivity to verb bias in implicit but not explicit measures, with no differences between groups. In Study 2, we used a multiverse approach to investigate whether individual differences in statistical learning predicted verb bias sensitivity in these participants as well as in a dataset of adult participants. Our analysis revealed no evidence of a relationship between statistical learning and verb bias sensitivity in children, which was not unexpected given we found no group differences in Study 1. Statistical learning predicted sensitivity to verb bias as measured through explicit measures in adults, though results were not robust. These findings suggest that verb bias may still be relatively unstable in school age children, and thus may not play the same role in sentence processing in children as in adults. It would also seem that individuals with DLD may not be using the same mechanisms during processing as their typically developing (TD) peers in adulthood. Thus, statistical information may differ in relevance for language processing in individuals with and without DLD. [ABSTRACT FROM AUTHOR]

We present a study investigating the neural correlates of artificial grammar learning – a process of implicit processing of regularities in the environment. Participants observed visual stimuli that were created using a set of complex rules and then classified items from a new stimulus set as either consistent with these rules or not. Unlike previous event-related potentials (ERP) studies in this area, we used a short-term learning procedure normally used in behavioral experiments. With this short-term learning paradigm, we were able to detect ERP-components related to two different types of implicit knowledge. We found component (P600) related to the violation of the learned abstract grammatical structure. We also found early ERP-components (N200) related to the violation of learned combinations of elements in stimuli (frequency structure). It was possible to observe these distinct results because of the specific design of the study in which frequency structure and abstract grammaticality were independently varied. The results show neural correlates of classical artificial grammar learning and speak in favor of two distinct mechanisms of implicit learning: one responsible for abstract rules learning and another – for the learning of frequency structure of the environment. [ABSTRACT FROM AUTHOR]

Strategies used in artificial grammar learning can shed light into the abilities of different species to extract regularities from the environment. In the A(X)nB rule, A and B items are linked, but assigned to different positional categories and separated by distractor items. Open questions are how widespread is the ability to extract positional regularities from A(X)nB patterns, which strategies are used to encode positional regularities and whether individuals exhibit preferences for absolute or relative position encoding. We used visual arrays to investigate whether cotton-top tamarins (Saguinusoedipus) can learn this rule and which strategies they use. After training on a subset of exemplars, two of the tested monkeys successfully generalized to novel combinations. These tamarins discriminated between categories of tokens with different properties (A, B, X) and detected a positional relationship between non-adjacent items even in the presence of novel distractors. The pattern of errors revealed that successful subjects used visual similarity with training stimuli to solve the task and that successful tamarins extracted the relative position of As and Bs rather than their absolute position, similarly to what has been observed in other species. Relative position encoding appears to be favoured in different tasks and taxa. Generalization, though, was incomplete, since we observed a failure with items that during training had always been presented in reinforced arrays, showing the limitations in grasping the underlying positional rule. These results suggest the use of local strategies in the extraction of positional rules in cotton-top tamarins. [ABSTRACT FROM AUTHOR]