Statistical learning of language: Theory, validity, and predictions of a statistical learning account of language acquisition

Electrophysiological markers of orthographic pattern learning in school-aged children with reading challenges: An ERP investigation

BACKGROUND

Previous studies suggested that children with reading difficulty have impaired statistical learning ability in extracting distributional orthographic regularities. However, the neural mechanisms underlying have not been fully investigated.

AIMS

The current study aimed to identify the electrophysiological markers and to examine the neural underpinnings of statistical learning of orthographic regularities in children with reading difficulties.

METHODS AND PROCEDURES

Using the event-related potentials (ERPs) and the orthographic learning task, 157 children were exposed to a sequence of artificial pseudocharacters with varying levels of positional and semantic consistency (low at 60 %, moderate at 80 %, and high at 100 %).

OUTCOMES AND RESULTS

Poor readers elicited an increased N170 response in the low consistency and a lack of left-lateralized P300 effect when learning positional regularities of radicals. Similarly, larger N170 effects were observed in poor readers, while similar N400 effects were found in both poor and average readers when learning semantic regularities of radicals.

CONCLUSIONS AND IMPLICATIONS

Our findings indicate that poor readers may have trouble using statistical information for early-stage orthographic pattern extraction, yet they can identify semantic inconsistencies after sufficient exposure. These results deepen our understanding of the neural mechanisms involved in statistical learning for poor readers and aid in improving criteria for differentiating between typically developing children and those with reading challenges.

Statistical word segmentation succeeds given the minimal amount of exposure

One of the first tasks in language acquisition is word segmentation, a process to extract word forms from continuous speech streams. Statistical approaches to word segmentation have been shown to be a powerful mechanism, in which word boundaries are inferred from sequence statistics. This approach requires the learner to represent the frequency of units from syllable sequences, though accounts differ on how much statistical exposure is required. In this study, we examined the computational limit with which words can be extracted from continuous sequences. First, we discussed why two occurrences of a word in a continuous sequence is the computational lower limit for this word to be statistically defined. Next, we created short syllable sequences that contained certain words either two or four times. Learners were presented with these syllable sequences one at a time, immediately followed by a test of the novel words from these sequences. We found that, with the computationally minimal amount of two exposures, words were successfully segmented from continuous sequences. Moreover, longer syllable sequences providing four exposures to words generated more robust learning results. The implications of these results are discussed in terms of how learners segment and store the word candidates from continuous sequences.

Predictive processing of music and language in autism: Evidence from Mandarin and English speakers

Atypical predictive processing has been associated with autism across multiple domains, based mainly on artificial antecedents and consequents. As structured sequences where expectations derive from implicit learning of combinatorial principles, language and music provide naturalistic stimuli for investigating predictive processing. In this study, we matched melodic and sentence stimuli in cloze probabilities and examined musical and linguistic prediction in Mandarin- (Experiment 1) and English-speaking (Experiment 2) autistic and non-autistic individuals using both production and perception tasks. In the production tasks, participants listened to unfinished melodies/sentences and then produced the final notes/words to complete these items. In the perception tasks, participants provided expectedness ratings of the completed melodies/sentences based on the most frequent notes/words in the norms. While Experiment 1 showed intact musical prediction but atypical linguistic prediction in autism in the Mandarin sample that demonstrated imbalanced musical training experience and receptive vocabulary skills between groups, the group difference disappeared in a more closely matched sample of English speakers in Experiment 2. These findings suggest the importance of taking an individual differences approach when investigating predictive processing in music and language in autism, as the difficulty in prediction in autism may not be due to generalized problems with prediction in any type of complex sequence processing.

Unraveling the complex interplay between statistical learning and working memory in Chinese children with and without dyslexia across different ages

The relation between statistical learning and working memory in children with developmental dyslexia (DD) remains unclear. This study employed a distributional and a conditional statistical learning experiment and a working memory task to examine this relation in 651 Chinese 6- to 12-year-olds with and without DD (NDD = 199, 101 females; NwoDD = 452, 227 females; participated 2014-2019). Results showed working memory positively associated with recognizing high-predictable and familiar items in both groups, but negatively associated with recognizing unfamiliar items in the DD group only. These findings uncovered the complex interplay between statistical learning and working memory, demonstrating how different working memory abilities in children with and without DD relate to various statistical learning mechanisms at the item level.

Syllabic rhythm and prior linguistic knowledge interact with individual differences to modulate phonological statistical learning

Phonological statistical learning - our ability to extract meaningful regularities from spoken language - is considered critical in the early stages of language acquisition, in particular for helping to identify discrete words in continuous speech. Most phonological statistical learning studies use an experimental task introduced by Saffran et al. (1996), in which the syllables forming the words to be learned are presented continuously and isochronously. This raises the question of the extent to which this purportedly powerful learning mechanism is robust to the kinds of rhythmic variability that characterize natural speech. Here, we tested participants with arhythmic, semi-rhythmic, and isochronous speech during learning. In addition, we investigated how input rhythmicity interacts with two other factors previously shown to modulate learning: prior knowledge (syllable order plausibility with respect to participants' first language) and learners' speech auditory-motor synchronization ability. We show that words are extracted by all learners even when the speech input is completely arhythmic. Interestingly, high auditory-motor synchronization ability increases statistical learning when the speech input is temporally more predictable but only when prior knowledge can also be used. This suggests an additional mechanism for learning based on predictions not only about when but also about what upcoming speech will be.

Enhancing Syntactic Knowledge in School-Age Children With Developmental Language Disorder: The Promise of Syntactic Priming

PURPOSE

We propose that implicit learning, including syntactic priming, has therapeutic promise to enhance the syntactic knowledge of children with developmental language disorder (DLD).

METHOD

We review the chunk-based learning framework of syntactic learning, the developmental evidence in support of it, and the developmental literature on syntactic priming as an instance of chunk-based statistical learning. We use this framework to help understand the nature of the syntactic learning difficulties of children with DLD. We then briefly review the available explicit treatments for syntactic deficits and end by proposing an implicit training activity that integrates syntactic priming with the principles of statistical learning.

RESULTS

Statistically induced chunk-based learning is a powerful driver of syntactic learning, and syntactic priming is a form of this learning. Repeated priming episodes during everyday language interactions lead children to create abstract and global syntactic representations in long-term memory. We offer some thoughts on an implicit language intervention approach with syntactic priming at its center.

CONCLUSIONS

Children's learning of syntactic structures is influenced by repeated syntactic priming experiences. Including a syntactic priming activity in our language intervention toolbox has the promise to enhance children's syntactic knowledge and sentence comprehension and production abilities.

Reliability of individual differences in distractor suppression driven by statistical learning

A series of recent studies has demonstrated that attentional selection is modulated by statistical regularities, even when they concern task-irrelevant stimuli. Irrelevant distractors presented more frequently at one location interfere less with search than distractors presented elsewhere. To account for this finding, it has been proposed that through statistical learning, the frequent distractor location becomes suppressed relative to the other locations. Learned distractor suppression has mainly been studied at the group level, where individual differences are treated as unexplained error variance. Yet these individual differences may provide important mechanistic insights and could be predictive of cognitive and real-life outcomes. In the current study, we ask whether in an additional singleton task, the standard measures of attentional capture and learned suppression are reliable and stable at the level of the individual. In an online study, we assessed both the within- and between-session reliability of individual-level measures of attentional capture and learned suppression. We show that the measures of attentional capture, but not of distractor suppression, are moderately stable within the same session (i.e., split-half reliability). Test-retest reliability over a 2-month period was found to be moderate for attentional capture but weak or absent for suppression. RT-based measures proved to be superior to accuracy measures. While producing very robust findings at the group level, the predictive validity of these RT-based measures is still limited when it comes to individual-level performance. We discuss the implications for future research drawing on inter-individual variation in the attentional biases that result from statistical learning.

The influence of language-specific properties on the role of consonants and vowels in a statistical learning task of an artificial language: A cross-linguistic comparison

The contribution of consonants and vowels in spoken word processing has been widely investigated, and studies have found a phenomenon of a Consonantal bias (C-bias), indicating that consonants carry more weight than vowels. However, across languages, various patterns have been documented, including that of no preference or a reverse pattern of Vowel bias. A central question is how the manifestation of the C-bias is modulated by language-specific factors. This question can be addressed by cross-linguistic studies. Comparing native Hebrew and native English speakers, this study examines the relative importance of transitional probabilities between non-adjacent consonants as opposed to vowels during auditory statistical learning (SL) of an artificial language. Hebrew is interesting because its complex Semitic morphological structure has been found to play a central role in lexical access, allowing us to examine whether morphological properties can modulate the C-bias in early phases of speech perception, namely, word segmentation. As predicted, we found a significant interaction between language and consonant/vowel manipulation, with a higher performance in the consonantal condition than in the vowel condition for Hebrew speakers, namely, C-bias, and no consonant/vowel asymmetry among English speakers. We suggest that the observed interaction is morphologically anchored, indicating that phonological and morphological processes interact during early phases of auditory word perception.

Specificity of Motor Contributions to Auditory Statistical Learning

Statistical learning is the ability to extract patterned information from continuous sensory signals. Recent evidence suggests that auditory-motor mechanisms play an important role in auditory statistical learning from speech signals. The question remains whether auditory-motor mechanisms support such learning generally or in a domain-specific manner. In Experiment 1, we tested the specificity of motor processes contributing to learning patterns from speech sequences. Participants either whispered or clapped their hands while listening to structured speech. In Experiment 2, we focused on auditory specificity, testing whether whispering equally affects learning patterns from speech and non-speech sequences. Finally, in Experiment 3, we examined whether learning patterns from speech and non-speech sequences are correlated. Whispering had a stronger effect than clapping on learning patterns from speech sequences in Experiment 1. Moreover, whispering impaired statistical learning more strongly from speech than non-speech sequences in Experiment 2. Interestingly, while participants in the non-speech tasks spontaneously synchronized their motor movements with the auditory stream more than participants in the speech tasks, the effect of the motor movements on learning was stronger in the speech domain. Finally, no correlation between speech and non-speech learning was observed. Overall, our findings support the idea that learning statistical patterns from speech versus non-speech relies on segregated mechanisms, and that the speech motor system contributes to auditory statistical learning in a highly specific manner.

Does bilingual experience influence statistical language learning?

Statistical language learning (SL) tasks measure different aspects of foreign language learning. Studies have used SL tasks to investigate whether bilingual experience confers advantages in acquiring additional languages through implicit processes. However, the results have been inconsistent, which may be related to bilingualism-related features (e.g., degree of dissimilarity between the specific language pair) and other variables such as specific processes that are targeted by the SL task. In the present study, we compared the performance of one Spanish monolingual and two bilingual (Spanish-Basque and Spanish-English) groups across three well-established SL tasks. Each task targeted a different aspect of foreign language learning; specifically, word segmentation, morphological rule generalization, and word-referent learning. In Experiment 1, we manipulated sub-lexical phonotactic patterns to vary the difficulty of three SL tasks, with the results showing no differences between the groups in word segmentation. In Experiment 2, we included non-adjacent dependencies to target affixal morphology rule learning, but again no group-related differences were found. In Experiment 3, we addressed word learning using an audio-visual SL task combining exclusive and multiple word-referent mappings, and found that bilinguals outperformed monolinguals, suggesting that bilingualism may exert influences on SL at the lexical level. This advantage might have been mediated by the high working memory demands required to perform the task. Summarizing, this study shows no evidence for a general bilingual advantage in SL, although bilinguals may outperform monolinguals under specific experimental conditions such as SL tasks that place high demands on working memory processes. In addition, the similar performance of Spanish-Basque and Spanish-English bilinguals across all three SL tasks suggests that the degree of dissimilarity between pairs of spoken languages does not modulate SL skills.

Using agreement probability to study differences in types of concepts and conceptualizers

Agreement probability p(a) is a homogeneity measure of lists of properties produced by participants in a Property Listing Task (PLT) for a concept. Agreement probability's mathematical properties allow a rich analysis of property-based descriptions. To illustrate, we use p(a) to delve into the differences between concrete and abstract concepts in sighted and blind populations. Results show that concrete concepts are more homogeneous within sighted and blind groups than abstract ones (i.e., exhibit a higher p(a) than abstract ones) and that concrete concepts in the blind group are less homogeneous than in the sighted sample. This supports the idea that listed properties for concrete concepts should be more similar across subjects due to the influence of visual/perceptual information on the learning process. In contrast, abstract concepts are learned based mainly on social and linguistic information, which exhibit more variability among people, thus, making the listed properties more dissimilar across subjects. Relative to abstract concepts, the difference in p(a) between sighted and blind is not statistically significant. Though this is a null result, and should be considered with care, it is expected because abstract concepts should be learned by paying attention to the same social and linguistic input in both, blind and sighted, and thus, there is no reason to expect that the respective lists of properties should differ. Finally, we used p(a) to classify concrete and abstract concepts with a good level of certainty. All these analyses suggest that p(a) can be fruitfully used to study data obtained in a PLT.

Seeking the neural representation of statistical properties in print during implicit processing of visual words

Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of distributional statistical properties of words on visual word processing, including the effects of word frequency (lexical level) and mappings between orthography, phonology, and semantics (sub-lexical level). However, there has been scant evidence to directly confirm that the statistical properties contained in print can be directly characterized by neural activities. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical properties in visual word processing. From the perspective of predictive coding, an equal probability sequence with low built-in prediction precision and three oddball sequences with high built-in prediction precision were designed with consistent and three types of inconsistent (orthographically inconsistent, orthography-to-phonology inconsistent, and orthography-to-semantics inconsistent) Chinese characters as visual stimuli. In the three oddball sequences, consistent characters were set as the standard stimuli (probability of occurrence p = 0.75) and three types of inconsistent characters were set as deviant stimuli (p = 0.25), respectively. In the equal probability sequence, the same consistent and inconsistent characters were presented randomly with identical occurrence probability (p = 0.25). Significant neural representation activities of word frequency were observed in the equal probability sequence. By contrast, neural representations of sub-lexical statistics only emerged in oddball sequences where short-term predictions were shaped. These findings reveal that the statistical properties learned from long-term print environment continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.

Realistic and broad-scope learning simulations: first results and challenges

There is a current 'theory crisis' in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic input and multiple levels of language, have the potential to contribute significantly to our understanding of language acquisition. We then review recent results obtained through such language learning simulations. Finally, we propose some guidelines for the community to build better simulations.

Statistical Information Affects Spoken Word Recognition of Tone Languages in Stutterers: Evidence From an Auditory-Perceptual Gating Study

PURPOSE

Previous studies have shown that individuals who stutter exhibit abnormal speech perception in addition to disfluent production as compared with their nonstuttering peers. This study investigated whether adult Chinese-speaking stutterers are still able to use knowledge of statistical regularities embedded in their native language to recognize spoken words and, if so, how much acoustic information is needed to trigger this information.

METHOD

Seventeen stutterers and 20 typical, nonstuttering controls participated in a gating experiment. All participants listened to monosyllabic words that consisted of syllables and lexical tones and were segmented into eight successive gates. These words differed in syllable token frequency and syllable-tone co-occurrence probability in line with a Chinese spoken word corpus. The correct syllable-only, correct tone-only, correct syllable-tone word, and correct syllable-incorrect tone responses were analyzed between the two groups using mixed-effects models.

RESULTS

Stutterers were less accurate overall than controls, with fewer correct syllables, tones, and their combination as words. However, stutterers showed consistent and reliable perceptual patterns triggered by statistical information of speech, as reflected by more accurate responses to high-frequency syllables, high-probability tones, and tone errors all in manners similar to those of nonstuttering controls.

CONCLUSIONS

Stutterers' atypical speech perception is not due to a lack of statistical learning. Stutterers were able to perceive spoken words with phonological tones based on statistical regularities embedded in their native speech. This finding echoes previous production studies of stuttering and lends some support for a link between perception and production. Implications of pathological, diagnostic, and therapeutic conditions of stuttering are discussed.

Intact procedural memory and impaired auditory statistical learning in adults with dyslexia

Developmental dyslexia is a reading disorder that is associated with atypical brain function. One neuropsychological theory posits that dyslexia reflects a deficit in the procedural memory system, which supports implicit learning, or the acquisition of knowledge without conscious awareness or intention. This study investigated various forms of procedural learning in adults with dyslexia and typically-reading adults. Adults with dyslexia exhibited typical skill learning on mirror tracing and rotary pursuit tasks that have been well-established as reflecting purely procedural memory and dependent on basal ganglia and cerebellar structures. They also exhibited typical statistical learning for visual material, but impaired statistical learning for auditory material. Auditory statistical learning proficiency correlated positively with single-word reading performance across all participants and within the group with dyslexia, linking a major difficulty in dyslexia with impaired auditory statistical learning. These findings dissociate multiple forms of procedural memory that are intact in dyslexia from a specific impairment in auditory statistical learning that is associated with reading difficulty.

The association between statistical learning and language development during childhood: A scoping review

The statistical account of language acquisition asserts that language is learned through computations on the statistical regularities present in natural languages. This type of account can predict variability in language development measures as arising from individual differences in extracting this statistical information. Given that statistical learning has been attested across different domains and modalities, a central question is which modality is more tightly yoked with language skills. The results of a scoping review, which aimed for the first time at identifying the evidence of the association between statistical learning skills and language outcomes in typically developing infants and children, provide preliminary support for the statistical learning account of language acquisition, mostly in the domain of lexical outcomes, indicating that typically developing infants and children with stronger auditory and audio-visual statistical learning skills perform better on lexical competence tasks. The results also suggest that the relevance of statistical learning skills for language development is dependent on sensory modality.

Tone Deafness in Music Does Not Preclude Distributional Learning of Nonnative Tonal Languages in Individuals With Congenital Amusia

PURPOSE

Previous studies have shown that individuals with congenital amusia exhibit deficient pitch processing across music and language domains. This study investigated whether adult Chinese-speaking listeners with amusia were still able to learn Thai lexical tones based on stimulus frequency of statistical distribution via distributional learning, despite their degraded lexical tone perception.

METHOD

Following a pretest-training-posttest design, 21 amusics and 23 typical, musically intact listeners were assigned into bimodal and unimodal distribution conditions. Listeners were asked to discriminate minimal pairs of Thai mid-level tone and falling tone superimposed on variable base syllables and uttered by different speakers. The perceptual accuracy for each test session and improvement from pretest to posttest were collected and analyzed between the two groups using generalized mixed-effects models.

RESULTS

When discriminating Thai lexical tones, amusics were less accurate than typical listeners. Nonetheless, similarly to control listeners, perceptual gains from pretest to posttest were observed in bimodally rather than unimodally trained amusics, as evidenced by both trained and nontrained test words.

CONCLUSIONS

Amusics are able to learn lexical tones in a second or foreign context of speech. This extends previous research by showing that amusics' distributional learning of linguistic pitch remains largely preserved despite their degraded pitch processing. It is thus likely that manifestations of amusia in speech could not result from their abnormal statistical learning mechanism. This study meanwhile provides a heuristic approach for future studies to apply this paradigm into amusics' treatment to mitigate their pitch-processing disorder.

Weight status impacts children's incidental statistical learning

The expanding literature investigating the cognitive effects of childhood weight status has not included examinations of incidental statistical learning, the process by which children unintentionally acquire knowledge about patterns in their environments, despite evidence that it underlies many higher-level information processing capabilities. In the present study, we measured event-related potentials (ERPs) while school-aged participants completed a variation of an oddball task in which stimuli predicted the appearance of a target. Children were asked to respond to the target but were not given any information about the existence of predictive dependencies. We found that children with a healthy weight status had larger P3 amplitudes in response to the predictors that were most meaningful in completing the task, a finding that may suggest optimized learning mechanisms influenced by weight status. These findings offer an important first step to understanding how healthy lifestyle factors may influence incidental statistical learning.

The contribution of theta and delta to feedback processing in children with developmental language disorder

PURPOSE

The study aimed at evaluating feedback processing at the electrophysiological level and its relation to learning in children with developmental language disorder (DLD) to further advance our understanding of the underlying neural mechanisms of feedback-based learning in children with this disorder.

METHOD

A feedback-based probabilistic learning task required children to classify novel cartoon animals into two categories that differ on five binary features, the probabilistic combination of which determined classification. The learning outcomes' variance in relation to time- and time-frequency measures of feedback processing were examined and compared between 20 children with developmental language disorder and 25 age-matched children with typical language development.

RESULTS

Children with developmental language disorder (DLD) performed poorer on the task when compared with their age-matched peers with typical language development (TD). The electrophysiological data in the time domain indicated no differences in the processing of positive and negative feedback among children with DLD. However, the time-frequency analysis revealed a strong theta activity in response to negative feedback in this group, suggesting an initial distinction between positive and negative feedback that was not captured by the ERP data. In the TD group, delta activity played a major role in shaping the FRN and P3a and was found to predict test performance. Delta did not contribute to the FRN and P3a in the DLD group. Additionally, theta and delta activities were not associated with the learning outcomes of children with DLD.

CONCLUSION

Theta activity, which is associated with the initial processing of feedback at the level of the anterior cingulate cortex, was detected in children with developmental language disorder (DLD) but was not associated with their learning outcomes. Delta activity, which is assumed to be generated by the striatum and to be linked to elaborate processing of outcomes and adjustment of future actions, contributed to processing and learning outcomes of children with typical language development but not of children with DLD. The results provide evidence for atypical striatum-based feedback processing in children with DLD.

Target voice probability influences enhancement in auditory selective attention

Auditory selective attention is thought to consist of two mechanisms: an enhancement mechanism that boosts the target signal, and a suppression mechanism that attenuates concurrent distracting signals. The current study explored the conditions necessary to observe enhancement of predictable auditory objects. Participants heard scenes consisting of three voices and a distracting noise. They were asked to find the gender singleton (target) and report whether it was saying even or odd numbers. One of the voices appeared as the high-probability target (70%) across trials. We expected responses to be faster when the high-probability target was in the scene, and results from Experiment 1 supported that prediction. However, this target enhancement effect was substantially weakened when a distracting noise was also in the scene, suggesting that the distractor captured attention and interfered with enhancement. Experiment 2 tested the hypothesis that distractor predictability modulates target enhancement by varying the probability of the distractor. Although this hypothesis was not supported, the results of Experiment 1 were replicated. Findings support the existence of an easily disruptable enhancement mechanism that boosts the representation of highly probable target objects.

Development of statistical learning ability across modalities, domains, and languages

Statistical learning (SL) is defined as our ability to use statistics (e.g., frequencies or transitional probabilities) to detect implicit regularities in the environment. Limited research has examined the developmental trajectory of SL across domains and modalities, and no previous research has made systematic comparisons across domains, modalities, and languages using comparable tasks. The current study investigated the development of SL ability across 9-, 11-, and 13-year-old native Chinese-speaking children in non-linguistic visual and auditory SL, first-language Chinese visual and auditory SL, and second-language English visual and auditory SL. Results showed that children across the three age groups achieved all types of SL, and they performed better in visual modality than in auditory modality. Furthermore, while visual SL constantly improved from 9- to 11- to 13-year-olds, auditory SL improved only from 11- to 13-year-olds but not from 9- to 11-year-olds, which could be explained by the discrepancy in developmental trajectory between auditory language and working memory. This pattern of age and modality interaction was similar across non-linguistic Chinese and English SL. A significant interaction between modality and language type also showed that better learning was achieved in visual SL as compared with auditory SL in both non-linguistic and English stimuli. However, children performed similarly across the two modalities in Chinese, possibly due to the contribution of tonal information. Together, our findings point to the joint function of age, modality, and language type in SL development.

Kindergarteners Use Cross-Situational Statistics to Infer the Meaning of Grammatical Elements

Many studies demonstrate that detecting statistical regularities in linguistic input plays a key role in language acquisition. Yet, it is unclear to what extent statistical learning is involved in more naturalistic settings, when young children have to acquire meaningful grammatical elements. In the present study, we address these points, by investigating whether statistical learning is involved in acquiring a morpho-syntactic structure from input that resembles natural languages more closely. We exposed 50 kindergarteners (M = 5 years, 5 months) to a miniature language in which they had to learn a grammatical marker that expressed number, and which could only be acquired on the basis of the distributional properties in the input. Half of the children performed an attention check during the experiment. Results show that young children are able to learn this meaning. We found no clear evidence that facilitating attention to the input increases learning performance.

Learning words without trying: Daily second language podcasts support word-form learning in adults

Spoken language contains overlapping patterns across different levels, from syllables to words to phrases. The discovery of these structures may be partially supported by statistical learning (SL), the unguided, automatic extraction of regularities from the environment through passive exposure. SL supports word learning in artificial language experiments, but few studies have examined whether it scales up to support natural language learning in adult second language learners. Here, adult English speakers (n = 70) listened to daily podcasts in either Italian or English for 2 weeks while going about their normal routines. To measure word knowledge, participants provided familiarity ratings of Italian words and nonwords both before and after the listening period. Critically, compared with English controls, Italian listeners significantly improved in their ability to discriminate Italian words and nonwords. These results suggest that unguided exposure to natural, foreign language speech supports the extraction of relevant word features and the development of nascent word forms. At a theoretical level, these findings indicate that SL may effectively scale up to support real-world language acquisition. These results also have important practical implications, suggesting that adult learners may be able to acquire relevant speech patterns and initial word forms simply by listening to the language. This form of learning can occur without explicit effort, formal instruction or focused study.

The Effects of Cooperative and Competitive Situations on Statistical Learning

Devising cooperative or competitive situations is an important teaching strategy in educational practices. Nevertheless, there is still controversy regarding which situation is better for learning. This study was conducted to explore the effects of cooperative and competitive situations on statistical learning, through the alternating serial reaction time (ASRT) task. Individual cooperative and competitive situations were devised in this study, in which individual situation served as the control condition. Ninety recruited participants were randomly assigned to a cooperative, competitive, or individual group to perform the ASRT task. For general learning, cooperative and competitive situations could indeed make learners respond faster, and there was no significant difference in the RT between the cooperative and competitive groups. Moreover, statistical learning was observed in all three groups. An additional analysis of the early stage of the experiment showed that the learning effect of the competitive group was greater than those of the cooperative and individual groups, in terms of statistical learning. However, the final learning effect was not significantly different among the three groups. Overall, the cooperative and competitive situations had a positive impact on learning and enabled the students to acquire approximately the same learning effect in a shorter time period, compared with the individual situation. Specifically, the competitive situation accelerated the statistical learning process but not the general learning process.

Repeated series learning revisited with a novel prediction on the reduced effect of item frequency in dyslexia

Developmental dyslexia, a difficulty with acquiring fluent reading, has also been characterized by reduced short-term memory (STM) capacity, which is often operationalized with span tasks. The low performance of individuals with dyslexia (IDDs) in such tasks is commonly attributed to poor phonological memory. However, we suggest an alternative explanation based on the observation that many times the items that are used in spans tasks are high-frequency items (e.g., digit words). We suggest that IDDs do not enjoy the benefit of item frequency to the same extent as controls, and thus their performance in span tasks is especially hampered. On the contrary, learning of repeated sequences was shown to be largely independent of item frequency, and therefore this type of learning may be unimpaired in dyslexia. To test both predictions, we used the Hebb-learning paradigm. We found that IDDs’ performance is especially poor compared to controls’ when high-frequency items are used, and that their repeated series learning does not differ from that of controls. Taken together with existing literature, our findings suggest that impaired learning of repeated series is not a core characteristic of dyslexia, and that the reports on reduced STM in dyslexia may to a large extent be explained by reduced benefit of item frequency.

Linking the neural basis of distributional statistical learning with transitional statistical learning: The paradox of attention

Statistical learning, the process of tracking distributional information and discovering embedded patterns, is traditionally regarded as a form of implicit learning. However, recent studies proposed that both implicit (attention-independent) and explicit (attention-dependent) learning systems are involved in statistical learning. To understand the role of attention in statistical learning, the current study investigates the cortical processing of distributional patterns in speech across local and global contexts. We then ask how these cortical responses relate to statistical learning behavior in a word segmentation task. We found Event-Related Potential (ERP) evidence of pre-attentive processing of both the local (mismatching negativity) and global distributional information (late discriminative negativity). However, as speech elements became less frequent and more surprising, some participants showed an involuntary attentional shift, reflected in a P3a response. Individuals who displayed attentive neural tracking of distributional information showed faster learning in a speech statistical learning task. These results suggest that an involuntary attentional shift might play a facilitatory, but not essential, role in statistical learning.

Acquiring Complex Communicative Systems: Statistical Learning of Language and Emotion

During the early postnatal years, most infants rapidly learn to understand two naturally evolved communication systems: language and emotion. While these two domains include different types of content knowledge, it is possible that similar learning processes subserve their acquisition. In this review, we compare the learnable statistical regularities in language and emotion input. We then consider how domain-general learning abilities may underly the acquisition of language and emotion, and how this process may be constrained in each domain. This comparative developmental approach can advance our understanding of how humans learn to communicate with others.

Explicit Instructions Do Not Enhance Auditory Statistical Learning in Children With Developmental Language Disorder: Evidence From Event-Related Potentials

A current issue in psycholinguistic research is whether the language difficulties exhibited by children with developmental language disorder [DLD, previously labeled specific language impairment (SLI)] are due to deficits in their abilities to pick up patterns in the sensory environment, an ability known as statistical learning (SL), and the extent to which explicit learning mechanisms can be used to compensate for those deficits. Studies designed to test the compensatory role of explicit learning mechanisms in children with DLD are, however, scarce, and the few conducted so far have led to inconsistent results. This work aimed to provide new insights into the role that explicit learning mechanisms might play on implicit learning deficits in children with DLD by resorting to a new approach. This approach involved not only the collection of event-related potentials (ERPs), while preschool children with DLD [relative to typical language developmental (TLD) controls] were exposed to a continuous auditory stream made of the repetition of three-syllable nonsense words but, importantly, the collection of ERPs when the same children performed analogous versions of the same auditory SL task first under incidental (implicit) and afterward under intentional (explicit) conditions. In each of these tasks, the level of predictability of the three-syllable nonsense words embedded in the speech streams was also manipulated (high vs. low) to mimic natural languages closely. At the end of both tasks' exposure phase, children performed a two-alternative forced-choice (2-AFC) task from which behavioral evidence of SL was obtained. Results from the 2-AFC tasks failed to show reliable signs of SL in both groups of children. The ERPs data showed, however, significant modulations in the N100 and N400 components, taken as neural signatures of word segmentation in the brain, even though a detailed analysis of the neural responses revealed that only children from the TLD group seem to have taken advantage of the previous knowledge to enhance SL functioning. These results suggest that children with DLD showed deficits both in implicit and explicit learning mechanisms, casting doubts on the efficiency of the interventions relying on explicit instructions to help children with DLD to overcome their language difficulties.

Learning Words in Two Languages: Manipulating Exemplar Variability for Within- and Cross-Language Generalization

PURPOSE

This article aims to describe how exemplar variability can manipulate the word learning environment to maximize within- and cross-language generalization in Spanish-English bilinguals. Furthermore, we examined sources of individual variability that predicted word learning.

METHOD

Nineteen Spanish-English bilingual children participated in a word learning task presenting words in both languages. Children learned words either in a high variability condition (in which multiple exemplars are introduced with the target word) or in a no variability condition (in which the same referent is used with the target word). Word learning was tracked over the course of the training, and retention was examined once the training was discontinued. Children's generalization of referents within and across languages was also examined.

RESULTS

The exemplar variability effect was observed in within-language generalization trials, whereas cross-language generalization was less robust. Nevertheless, cross-language associations emerged in examining the role of language proficiency, such that semantic skills in English predicted word retention across languages. Similarly, children's propensity to code-switch during language production was positively correlated with retention of words learned in the high variability condition.

CONCLUSIONS

The findings show that Spanish-English bilingual children may make use of exemplar variability to support word learning in different ways compared with monolinguals. The exemplar variability effect interacts with children's acquired language skills and word learning abilities at the start of the intervention. This study provides preliminary evidence from which future research can develop word learning interventions that are responsive to the needs of multilinguals.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.19241856.

Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning

Statistical learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive electroencephalograph (EEG) recordings of neural activity in humans. Importantly, we used carefully constructed controls to address potential acoustic confounds of the frequency-tagging approach, and compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Comparison of the neural metric to previously established behavioral measures for assessing SL showed a significant yet weak correspondence with performance on an implicit task, which was above-chance in 70% of participants, but no correspondence with the more common explicit 2-alternative forced-choice task, where performance did not exceed chance-level. Given the proposed ubiquitous nature of SL, our results highlight some of the operational and methodological challenges of obtaining robust metrics for assessing SL, as well as the potential confounds that should be taken into account when using the frequency-tagging approach in EEG studies.

The learnability consequences of Zipfian distributions in language

While the languages of the world differ in many respects, they share certain commonalties, which can provide insight on our shared cognition. Here, we explore the learnability consequences of one of the striking commonalities between languages. Across languages, word frequencies follow a Zipfian distribution, showing a power law relation between a word's frequency and its rank. While their source in language has been studied extensively, less work has explored the learnability consequences of such distributions for language learners. We propose that the greater predictability of words in this distribution (relative to less skewed distributions) can facilitate word segmentation, a crucial aspect of early language acquisition. To explore this, we quantify word predictability using unigram entropy, assess it across languages using naturalistic corpora of child-directed speech and then ask whether similar unigram predictability facilitates word segmentation in the lab. We find similar unigram entropy in child-directed speech across 15 languages. We then use an auditory word segmentation task to show that the unigram predictability levels found in natural language are uniquely facilitative for word segmentation for both children and adults. These findings illustrate the facilitative impact of skewed input distributions on learning and raise questions about the possible role of cognitive pressures in the prevalence of Zipfian distributions in language.

Cognitive mechanisms of statistical learning and segmentation of continuous sensory input

Two classes of cognitive mechanisms have been proposed to explain segmentation of continuous sensory input into discrete recurrent constituents: clustering and boundary-finding mechanisms. Clustering mechanisms are based on identifying frequently co-occurring elements and merging them together as parts that form a single constituent. Bracketing (or boundary-finding) mechanisms work by identifying rarely co-occurring elements that correspond to the boundaries between discrete constituents. In a series of behavioral experiments, I tested which mechanisms are at play in the visual modality both during segmentation of a continuous syllabic sequence into discrete word-like constituents and during recognition of segmented constituents. Additionally, I explored conscious awareness of the products of statistical learning—whole constituents versus merged clusters of smaller subunits. My results suggest that both online segmentation and offline recognition of extracted constituents rely on detecting frequently co-occurring elements, a process likely based on associative memory. However, people are more aware of having learnt whole tokens than of recurrent composite clusters.

Voice modulatory cues to structure across languages and species

Voice modulatory cues such as variations in fundamental frequency, duration and pauses are key factors for structuring vocal signals in human speech and vocal communication in other tetrapods. Voice modulation physiology is highly similar in humans and other tetrapods due to shared ancestry and shared functional pressures for efficient communication. This has led to similarly structured vocalizations across humans and other tetrapods. Nonetheless, in their details, structural characteristics may vary across species and languages. Because data concerning voice modulation in non-human tetrapod vocal production and especially perception are relatively scarce compared to human vocal production and perception, this review focuses on voice modulatory cues used for speech segmentation across human languages, highlighting comparative data where available. Cues that are used similarly across many languages may help indicate which cues may result from physiological or basic cognitive constraints, and which cues may be employed more flexibly and are shaped by cultural evolution. This suggests promising candidates for future investigation of cues to structure in non-human tetrapod vocalizations. This article is part of the theme issue 'Voice modulation: from origin and mechanism to social impact (Part I)'.

The autistic brain can process local but not global emotion regularities in facial and musical sequences

Whether autism spectrum disorder (ASD) is associated with a global processing deficit remains controversial. Global integration requires extraction of regularity across various timescales, yet little is known about how individuals with ASD process regularity at local (short timescale) versus global (long timescale) levels. To this end, we used event-related potentials to investigate whether individuals with ASD would show different neural responses to local (within trial) versus global (across trials) emotion regularities extracted from sequential facial expressions; and if so, whether this visual abnormality would generalize to the music (auditory) domain. Twenty individuals with ASD and 21 age- and IQ-matched individuals with typical development participated in this study. At an early processing stage, ASD participants exhibited preserved neural responses to violations of local emotion regularity for both faces and music. At a later stage, however, there was an absence of neural responses in ASD to violations of global emotion regularity for both faces and music. These findings suggest that the autistic brain responses to emotion regularity are modulated by the timescale of sequential stimuli, and provide insight into the neural mechanisms underlying emotional processing in ASD.

Children's Learning of Non-adjacent Dependencies Using a Web-Based Computer Game Setting

Infants show impressive speech decoding abilities and detect acoustic regularities that highlight the syntactic relations of a language, often coded via non-adjacent dependencies (NADs, e.g., is singing). It has been claimed that infants learn NADs implicitly and associatively through passive listening and that there is a shift from effortless associative learning to a more controlled learning of NADs after the age of 2 years, potentially driven by the maturation of the prefrontal cortex. To investigate if older children are able to learn NADs, Lammertink et al. (2019) recently developed a word-monitoring serial reaction time (SRT) task and could show that 6-11-year-old children learned the NADs, as their reaction times (RTs) increased then they were presented with violated NADs. In the current study we adapted their experimental paradigm and tested NAD learning in a younger group of 52 children between the age of 4-8 years in a remote, web-based, game-like setting (whack-a-mole). Children were exposed to Italian phrases containing NADs and had to monitor the occurrence of a target syllable, which was the second element of the NAD. After exposure, children did a "Stem Completion" task in which they were presented with the first element of the NAD and had to choose the second element of the NAD to complete the stimuli. Our findings show that, despite large variability in the data, children aged 4-8 years are sensitive to NADs; they show the expected differences in r RTs in the SRT task and could transfer the NAD-rule in the Stem Completion task. We discuss these results with respect to the development of NAD dependency learning in childhood and the practical impact and limitations of collecting these data in a web-based setting.

Language Processing Differences Between Blind and Sighted Individuals and the Abstract Versus Concrete Concept Difference

In the property listing task (PLT), participants are asked to list properties for a concept (e.g., for the concept dog, "barks," and "is a pet" may be produced). In conceptual property norming (CPNs) studies, participants are asked to list properties for large sets of concepts. Here, we use a mathematical model of the property listing process to explore two longstanding issues: characterizing the difference between concrete and abstract concepts, and characterizing semantic knowledge in the blind versus sighted population. When we apply our mathematical model to a large CPN reporting properties listed by sighted and blind participants, the model uncovers significant differences between concrete and abstract concepts. Though we also find that blind individuals show many of the same processing differences between abstract and concrete concepts found in sighted individuals, our model shows that those differences are noticeably less pronounced than in sighted individuals. We discuss our results vis-a-vis theories attempting to characterize abstract concepts.

Child-directed speech is optimized for syntax-free semantic inference

The way infants learn language is a highly complex adaptive behavior. This behavior chiefly relies on the ability to extract information from the speech they hear and combine it with information from the external environment. Most theories assume that this ability critically hinges on the recognition of at least some syntactic structure. Here, we show that child-directed speech allows for semantic inference without relying on explicit structural information. We simulate the process of semantic inference with machine learning applied to large text collections of two different types of speech, child-directed speech versus adult-directed speech. Taking the core meaning of causality as a test case, we find that in child-directed speech causal meaning can be successfully inferred from simple co-occurrences of neighboring words. By contrast, semantic inference in adult-directed speech fundamentally requires additional access to syntactic structure. These results suggest that child-directed speech is ideally shaped for a learner who has not yet mastered syntactic structure.

Cross-situational statistical learning in younger and older adults

Research investigating statistical learning, the process of tracking regularities in the environment, in older adults has been limited; with existing studies suggesting there are age-related declines. We aim to further understand older adults' statistical learning abilities using a cross-situational statistical learning paradigm in which learners map novel words to novel objects. In Experiment 1, we manipulated task difficulty and found an overall age deficit but no interaction between age and difficulty. In Experiment 2, after extended practice with a first set of object-word mappings, learners could remap a subset of previously learned words to novel objects. Based on hyper-binding, older adults might be more willing to remap previously learned words to novel objects. However, despite overall poorer learning, older adults were actually less likely to remap. Even though older adults may have an associative memory deficit, learned associations are not more weakly bound for older relative to younger adults.

Toddlers' Ability to Leverage Statistical Information to Support Word Learning

PURPOSE

This study investigated whether the ability to utilize statistical regularities from fluent speech and map potential words to meaning at 18-months predicts vocabulary at 18- and again at 24-months.

METHOD

Eighteen-month-olds (N = 47) were exposed to an artificial language with statistical regularities within the speech stream, then participated in an object-label learning task. Learning was measured using a modified looking-while-listening eye-tracking design. Parents completed vocabulary questionnaires when their child was 18-and 24-months old.

RESULTS

Ability to learn the object-label pairing for words after exposure to the artificial language predicted productive vocabulary at 24-months and amount of vocabulary change from 18- to 24 months, independent of non-verbal cognitive ability, socio-economic status (SES) and/or object-label association performance.

CONCLUSION

Eighteen-month-olds' ability to use statistical information derived from fluent speech to identify words within the stream of speech and then to map the "words" to meaning directly predicts vocabulary size at 24-months and vocabulary change from 18 to 24 months. The findings support the hypothesis that statistical word segmentation is one of the important aspects of word learning and vocabulary acquisition in toddlers.

The Influence of Different Prosodic Cues on Word Segmentation

A prerequisite for spoken language learning is segmenting continuous speech into words. Amongst many possible cues to identify word boundaries, listeners can use both transitional probabilities between syllables and various prosodic cues. However, the relative importance of these cues remains unclear, and previous experiments have not directly compared the effects of contrasting multiple prosodic cues. We used artificial language learning experiments, where native German speaking participants extracted meaningless trisyllabic "words" from a continuous speech stream, to evaluate these factors. We compared a baseline condition (statistical cues only) to five test conditions, in which word-final syllables were either (a) followed by a pause, (b) lengthened, (c) shortened, (d) changed to a lower pitch, or (e) changed to a higher pitch. To evaluate robustness and generality we used three tasks varying in difficulty. Overall, pauses and final lengthening were perceived as converging with the statistical cues and facilitated speech segmentation, with pauses helping most. Final-syllable shortening hindered baseline speech segmentation, indicating that when cues conflict, prosodic cues can override statistical cues. Surprisingly, pitch cues had little effect, suggesting that duration may be more relevant for speech segmentation than pitch in our study context. We discuss our findings with regard to the contribution to speech segmentation of language-universal boundary cues vs. language-specific stress patterns.

How statistical learning interacts with the socioeconomic environment to shape children's language development

Language is acquired in part through statistical learning abilities that encode environmental regularities. Language development is also heavily influenced by social environmental factors such as socioeconomic status. However, it is unknown to what extent statistical learning interacts with SES to affect language outcomes. We measured event-related potentials in 26 children aged 8–12 while they performed a visual statistical learning task. Regression analyses indicated that children’s learning performance moderated the relationship between socioeconomic status and both syntactic and vocabulary language comprehension scores. For children demonstrating high learning, socioeconomic status had a weaker effect on language compared to children showing low learning. These results suggest that high statistical learning ability can provide a buffer against the disadvantages associated with being raised in a lower socioeconomic status household.

Statistically defined visual chunks engage object-based attention

Although objects are the fundamental units of our representation interpreting the environment around us, it is still not clear how we handle and organize the incoming sensory information to form object representations. By utilizing previously well-documented advantages of within-object over across-object information processing, here we test whether learning involuntarily consistent visual statistical properties of stimuli that are free of any traditional segmentation cues might be sufficient to create object-like behavioral effects. Using a visual statistical learning paradigm and measuring efficiency of 3-AFC search and object-based attention, we find that statistically defined and implicitly learned visual chunks bias observers' behavior in subsequent search tasks the same way as objects defined by visual boundaries do. These results suggest that learning consistent statistical contingencies based on the sensory input contributes to the emergence of object representations.

Non-Māori-speaking New Zealanders have a Māori proto-lexicon

We investigate implicit vocabulary learning by adults who are exposed to a language in their ambient environment. Most New Zealanders do not speak Māori, yet are exposed to it throughout their lifetime. We show that this exposure leads to a large proto-lexicon - implicit knowledge of the existence of words and sub-word units without any associated meaning. Despite not explicitly knowing many Māori words, non-Māori-speaking New Zealanders are able to access this proto-lexicon to distinguish Māori words from Māori-like nonwords. What's more, they are able to generalize over the proto-lexicon to generate sophisticated phonotactic knowledge, which lets them evaluate the well-formedness of Māori-like nonwords just as well as fluent Māori speakers.

Out of the blue: understanding abrupt and wayward transitions in thought using probability and predictive processing

Thoughts that appear to come to us ‘out of the blue’ or ‘out of nowhere’ are a familiar aspect of mental experience. Such thoughts tend to elicit feelings of surprise and spontaneity. Although we are beginning to understand the neural processes that underlie the arising of such thoughts, little is known about what accounts for their peculiar phenomenology. Here, we focus on one central aspect of this phenomenology—the experience of surprise at their occurrence, as it relates to internal probabilistic predictions regarding mental states. We introduce a distinction between two phenomenologically different types of transitions in thought content: (i) abrupt transitions, which occur at surprising times but lead to unsurprising thought content, and (ii) wayward transitions, which occur at surprising times and also lead to surprising thought content. We examine these two types of transitions using a novel approach that combines probabilistic and predictive processing concepts and principles. We employ two different probability metrics—transition and occurrence probability—to characterize and differentiate between abrupt and wayward transitions. We close by discussing some potentially beneficial ways in which these two kinds of transitions in thought content may contribute to mental function, and how they may be implemented at the neural level.

This article is part of the theme issue ‘Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation’.

Adjacent and Non-Adjacent Word Contexts Both Predict Age of Acquisition of English Words: A Distributional Corpus Analysis of Child-Directed Speech

Children show a remarkable degree of consistency in learning some words earlier than others. What patterns of word usage predict variations among words in age of acquisition? We use distributional analysis of a naturalistic corpus of child-directed speech to create quantitative features representing natural variability in word contexts. We evaluate two sets of features: One set is generated from the distribution of words into frames defined by the two adjacent words. These features primarily encode syntactic aspects of word usage. The other set is generated from non-adjacent co-occurrences between words. These features encode complementary thematic aspects of word usage. Regression models using these distributional features to predict age of acquisition of 656 early-acquired English words indicate that both types of features improve predictions over simpler models based on frequency and appearance in salient or simple utterance contexts. Syntactic features were stronger predictors of children's production than comprehension, whereas thematic features were stronger predictors of comprehension. Overall, earlier acquisition was predicted by features representing frames that select for nouns and verbs, and by thematic content related to food and face-to-face play topics; later acquisition was predicted by features representing frames that select for pronouns and question words, and by content related to narratives and object play.

PPM-Decay: A computational model of auditory prediction with memory decay

Statistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from training sequences. However, PPM has limitations as a cognitive model: in particular, it has a perfect memory that weights all historic observations equally, which is inconsistent with memory capacity constraints and recency effects observed in human cognition. We address these limitations with PPM-Decay, a new variant of PPM that introduces a customizable memory decay kernel. In three studies-one with artificially generated sequences, one with chord sequences from Western music, and one with new behavioral data from an auditory pattern detection experiment-we show how this decay kernel improves the model's predictive performance for sequences whose underlying statistics change over time, and enables the model to capture effects of memory constraints on auditory pattern detection. The resulting model is available in our new open-source R package, ppm (https://github.com/pmcharrison/ppm).

Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts

Statistical-learning (SL) theory offers an experience-based account of typical and atypical spoken and written language acquisition. Recent work has provided initial support for this view, tying individual differences in SL abilities to linguistic skills, including language impairments. In the current article, we provide a critical review of studies testing SL abilities in participants with and without developmental dyslexia and specific language impairment and discuss the directions that this field of research has taken so far. We identify substantial vagueness in the demarcation lines between different theoretical constructs (e.g., “statistical learning,” “implicit learning,” and “procedural learning”) as well as in the mappings between experimental tasks and these theoretical constructs. Moreover, we argue that current studies are not designed to contrast different theoretical approaches but rather test singular confirmatory predictions without including control tasks showing normal performance. We end by providing concrete suggestions for how to advance research on SL deficits in language impairments.

A Sound Explanation for Motor Cortex Engagement during Action Word Comprehension

Comprehending action words often engages similar brain regions to those involved in perceiving and executing actions. This finding has been interpreted as support for grounding of conceptual processing in motor representations or that conceptual processing involves motor simulation. However, such demonstrations cannot confirm the nature of the mechanism(s) responsible, as word comprehension involves multiple processes (e.g., lexical, semantic, morphological, phonological). In this study, we tested whether this motor cortex engagement instead reflects processing of statistical regularities in sublexical phonological features. Specifically, we measured brain activity in healthy participants using functional magnetic resonance imaging while they performed an auditory lexical decision paradigm involving monosyllabic action words associated with specific effectors (face, arm, and leg). We show that nonwords matched to the action words in terms of their phonotactic probability elicit common patterns of activation. In addition, we show that a measure of the action words' phonological typicality, the extent to which a word's phonology is typical of other words in the grammatical category to which it belongs (i.e., more or less verb-like), is responsible for their activating a significant portion of primary and premotor cortices. These results indicate motor cortex engagement during action word comprehension is more likely to reflect processing of statistical regularities in sublexical phonological features than conceptual processing. We discuss the implications for current neurobiological models of language, all of which implicitly or explicitly assume that the relationship between the sound of a word and its meaning is arbitrary.