Disruption of Broca's Area Alters Higher-order Chunking Processing during Perceptual Sequence Learning

C-SMB 2.0: Integrating over 25 years of motor sequencing research with the Discrete Sequence Production task

An exhaustive review is reported of over 25 years of research with the Discrete Sequence Production (DSP) task as reported in well over 100 articles. In line with the increasing call for theory development, this culminates into proposing the second version of the Cognitive framework of Sequential Motor Behavior (C-SMB 2.0), which brings together known models from cognitive psychology, cognitive neuroscience, and motor learning. This processing framework accounts for the many different behavioral results obtained with the DSP task and unveils important properties of the cognitive system. C-SMB 2.0 assumes that a versatile central processor (CP) develops multimodal, central-symbolic representations of short motor segments by repeatedly storing the elements of these segments in short-term memory (STM). Independently, the repeated processing by modality-specific perceptual and motor processors (PPs and MPs) and by the CP when executing sequences gradually associates successively used representations at each processing level. The high dependency of these representations on active context information allows for the rapid serial activation of the sequence elements as well as for the executive control of tasks as a whole. Speculations are eventually offered as to how the various cognitive processes could plausibly find their neural underpinnings within the intricate networks of the brain.

Dual-functional network regulation underlies the central executive system in working memory

The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON→FPN, CON→DMN, CON→visual areas, FPN→visual areas, and phonological areas→FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON→FPN, CON→DMN, visual areas→CON, and visual areas→FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.

Dissociating Language and Thought in Human Reasoning

What is the relationship between language and complex thought? In the context of deductive reasoning there are two main views. Under the first, which we label here the language-centric view, language is central to the syntax-like combinatorial operations of complex reasoning. Under the second, which we label here the language-independent view, these operations are dissociable from the mechanisms of natural language. We applied continuous theta burst stimulation (cTBS), a form of noninvasive neuromodulation, to healthy adult participants to transiently inhibit a subregion of Broca's area (left BA44) associated in prior work with parsing the syntactic relations of natural language. We similarly inhibited a subregion of dorsomedial frontal cortex (left medial BA8) which has been associated with core features of logical reasoning. There was a significant interaction between task and stimulation site. Post hoc tests revealed that performance on a linguistic reasoning task, but not deductive reasoning task, was significantly impaired after inhibition of left BA44, and performance on a deductive reasoning task, but not linguistic reasoning task, was decreased after inhibition of left medial BA8 (however not significantly). Subsequent linear contrasts supported this pattern. These novel results suggest that deductive reasoning may be dissociable from linguistic processes in the adult human brain, consistent with the language-independent view.

Resting state functional connectivity underlying musical creativity

While the behavior of "being musically creative"- improvising, composing, songwriting, etc.-is undoubtedly a complex and highly variable one, recent neuroscientific investigation has offered significant insight into the neural underpinnings of many of the creative processes contributing to such behavior. A previous study from our research group (Bashwiner et al., 2016), which examined two aspects of brain structure as a function of creative musical experience, found significantly increased cortical surface area or subcortical volume in regions of the default-mode network, a motor planning network, and a "limbic" network. The present study sought to determine how these regions coordinate with one another and with other regions of the brain in a large number of participants (n ​= ​218) during a task-neutral period, i.e., during the "resting state." Deriving from the previous study's results a set of eleven regions of interest (ROIs), the present study analyzed the resting-state functional connectivity (RSFC) from each of these seed regions as a function of creative musical experience (assessed via our Musical Creativity Questionnaire). Of the eleven ROIs investigated, nine showed significant correlations with a total of 22 clusters throughout the brain, the most significant being located in bilateral cerebellum, right inferior frontal gyrus, midline thalamus (particularly the mediodorsal nucleus), and medial premotor regions. These results support prior reports (by ourselves and others) implicating regions of the default-mode, executive, and motor-planning networks in musical creativity, while additionally-and somewhat unanticipatedly-including a potentially much larger role for the salience network than has been previously reported in studies of musical creativity.

A common probabilistic framework for perceptual and statistical learning

System-level learning of sensory information is traditionally divided into two domains: perceptual learning that focuses on acquiring knowledge suitable for fine discrimination between similar sensory inputs, and statistical learning that explores the mechanisms that develop complex representations of unfamiliar sensory experiences. The two domains have been typically treated in complete separation both in terms of the underlying computational mechanisms and the brain areas and processes implementing those computations. However, a number of recent findings in both domains call in question this strict separation. We interpret classical and more recent results in the general framework of probabilistic computation, provide a unifying view of how various aspects of the two domains are interlinked, and suggest how the probabilistic approach can also alleviate the problem of dealing with widely different types of neural correlates of learning. Finally, we outline several directions along which our proposed approach fosters new types of experiments that can promote investigations of natural learning in humans and other species.

Language as a Tool: Motor Proficiency Using a Tool Predicts Individual Linguistic Abilities

Different disciplines converge to trace language evolution from motor skills. The human ability to use tools has been advocated as a fundamental step toward the emergence of linguistic processes in the brain. Neuropsychological and neuroimaging research has established that linguistic functions and tool-use are mediated by partially overlapping brain networks. Yet, scholars still theoretically debate whether the relationship between tool-use and language is contingent or functionally relevant, since empirical evidence is critically missing. Here, we measured both linguistic production and tool-use abilities in the same participants, as well as manual and linguistic motor skills. A path analysis ruling out unspecific contributions from manual or linguistic motor skills, showed that motor proficiency using a tool lawfully predicts differences in individual linguistic production. In addition, more complex tool-use reveals stronger association between linguistic production and tool mastery. These findings establish the existence of shared cognitive processes between tool-use and language.

The Role of Statistical Learning in Understanding and Treating Spoken Language Outcomes in Deaf Children With Cochlear Implants

Purpose

Statistical learning-the ability to learn patterns in environmental input-is increasingly recognized as a foundational mechanism necessary for the successful acquisition of spoken language. Spoken language is a complex, serially presented signal that contains embedded statistical relations among linguistic units, such as phonemes, morphemes, and words, which represent the phonotactic and syntactic rules of language. In this review article, we first review recent work that demonstrates that, in typical language development, individuals who display better nonlinguistic statistical learning abilities also show better performance on different measures of language. We next review research findings that suggest that children who are deaf and use cochlear implants may have difficulties learning sequential input patterns, possibly due to auditory and/or linguistic deprivation early in development, and that the children who show better sequence learning abilities also display improved spoken language outcomes. Finally, we present recent findings suggesting that it may be possible to improve core statistical learning abilities with specialized training and interventions and that such improvements can potentially impact and facilitate the acquisition and processing of spoken language.

Method

We conducted a literature search through various online databases including PsychINFO and PubMed, as well as including relevant review articles gleaned from the reference sections of other review articles used in this review. Search terms included various combinations of the following: sequential learning, sequence learning, statistical learning, sequence processing, procedural learning, procedural memory, implicit learning, language, computerized training, working memory training, statistical learning training, deaf, deafness, hearing impairment, hearing impaired, DHH, hard of hearing, cochlear implant(s), hearing aid(s), and auditory deprivation. To keep this review concise and clear, we limited inclusion to the foundational and most recent (2005-2018) relevant studies that explicitly included research or theoretical perspectives on statistical or sequential learning. We here summarize and synthesize the most recent and relevant literature to understanding and treating language delays in children using cochlear implants through the lens of statistical learning.

Conclusions

We suggest that understanding how statistical learning contributes to spoken language development is important for understanding some of the difficulties that children who are deaf and use cochlear implants might face and argue that it may be beneficial to develop novel language interventions that focus specifically on improving core foundational statistical learning skills.

The neuroanatomy of speech sequencing at the syllable level

Correctly ordering a sequence of speech sounds is a crucial aspect of speech production. Although studies have yielded a rich body of data on the neural substrates of visuomotor sequencing and sequence learning, research on brain regions and their functions involving speech sequence production hasn’t attracted much attention until recently. Previous functional MRI studies manipulating the complexity of sequences at the phonemic, syllabic, and suprasyllabic levels have revealed a network of motor-related cortical and sub-cortical speech regions. In this study, we directly compared human brain activity measured with functional MRI during processing of a sequence of syllables compared with the same syllables processed individually. Among a network of regions independently identified as being part of the sensorimotor circuits for speech production, only the left posterior inferior frontal gyrus (pars opercularis, lIFG), the supplementary motor area (SMA), and the left inferior parietal lobe (lIPL) responded more during the production of syllable sequences compared to producing the same syllables articulated one at a time.

Transcranial direct current stimulation enhances retention of a second (but not first) order conditional visuo-motor sequence

This study examined the role of the left inferior frontal gyrus in the implicit learning and retention of a 'simple' first order conditional (FOC) sequence and a relatively 'complex' second order conditional (SOC) sequence, using anodal transcranial direct current stimulation (a-tDCS). Groups of healthy adults received either a-tDCS (n = 18) over the left inferior frontal gyrus or sham/placebo (n = 18) stimulation. On separate days, participants completed a serial reaction time (SRT) task whilst receiving stimulation. On one of the days, participants were presented with a FOC sequence and in another, a SOC sequence. Both the learning and short-term retention of the sequences were measured. Results showed a-tDCS enhanced the short-term retention of the SOC sequence but not the FOC sequence. There was no effect of a-tDCS on the learning of either FOC or SOC sequences. The results provide evidence of prefrontal involvement in the retention of a motor sequence. However, its role appears to be influenced by the complexity of the sequence's structure. Additionally, the results show a-tDCS can enhance retention of an implicitly learnt motor sequence.

Interaction between Perceived Action and Music Sequences in the Left Prefrontal Area

Observing another person's piano play and listening to a melody interact with the observer's execution of piano play. This interaction is thought to occur because the execution of musical-action and the perception of both musical-action and musical-sound share a common representation in which the frontoparietal network is involved. However, it is unclear whether the perceptions of observed piano play and listened musical sound use a common neural resource. The present study used near-infrared spectroscopy to determine whether the interaction between the perception of musical-action and musical-sound sequences appear in the left prefrontal area. Measurements were obtained while participants watched videos that featured hands playing familiar melodies on a piano keyboard. Hand movements were paired with either a congruent or an incongruent melody. Two groups of participants (nine well-trained and nine less-trained) were instructed to identify the melody according to hand movements and to ignore the accompanying auditory track. Increased cortical activation was detected in the well-trained participants when hand movements were paired with incongruent melodies. Therefore, an interference effect was detected regarding the processing of action and sound sequences, indicating that musical-action sequences may be perceived with a representation that is also used for the perception of musical-sound sequences. However, in less-trained participants, such a contrast was not detected between conditions despite both groups featuring comparable key-touch reading abilities. Therefore, the current results imply that the left prefrontal area is involved in translating temporally structured sequences between domains. Additionally, expertise may be a crucial factor underlying this translation.

Non-parametric Algorithm to Isolate Chunks in Response Sequences

Chunking consists in grouping items of a sequence into small clusters, named chunks, with the assumed goal of lessening working memory load. Despite extensive research, the current methods used to detect chunks, and to identify different chunking strategies, remain discordant and difficult to implement. Here, we propose a simple and reliable method to identify chunks in a sequence and to determine their stability across blocks. This algorithm is based on a ranking method and its major novelty is that it provides concomitantly both the features of individual chunk in a given sequence, and an overall index that quantifies the chunking pattern consistency across sequences. The analysis of simulated data confirmed the validity of our method in different conditions of noise, chunk lengths and chunk numbers; moreover, we found that this algorithm was particularly efficient in the noise range observed in real data, provided that at least 4 sequence repetitions were included in each experimental block. Furthermore, we applied this algorithm to actual reaction time series gathered from 3 published experiments and were able to confirm the findings obtained in the original reports. In conclusion, this novel algorithm is easy to implement, is robust to outliers and provides concurrent and reliable estimation of chunk position and chunking dynamics, making it useful to study both sequence-specific and general chunking effects. The algorithm is available at: https://github.com/artipago/Non-parametric-algorithm-to-isolate-chunks-in-response-sequences.

Chunking improves symbolic sequence processing and relies on working memory gating mechanisms

Chunking, namely the grouping of sequence elements in clusters, is ubiquitous during sequence processing, but its impact on performance remains debated. Here, we found that participants who adopted a consistent chunking strategy during symbolic sequence learning showed a greater improvement of their performance and a larger decrease in cognitive workload over time. Stronger reliance on chunking was also associated with higher scores in a WM updating task, suggesting the contribution of WM gating mechanisms to sequence chunking. Altogether, these results indicate that chunking is a cost-saving strategy that enhances effectiveness of symbolic sequence learning.