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Cloud computing and storage processing is a big service for maintaining a large number of data in centralized servers to store and retrieve data depending on use to pay as a service mode. Due to increasing storage depending on duplicate copy presence during different sceneries, the size increase leads to cost increase. To resolve this problem, efficient Deduplication techniques are implemented to reduce storage. Redundant Sparse Look-up Indexing (RSLI) based file Deduplication using Semantic Cluster Content Chunking (SCCC) for optimizing cloud storage. To optimize the cloud storage contiguous fragmentation was applied to reduce the storage blocks. Initially, preprocessing can be used to track content availability by checking file name, size, and type patterns using file space optimization techniques. Also, the file can be split into hash-indexed chunks by implementing predefined methods based on these. The content union can also be compared to other files through Sparse Lookup Content Source Duplication (SLCSD). Their use helps to identify the number of overlaps between files in different transformations. Distance Vector Weightage Correlation (DWWC) Document Similarity Weights Based on Presence Counts help to include and cluster related documents. Finally, the RCB compares the document based on the coefficient match case contents present in the cluster using the similarity weight to find the duplicate content. The simulated results demonstrated high performance in terms of precision, recall, storage efficiency, and time complexity when utilizing content file types. The proposed method enhances de-duplication performance by efficiently locating duplicate files, improving indexing, and reducing storage space in the cloud environment. The proposed method results are precision is 96.9%, recall is 97.8%, the false rate is 2.6% and storage redundant is 36.9% than other methods. [ABSTRACT FROM AUTHOR]

Spatial memory is important for supporting the successful completion of everyday activities and is a particularly vulnerable domain in late life. Grouping items together in memory, or chunking, can improve spatial memory performance. In memory for desktop scale spaces and well-learned large-scale environments, error patterns suggest that information is chunked in memory. However, the chunking mechanisms involved in learning new large-scale, navigable environments are poorly understood. In five experiments, two of which included young and older adult samples, participants watched movies depicting routes through building-sized environments while attempting to remember the locations of cued objects. We tested memory for the cued objects with virtual pointing, distance estimation, and map drawing tasks after participants viewed each route. Patterns of error failed to show consistent evidence of chunking in spatial memory across all experiments. One possibility is that chunking in spatial memory relies on visual perceptual grouping mechanisms that are not in play during encoding of large-scale spaces encountered through extended route experiences that do not afford concurrent viewing of target locations. [ABSTRACT FROM AUTHOR]

Enormous information is an accumulation of vast measures of information that contains copied things gathered from different sources. Distinguishing copied information from enormous information is a difficult issue. In this paper secure deduplication scheme has been presented for huge information stockpiling. Customers may store and transmit data effectively and efficiently due to cloud service providers. Providers of cloud services are drawn to the application of data de-duplication tools to lower storage costs and conserve bandwidth. To secure the data they share on the cloud, users of the cloud are interested in having a secure and private connection. As a result, the priory is transferred to the cloud, and the data is encrypted. Because the encryption objective conflicts with the purpose of data deduplication, this feature presents a difficult problem. The author then suggested brand-new hybrid chunking methods to boost deduplication performance in cloud storage. Content-based hybrid chunking algorithm of Two Threshold Two Divisor (TTTD) with a Dynamic Prime Coding (DPC) algorithm is suggested for reducing the deduplication data. To increase throughput, which means reducing execution time and CPU resource utilisation, an index method must be incorporated into the deduplication matching process. For chunks with higher security levels, it offers greater security protection, while for chunks with lower security levels, it trades off security for greater deduplication effectiveness. When moving data to open, semi-trusted systems like public clouds, identity management, secure data exchange, and privacy preservation become more challenging than they are in closed, trusted systems where security and privacy can be controlled more simply. Accordingly, the article proposed a cross-domain authentication technique for the verification process. Evaluations of the real-world datasets show the efficiency of the design. With other methods, the proposed hybrid Content-based TTTD-DPC analyses many performance parameters, including chunk distribution, processing time, chunking time, throughput, average chunk size, and deduplication ratio. The deduplication system's total throughput and processing speed are both enhanced by the proposed approach. In addition, the recommended methodology reduces computational cost (772 ms) in terms of hash function compared to other existing methods. [ABSTRACT FROM AUTHOR]

We examined how flexibly we plan sequences of actions when we switch between multiple action sequences. Mastering a sequential skill is assumed to involve integrating successive actions into groups known as chunks that can be efficiently planned and smoothly executed. Chunking is suggested by gains in planning efficiency for long compared to short action sequences following practice and learning associations between actions and perceptual outcomes. Less is understood about how efficiently we plan sequential chunks when we switch between multiple action sequences. Do we plan learned chunks less efficiently when we switch to a different action sequence? We examined this question by comparing the initiation and execution latencies of long versus short action sequences, performed from memory, when sequences switched or repeated across trials. Additionally, each action within the sequences generated predictable perceptual outcomes that were either spatially compatible or spatially incompatible with the action sequences. Results suggested repetition costs (instead of benefits) when performing long sequences. Repetition, as opposed to switching, prolonged initiation and increased the error rate of long compared to short sequences. We attribute these results to the flexible coordination of chunk planning and execution. Repetition may prolong advanced planning of long sequences in order to resolve conflict between multiple chunks, and switching may allow the planning of later chunks to be postponed until execution. We propose that the chunking of action sequences can both facilitate and interfere with action-switching performance. [ABSTRACT FROM AUTHOR]

People often subdivide a list into smaller pieces, called chunks. Some theories of serial recall assume memories are stored hierarchically, with all-or-none retrieval of chunks, but most mathematical models avoid hierarchical assumptions. Johnson (Journal of Verbal Learning and Verbal Behavior, 8(6), 725–731, 1969) found steep drops in errors following correct recalls (transitional-error probabilities) within putative chunks during multi-trial letter-list learning, and viewed this as evidence for all-or-none retrieval. Here we test whether all-or-none retrieval occurs in lists studied only once. In serial recall of six-word lists (Experiment 1), transitional-error probabilities were inconsistent with all-or-none retrieval, both when participants were instructed to subdivide and when temporal grouping induced subdivision. Curiously, the same analysis of previous temporally grouped nine-letter lists produced compelling evidence for all-or-none retrieval, which may result from recoding rather than the formation of chunks. In Experiment 2, participants were pre-trained on three-word chunks. For nine-word lists constructed from those trained chunks, transitional-error probabilities exhibited more pronounced evidence of all-or-none retrieval. Nearly all effects reversed with post-cued backward recall, suggesting mechanisms that play out over the course of recall rather than encoding of the list. In sum, subdivided lists do not result in hierarchical memories after a single study trial, although they may emerge in lists formed from chunks that are previously learned as such. This suggests a continuous transition from non-hierarchical subdivision of lists to all-or-none retrieval over the course of chunk formation. [ABSTRACT FROM AUTHOR]

Repetitive thoughts and motor programs including perseveration are bridge symptoms characteristic of obsessive compulsive disorder (OCD), schizophrenia and in the co-morbid overlap of these conditions. The above pathologies are sensitive to altered activation and kinetics of dopamine D 1 and D 2 receptors that differently influence sequence learning and recall. Recognizing start and stop elements of motor and cognitive behaviors has crucial importance. During chunking, frequent components of temporal strings are concatenated into single units. We extended a published computational model (Asabuki et al. 2018), where two populations of neurons are connected and simulated in a reservoir computing framework. These neural pools were adopted to represent D1 and D2 striatal neuronal populations. We investigated how specific neural and striatal circuit parameters can influence start/stop signaling and found that asymmetric intra-network connection probabilities, synaptic weights and differential time constants may contribute to signaling of start/stop elements within learned sequences. Asymmetric coupling between the striatal D 1 and D 2 neural populations was also demonstrated to be beneficial. Our modeling results predict that dynamical differences between the two dopaminergic striatal populations and the interaction between them may play complementary roles in chunk boundary signaling. Start and stop dichotomies can arise from the larger circuit dynamics as well, since neural and intra-striatal connections only partially support a clear division of labor. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), congenital heart disorders (CHDs) impact a significant proportion of babies worldwide, with prevalence rates ranging from 0.8% to 1.2%. Phonocardiography (PCG) is the leading non-invasive method for detecting congenital heart defects (CHDs), offering important information about cardiac signals i.e. S1, S2, S3, and S4 and heartbeat patterns. This research study focuses on developing a strong binary classification system for congenital heart diseases (CHDs) utilizing deep neural networks. The system will be trained on a combined dataset that includes both local and publicly available repositories. The local dataset (LD) consists of 583 signals containing both normal and aberrant PCG recordings, while the public dataset (PD) obtained from Michigan University consists of 23 PCG recordings. In order to achieve consistency and compatibility, both datasets are subjected to down-sampling, resulting in a frequency of 8 kHz. A well-engineered band-pass filter efficiently removes signals outside the 20–650 Hz range, allowing for exclusive processing of the desired frequencies. The signals are divided into segments, each lasting exactly 4 seconds. The study utilizes data augmentation techniques, specifically pitch-shifting, to boost the model’s robustness. This is accomplished by implementing a 1D convolutional neural network (CNN). The most notable outcomes are achieved in case C, exhibiting a sensitivity of 99.0%, specificity of 98.0%, F1 score of 98.56%, precision of 98.57%, and an accuracy of 98.56%. This study enhances the progress of automated techniques for detecting congenital heart disease (CHD), demonstrating the potential of deep neural networks in precision medicine for pediatric cardiology.

Integrating individual actions into coherent, organised behavioural units, a process called chunking, is a fundamental, evolutionarily conserved process that renders actions automatic. In vertebrates, evidence points to the basal ganglia – a complex network believed to be involved in action selection – as a key component of action sequence encoding, although the underlying mechanisms are only just beginning to be understood. Central pattern generators control many innate automatic behavioural sequences that form some of the most basic behaviours in an animal's repertoire, and in vertebrates, brainstem and spinal pattern generators are under the control of higher order structures such as the basal ganglia. Evidence suggests that the basal ganglia play a crucial role in the concatenation of simpler behaviours into more complex chunks, in the context of innate behavioural sequences such as chain grooming in rats, as well as sequences in which innate capabilities and learning interact such as birdsong, and sequences that are learned from scratch, such as lever press sequences in operant behaviour. It has been proposed that the role of the striatum, the largest input structure of the basal ganglia, might lie in selecting and allowing the relevant central pattern generators to gain access to the motor system in the correct order, while inhibiting other behaviours. As behaviours become more complex and flexible, the pattern generators seem to become more dependent on descending signals. Indeed, during learning, the striatum itself may adopt the functional characteristics of a higher order pattern generator, facilitated at the microcircuit level by striatal neuropeptides. [ABSTRACT FROM AUTHOR]

Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and degree of stimulation received. However, the detailed mechanisms underlying this process are unknown. This paper argues three main points of statistical learning, including 1) cognitive individuality based on "reliability" of prediction, 2) the construction of information “hierarchy” through chunking, and 3) the acquisition of “1-3Hz rhythm” that is essential for early language and music learning. We developed a Hierarchical Bayesian Statistical Learning (HBSL) model that takes into account both reliability and hierarchy, mimicking the statistical learning processes of the brain. Using this model, we conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning. By modulating the sensitivity to sound stimuli, we simulated three cognitive models with different reliability on bottom-up sensory stimuli relative to top-down prior prediction: hypo-sensitive, normal-sensitive, and hyper-sensitive models. We suggested that statistical learning plays a crucial role in the acquisition of 1-3 Hz rhythm. Moreover, a hyper-sensitive model quickly learned the sensory statistics but became fixated on their internal model, making it difficult to generate new information, whereas a hypo-sensitive model has lower learning efficiency but may be more likely to generate new information. Various individual characteristics may not necessarily confer an overall advantage over others, as there may be a trade-off between learning efficiency and the ease of generating new information. This study has the potential to shed light on the heterogeneous nature of statistical learning, as well as the paradoxical phenomenon in which individuals with certain cognitive traits that impede specific types of perceptual abilities exhibit superior performance in creative contexts.

Chunking mechanisms, the processes of grouping several items together into a single processing unit, are central to several cognitive processes in human and non-human primates and notably to the acquisition of visuomotor sequences. Individuals segment sequences into chunks to perform visuomotor tasks more fluidly, rapidly, and accurately. Using an operant conditioning device, we previously studied the precise mechanisms by which chunks are formed and reorganized during sequence learning. Eighteen Guinea baboons (Papio papio) repeatedly produced the same fixed sequence of nine movements during 1,000 trials by pointing to a moving target on a touch screen. We found that chunking patterns are reorganized during the course of learning, with chunks becoming progressively fewer and longer. We also identified two forms of reorganization of the chunking pattern: the recombination of preexisting chunks and the concatenation of two distinct chunks into a single one. To understand the conditions under which these reorganizations occur, we study here how the stability of a chunk and the stability of chunk boundaries are related to these reorganizations. Our analyses showed that less stable chunks and less stable boundaries are more likely to produce reorganizations. These results provide new evidence about the fine-grained dynamics of chunking mechanisms during sequence learning.

Bouldering is an Olympic climbing discipline that encompasses short climbing sequences, referred to as boulders, set up on low-height bouldering walls. Memory plays a critical role in bouldering, as it allows climbers to develop climbing strategies, to mentally rehearse climbing movements, and to recall climbing holds of boulders. This study extends previous research on memory in climbing and bouldering with the purpose to elucidate potential mechanisms underlying superior memory abilities of skilled climbers. Sixty climbers with intermediate (n = 20), advanced (n = 20), or elite (n = 20) skill levels were tasked to memorise the climbing holds and movements of a boulder, set up on a spray wall and demonstrated by a bouldering expert. Findings revealed a positive relation between the participants' bouldering skills and sport-specific movement knowledge and both, the number of climbing holds and movements they were able to memorise following a two-minute rehearsal period. Consistent with previous research, bouldering expertise is positively associated with the ability to memorise domain-specific information. Superior memory abilities among skilled climbers appear to be associated with climbing-specific movement knowledge, coupled with better mental visualisation and increased attentional focus towards functional aspects of boulders. [ABSTRACT FROM AUTHOR]

Previous work on individual differences has revealed limitations in the ability of existing measures (e.g., working memory) to predict language processing. Recent evidence suggests that an individual's sensitivity to detect the statistical regularities present in language (i.e., "chunk sensitivity") may significantly modulate online sentence processing. We investigated whether individual chunk sensitivity predicted the online processing of gender cues, a core linguistic feature of Spanish. In a self‐paced reading task, we examined native speakers' processing of relative clauses in which gender cues were variably exploited to induce processing costs. Even after considering the effect of working memory and cognitive control, the results revealed a significant effect of chunking ability in modulating online sentence processing. Critically, higher‐chunking ability speakers' reading times showed online sensitivity to core linguistic cues online; while low‐chunking ability readers showed no sensitivity to manipulations, indicating shallow real‐time processing of their native language. [ABSTRACT FROM AUTHOR]

We describe in this paper the adopted approach combining a phrase structure grammar and dependency rules to develop AlkhalilPArser. The general architecture of this parser is composed of three main levels. The first level includes basic tasks such as tokenization, part-of-speech tagging, and chunking. The next level deals with analysis tasks such as managing common, internal and external dependencies according to the nature of the proposal. The last level detects and corrects anomalies. This parser is an extension to verbal sentences of a system previously designed for parsing the nominal sentences. Several modifications have been made to adapt this system to all types of sentences and improve its accuracy. The tests carried out on a representative corpus and the comparisons with other parsers testify to the robustness of our system. [ABSTRACT FROM AUTHOR]

Word segmentation is a crucial step in children's vocabulary learning. While computational models of word segmentation can capture infants' performance in small‐scale artificial tasks, the examination of early word segmentation in naturalistic settings has been limited by the lack of measures that can relate models' performance to developmental data. Here, we extended CLASSIC (Chunking Lexical and Sublexical Sequences in Children; Jones et al., 2021), a corpus‐trained chunking model that can simulate several memory and phonological and vocabulary learning phenomena to allow it to perform word segmentation using utterance boundary information, and we have named this extended version CLASSIC utterance boundary (CLASSIC‐UB). Further, we compared our model to the performance of children on a wide range of new measures, capitalizing on the link between word segmentation and vocabulary learning abilities. We showed that the combination of chunking and utterance‐boundary information used by CLASSIC utterance boundary allowed a better prediction of English‐learning children's output vocabulary than did other models. A one‐page Accessible Summary of this article in non‐technical language is freely available in the Supporting Information online and at https://oasis‐database.org [ABSTRACT FROM AUTHOR]

The TBRS*C computational model provides a mathematical implementation of the cognitive processes involved in complex span tasks. The logic of the core processes, i.e., encoding, refreshing/time-based decay, and chunking, is based on Hebbian learning, synaptic facilitation, and long-term neural plasticity, respectively. The modeling, however, takes place on a cognitive rather than a physiological level. Chunking is implemented as a process of searching for sequences of memoranda in long-term memory and recoding them as a single unit which increases the efficacy of memory maintenance. Using TBRS*C simulations, the present study investigated how chunking and central working memory processes change with expertise. Hobby musicians and music students completed a complex span task in which sequences of twelve note symbols were presented for serial recall of pitch. After the presentation of each memorandum, participants performed an unknown, notated melody on an electric piano. To manipulate the potential for chunking, we varied whether sequences of memoranda formed meaningful tonal structures (major triads) or arbitrary trichords. Hobby musicians and music students were each split up in a higher-expertise and a lower-expertise group and TBRS*C simulations were performed for each group individually. In the simulations, higher-expertise hobby musicians encoded memoranda more rapidly, invested less time in chunk search, and recognized chunks with a higher chance than lower-expertise hobby musicians. Parameter estimates for music students showed only marginal expertise differences. We conclude that expertise in the TBRS model can be conceptualized by a rapid access to long-term memory and by chunking, which leads to an increase in the opportunity and efficacy of refreshing. [ABSTRACT FROM AUTHOR]

There have been some suggestions in linguistics and cognitive science that humans process continuous speech by routinely chunking it up into smaller units. The nature of the process is open to debate, which is complicated by the apparent existence of two entirely different chunking processes, both of which seem to be warranted by the limitations of working memory. To overcome them, humans seem to both combine items into larger units for future retrieval (usage-based chunking), and partition incoming streams into temporal groups (perceptual chunking). To determine linguistic properties and cognitive constraints of perceptual chunking, most previous research has employed short-constructed stimuli modeled on written language. In contrast, we presented linguistically naïve listeners with excerpts of natural speech from corpora and collected their intuitive perceptions of chunk boundaries. We then used mixed-effects logistic regression models to find out to what extent pauses, prosody, syntax, chunk duration, and surprisal predict chunk boundary perception. The results showed that all cues were important, suggesting cue degeneracy, but with substantial variation across listeners and speech excerpts. Chunk duration had a strong effect, supporting the cognitive constraint hypothesis. The direction of the surprisal effect supported the distinction between perceptual and usage-based chunking. [ABSTRACT FROM AUTHOR]

Chunking is an important cognitive process allowing the compression of information in short-term memory. The aim of this study is to compare the dynamics of chunking during the learning of a visuomotor sequence in humans (Homo sapiens) and Guinea baboons (Papio papio). We duplicated in humans an experimental paradigm that has been used previously in baboons. On each trial, human participants had to point to a moving target on a touch screen. The experiment involved the repetition of the same sequence of nine items over a 1,000 trials. To reproduce as much as possible the conditions under which baboons performed the task, human participants were tested at their own pace. Results revealed that baboons and humans shared similar chunking dynamics: In both species, the sequence was initially parsed into small chunks that became longer and fewer with practice through two reorganization mechanisms (recombinations and concatenations). Differences were also observed regarding the global decrease in response times that was faster and more pronounced in humans compared with baboons. Analyses of these similarities and differences provide new empirical evidence for understanding the general properties of chunking mechanisms in sequence learning and its evolution across species. [ABSTRACT FROM AUTHOR]

Cascade support vector machine (CSVM) divides the dataset into groups and trains them in parallel, greatly reducing training time and memory usage. However, the accuracy of the model obtained using this method has certain errors compared to direct training. In order to reduce the error, the reasons for the error caused by grouping training are analyzed, and the ideal grouping without error is summarized. A balanced cascade support vector machine (BCSVM) algorithm is proposed. The algorithm balances the sample proportions in the sub-datasets after grouping, ensuring that the sample proportions in the sub-datasets are the same as those in the original dataset. It adjusts the parameter values during grouping training to obtain more support vectors, thereby reducing the possibility of global support vector loss. At the same time, researchers discussed the effectiveness of BCSVM algorithm and demonstrated that models obtained using this algorithm have better performance in prediction accuracy than those obtained using random grouping CSVM. Finally, multiple common datasets are used for experimental verification, and the results show that the accuracy error obtained by training using the BCSVM algorithm is reduced from 1 % to about 0.1%, i. e., by one order of magnitude. [ABSTRACT FROM AUTHOR]

Auditory memory is an important everyday skill evaluated more and more frequently in clinical settings as there is recently a greater recognition of the cost of hearing loss to cognitive systems. Testing often involves reading a list of unrelated items aloud; but prosodic variations in pitch and timing across the list can affect the number of items remembered. Here, we ran a series of online studies on normally-hearing participants to provide normative data (with a larger and more diverse population than the typical student sample) on a novel protocol characterizing the effects of suprasegmental properties in speech, namely investigating pitch patterns, fast and slow pacing, and interactions between pitch and time grouping. In addition to free recall, and in line with our desire to work eventually with individuals exhibiting more limited cognitive capacity, we included a cued recall task to help participants recover specifically the words forgotten during the free recall part. We replicated key findings from previous research, demonstrating the benefits of slower pacing and of grouping on free recall. However, only slower pacing led to better performance on cued recall, indicating that grouping effects may decay surprisingly fast (over a matter of one minute) compared to the effect of slowed pacing. These results provide a benchmark for future comparisons of short-term recall performance in hearing-impaired listeners and users of cochlear implants. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

Neural oscillations are thought to support speech and language processing. They may not only inherit acoustic rhythms, but might also impose endogenous rhythms onto processing. In support of this, we here report that human (both male and female) eye movements during naturalistic reading exhibit rhythmic patterns that show frequency-selective coherence with the EEG, in the absence of any stimulation rhythm. Periodicity was observed in two distinct frequency bands: First, wordlocked saccades at 4-5 Hz display coherence with whole-head theta-band activity. Second, fixation durations fluctuate rhythmically at ~1 Hz, in coherence with occipital delta-band activity. This latter effect was additionally phase-locked to sentence endings, suggesting a relationship with the formation of multi-word chunks. Together, eye movements during reading contain rhythmic patterns that occur in synchrony with oscillatory brain activity. This suggests that linguistic processing imposes preferred processing time scales onto reading, largely independent of actual physical rhythms in the stimulus. [ABSTRACT FROM AUTHOR]

A fundamental issue in cognitive science concerns the mentalprocesses that underlie the formation and retrieval of conceptsin the short-term and long-term memory (STM and LTMrespectively). This study advances Chunking Theory and itscomputational embodiment CHREST to propose a singlemodel that accounts for significant aspects of conceptformation in the domains of literature and music. The proposedmodel inherits CHREST’s architecture with its integratedSTM/LTM stores, while also adding a moving attentionwindow and an “LTM chunk activation” mechanism. Theseadditions address the overly destructive nature of primacyeffect in discrimination network based architectures andexpand Chunking Theory to account for learning, retrieval andcategorisation of complex sequential symbolic patterns – likereal-life text and written music scores. The model was trainedthrough exposure to labelled stimuli and learned to categoriseclassical poets/writers and composers. The model categorisedpreviously unseen literature pieces by Homer, Chaucer,Shakespeare, Walter Scott, Dickens and Joyce, as well asunseen sheet music scores by Bach, Mozart, Beethoven andChopin. These findings offer further support to mechanismsproposed by Chunking Theory and expand it into thepsychology of music.

Studies have shown that temporal chunk measures in transcrip-tion tasks can be used to assess competence in various domains.However, in other tasks, chunking strategies and thus perfor-mance differences can be highly variable across participants.If such individual differences are also large in transcriptiontasks this would undermine the use of chunk-based competencemeasures. Using four stimuli with fixed spatial structures thisexperiment demonstrates that there is good consistency inchunking strategy across 52 participants in two types of tran-scription tasks. The experiment spans 16,000 data points.

Gestures in learners of second languages (L2) are still an understudied field. Most studies comparing the same individuals gesturing in their L2 and in the mother tongue (L1) report a higher frequency of gestures in the L2, however, the reason for this difference is not yet clear. Aside from idiosyncratic and cultural variations, differences in language type, such as syllabic duration and functional load and syntactical structure, as well as differences in the proficiency of the speaker are likely to affect the production of gestures in terms of their frequency and function. The many variables to be considered obscure the role of proficiency and the integration of gesture-speech-idea in L2 speakers. These variables are only controlled in a handful of longitudinal studies comparing speakers' gestures at two or more points in the proficiency continuum. With this paper, we seek to add our observations to this corpus, using the data collected under the case study of a Cantonese learner of English, to reflect on a number of existing theoretical constructs. We propose a number of ideas that need further research, as well as a number of variables that need to be better understood in order to advance L2 gesture research. As our observations are based on the narrations of one individual, our generalizations are theoretical at this point as our results cannot to be extrapolated to all L2 learners, instead, they are intended to highlight a number of areas related to L2 gesture production and how this might change with proficiency, that require further study. [ABSTRACT FROM AUTHOR]

Does expertise mostly stem from pattern recognition or look-ahead search? van Opheusden et al. contribute to this important debate in cognitive psychology and artificial intelligence (AI) with a multi-method, multi-experiment study and a new model. Using a novel, relatively simple board game, they show that players increase depth of search when improving their skill. [ABSTRACT FROM AUTHOR]

In a simple transcription task in which sections of Java programcode are copied by freehand writing, it is demonstrated thatchunk related temporal signals are sufficiently robust to permitthe measurement of programming competence. An experimentwith 24 participants revealed that the number of views of thestimulus per trial and the duration of writing per stimulus vieware both strongly correlated with independent measures of Javacompetence.

A central question for cognitive science is whether children’slinguistic productivity can be captured by item-basedlearning, or whether the learner must be guided by abstract,system-wide principles governed by innate constraints. Here,we present a computational model of early languageacquisition which learns to discover and use lexically-basedframes in a fully incremental, on-line fashion. The model isrooted in simple prediction- and recognition-based processes,subject to the same memory limitations as language learners.When exposed to English corpora of child-directed speech,the model is able learn developmentally plausible frames anduse them to capture over 70% of the utterances produced bytarget children aged 2 to 5. Across a typologically diverserange of 29 languages, the model is able to capture over 68%of child utterances. Together, these findings suggest thatmuch of children’s early linguistic productivity can becaptured by item-based learning through computationallysimple mechanisms.

Subject-auxiliary inversion in interrogatives has been a topicof great interest in language acquisition research, and hasoften been held up as evidence for the structure-dependenceof grammar. Usage-based and nativist approaches positdifferent representations and processes underlying children’squestion formation and therefore predict different causes forthese errors. Here, we explore the question of whether inputstatistics predict children’s spontaneous non-inversion errorswith wh- questions. In contrast to previous studies, we look atproperties of the non-inverted, errorful forms of questions.Through a series of corpus analyses, we show that thefrequency of uninverted subsequences (e.g., “she is going” in“what she is going to do?*”) is a good predictor of children’serrors, consistent with recent evidence for multiword units inchildren’s comprehension and production. This finding hasimplications for the types of mental representations andcognitive processes researchers ascribe to children acquiring afirst language.

Abstract Representing the base-10 structure of numbers is a challenging cognitive ability, unique to humans, but it is yet unknown how precisely this is done. Here, we examined whether and how literate adults represent a number’s full syntactic structure. In 5 experiments, participants repeated number-word sequences and we systematically varied the order of words within each sequence. Repetition on grammatical sequences (e.g., two hundred ninety-seven) was better than on non-grammatical ones (hundred seven two ninety). We conclude that the participants represented the number’s full syntactic structure and used it to merge number words into chunks in short-term memory. Accuracy monotonously improved for sequences with increasingly longer grammatical segments, up to a limit of ~ 4 words per segment, irrespectively of the number of digits, and worsened thereafter. Namely, short chunks improved memorization, whereas oversized chunks disrupted memorization. This chunk size limit suggests that the chunks are not based on predefined structures, whose size limit is not expected to be so low, but are created ad hoc by a generative process, such as the hierarchical syntactic representation hypothesized in Michael McCloskey’s number-processing model. Chunking occurred even when it disrupted performance, as in the oversized chunks, and even when external cues for chunking were controlled for or were removed. We conclude that the above generative process operates automatically rather than voluntarily. To date, this is the most detailed account of the core representation of the syntactic structure of numbers—a critical aspect of numerical literacy and of the ability to read and write numbers.

Why do we divide ('chunk') long tasks into a series of shorter subtasks? A popular view is that limits in working memory (WM) prevent us from simultaneously maintaining all task relevant information in mind. We therefore chunk the task into smaller units so that we only maintain information in WM that is relevant to the current unit. In contrast to this view, we show that long tasks that are not constrained by WM limits are nonetheless chunked into smaller units. Participants executed long sequences of standalone but demanding trials that were not linked to any WM representation and whose execution was not constrained by how much information could be simultaneously held in WM. Using signs well-known to reflect beginning of new task units, we show that such trial sequences were not executed as a single task unit but were spontaneously chunked and executed as series smaller units. We also found that sequences made of easier trials were executed as longer task units and vice-versa, further suggesting that the length of task executed as one unit may be constrained by cognitive limits other than WM. Cognitive limits are typically seen to constrain how many things can be done simultaneously e.g., how many events can be maintained in WM or attended at the same time. We show a new aspect of these limits that constrains the length of behaviour that can be executed sequentially as a single taskunit. [ABSTRACT FROM AUTHOR]

Individual differences in cognitive abilities are ubiquitous across the spectrum of proficient language users. Although speakers differ with regard to their memory capacity, ability for inhibiting distraction, and ability to shift between different processing levels, comprehension is generally successful. However, this does not mean it is identical across individuals; listeners and readers may rely on different processing strategies to exploit distributional information in the service of efficient understanding. In the following psycholinguistic reading experiment, we investigate potential sources of individual differences in the processing of co-occurring words. Participants read modifier-noun bigrams like absolute silence in a self-paced reading task. Backward transition probability (BTP) between the two lexemes was used to quantify the prominence of the bigram as a whole in comparison to the frequency of its parts. Of five individual difference measures (processing speed, verbal working memory, cognitive inhibition, global-local scope shifting, and personality), two proved to be significantly associated with the effect of BTP on reading times. Participants who could inhibit a distracting global environment in order to more efficiently retrieve a single part and those that preferred the local level in the shifting task showed greater effects of the co-occurrence probability of the parts. We conclude that some participants are more likely to retrieve bigrams via their parts and their co-occurrence statistics whereas others more readily retrieve the two words together as a single chunked unit. [ABSTRACT FROM AUTHOR]

• Studies show that active learning results in better student learning outcomes. • Scaffolding of course assignments is fully implemented following six principles. • Faculty can improve active learning as exemplified by scaffolded assignments. Recent changes, such as COVID-19 restrictions and advances in technology, have led to unique effects on nursing education, requiring new pedagogical thinking and strategies. Studies have shown that a more active approach to learning leads to positive gains for students. In this conceptual paper, we argue that scaffolding specifically allows instructors to break down assignments into meaningful chunks with instructional support that fall within reach of a student's ability, making the assignment both more manageable and actively engaging. This paper outlines six principles (Van Lier, 1996) to the scaffolding approach and discusses two example assignments that utilize scaffolding in nursing education to increase active engagement and improve learning outcomes. Examples include a research critique paper and an unfolding case study. [ABSTRACT FROM AUTHOR]

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Are visual features of real-world objects stored as bound units? Previous research has shown that simple visual features (e.g., colored squares or geometric shapes) can be effectively bound together when forming predictable pairs in memory tasks. Through a "memory compression" process, observers can take advantage of these features to compress them into a chunk. However, a recent study found that visual features in real-world objects are stored independently. In the present study, we explored this issue by using drawings of fruits as memory stimuli, presenting four pictures of fruit in separate test trials in which we required observers to remember eight total features (i.e., four colors and four shapes). In the congruent trials, the color of the fruit matched its natural appearance (e.g., a red apple), while in incongruent trials, the color of the fruit mismatched its natural appearance (e.g., a red banana). We paired the shape of the fruits randomly with a color (without replacement). According to chunking theory, if visual features of real-world objects are stored in a chunk, the highest memory capacity should be accompanied by the longest response time in congruent trials due to an extra decoding process required from the chunk. We did find that participants had the highest memory capacity in the congruent condition, but their response times in the congruent condition were significantly faster than in the incongruent condition. Thus, observers did not undergo a decoding process in the congruent condition, and we concluded that visual features in real-world objects are not stored in a chunk. [ABSTRACT FROM AUTHOR]

• Here, POS tagger and Chunker developed for Maithili, Magahi and Bhojpuri languages. • Non-continuous long dependencies among words captured by self attention. • Robust features extracted by SAHBiLC and Fine-tuned SAHBiLC architectures. • Linguistic Error analyses of proposed architectures are described. Sequential labelling plays a vital role in solving numerous Natural Language Processing (NLP) applications such as Machine Translation and Information Extraction etc. One of these is Part-of-Speech (POS) tagging, which assigns a sequence of grammatical categories to the given sentence, and Chunking which groups them into 'chunks' or what can be called minimal phrases. Bhojpuri, Maithili and Magahi are low resource languages and widely spoken in central north-eastern India, belonging to the Indo-Aryan language family. The creation of an annotated corpus for POS tagging and Chunking, and then building an initial automatic tool for these problems is the first attempt towards building language technology tools for these languages. The annotated corpus used to develop POS Taggers and Chunkers, based on various machine learning algorithms (TnT, CRF, MEMM and Structured SVM) and state-of-the-art LSTM-CNN-CRF model, and then these compared with the obtained results on two new proposed deep learning-based models, Self-Attention Hierarchical Bi-LSTM CRF (SAHBiLC) and a fine-tuned version of it, Fine-SAHBiLC. The SAHBiLC and Fine-SAHBiLC models outperform on Bhojpuri (Accuracy for POS and Chunking is 0.86% and 0.94%, respectively) and Maithili (Accuracy for POS and Chunking is 0.86% and 0.95%, respectively) and Magahi (Accuracy for POS is 0.86%). [ABSTRACT FROM AUTHOR]

Perceptual performance, anticipating opponents' strategies, and judging chess positions especially in subliminal processing is related to expertise level and dependent on chunking processes. It becomes obvious that chess expertise is a multidimensional phenomenon related predominantly to experience. Under consideration of chess expertise categorization, we conducted two priming experiments expanding existing designs by gradually increasing the target and task complexity. The main aim was the evaluation of potential visuocognitive limitations. The results reveal experts' perceptual superiority manifested by their faster reaction times in settings with increased stimulus and task complexity. Further, experts' priming effects seem to be affected by the target content and/or priming duration. For short prime duration, experts show priming effects only for less complex prime-target content. Interestingly, for longer prime duration andmore complex prime-target content, all participants reveal priming effects. In summary, we argue that experts' visuocognitive processing (i.e., detecting or anticipating potential threats to the king) is rooted in amore efficient visuocognition due to stored chunks of checking andmating constellations. We suggest that visuocognitive limitations are related also to the prime-target complexity as well as to the task. Further investigations must be conducted in order to elucidate the factors with an increased impact on chess players' performance. [ABSTRACT FROM AUTHOR]

Microlearning, in which lecture recordings are segmented into parts, saw renewed focus as a means of maintaining student engagement amid the challenging conditions of the COVID-19 pandemic. While many institutions shifted to remote provision with segmented lecture recordings, there is a lack of consensus about the length that these segments should be in order to best maintain engagement. Using a self-reported system of Likert-based diagnostics, 135 videos in use at Solent University’s computer games area were analysed. Ninety-four students were asked to agree or disagree with statements in the format ‘I understand X’, each tailored to the subject material of the video in question. Repeated questions before and after the video allowed for a change in confidence to be measured, as an indicator of engagement. The resulting 4198 responses showed an optimum range of 5–8 min overall. However, the year of study emerged as a significant factor in this regard – with an optimum range for first years at 6–12 min, and for second and third years at under 8 min. There is a need for institutional-level change in this area, as many institutions currently recommend use of lecture video segments far longer than either figure.

Two studies investigate the production and perception of speech chunks in Estonian. A corpus study examines to what degree the boundaries of syntactic constituents and frequent collocations influence the distribution of prosodic information in spontaneously spoken utterances. A perception experiment tests to what degree prosodic information, constituent structure, and collocation frequencies interact in the perception of speech chunks. Two groups of native Estonian speakers rated spontaneously spoken utterances for the presence of disjunctures, whilst listening to these utterances (N=47N=47) or reading them (N=40N=40). The results of the corpus study reveal a rather weak correspondence between the distribution of prosodic information and boundaries of the syntactic constituents and collocations. The results of the perception experiments demonstrate a strong influence of clause boundaries on the perception of prosodic discontinuities as prosodic breaks. Thus, the results indicate that there is no direct relationship between the semantico-syntactic characteristics of utterances and the distribution of prosodic information. The percept of a prosodic break relies on the rapid recognition of constituent structure, i.e. structural information.