Chunking Process Research Articles

Distinct brain network organizations between club players and novices under different difficulty levels.

Chunk memory is one of the essential cognitive functions for high-expertise (HE) player to make efficient decisions. However, it remains unknown how the neural mechanisms of chunk memory processes mediate or alter chess players' performance when facing different opponents. This study aimed at inspecting the significant brain networks associated with chunk memory, which would vary between club players and novices. Functional networks and topological features of 20 club players (HE) and 20 novice players (LE) were compared at different levels of difficulty by means of functional near-infrared spectroscopy. Behavioral performance indicated that the club player group was unaffected by differences in difficulty. Furthermore, the club player group demonstrated functional connectivity among the dorsolateral prefrontal cortex, the frontopolar cortex, the supramarginal gyrus, and the subcentral gyrus, as well as higher clustering coefficients and lower path lengths in the high-difficulty task. The club player group illustrated significant frontal-parietal functional connectivity patterns and topological characteristics, suggesting enhanced chunking processes for improved chess performance.

Chunking as a function of sequence length.

Chunking mechanisms are central to several cognitive processes. During the acquisition of visuo-motor sequences, it is commonly reported that these sequences are segmented into chunks leading to more fluid, rapid, and accurate performances. The question of a chunk's storage capacity has been often investigated but little is known about the dynamics of chunk size evolution relative to sequence length. In two experiments, we studied the dynamics and the evolution of a sequence's chunking pattern as a function of sequence length in a non-human primate species (Guinea baboons, Papio papio). Using an operant conditioning device, baboons had to point on a touch screen to a moving target. In Experiment 1, they had to produce repeatedly the same sequence of 4 movements during 2000 trials. In Experiment 2, the sequence was composed of 5 movements and was repeated 4000 times. For both lengths, baboons initially produced small chunks that became fewer and longer with practice. Moreover, the dynamics and the evolution of the chunking pattern varied as a function of sequence length. Finally, with extended practice (i.e., more than 2000 trials), we observed that the mean chunk size reached a plateau indicating that there are fundamental limits to chunking processes that also depend on sequence length. These data therefore provide new empirical evidence for understanding the general properties of chunking mechanisms in sequence learning.

L2 Pragmatic Competence in Chinese EFL Routines. Yuqi Wang (2023) Singapore, Springer, 144 pages, ISBN 978-981-19-6351-3

Pragmatic routines have long been considered essential tools for second/foreign language (L2) learners’ pragmatic competence and language use (Taguchi & Roever, 2017). However, the current literature is largely confined to the recognition, comprehension, and production of formulaic routines without considering the role of task modality in learners’ cognitive processing of such chunks (Roever, 2012). To fill this gap, in this monograph entitled “L2 Pragmatic Competence in Chinese EFL Routines”, Wang takes a cross-sectional approach to study L2 pragmatic routines of Chinese learners in light of pragmatic multimodality and the socio-cognitive perspective. In doing so, the author scrutinizes the mediating role of proficiency and study abroad experience in different aspects of pragmatic routines’ competence. This resource expands the scope of research on L2 pragmatic routines by integrating multidimensional pragmatic modalities into the study of routines as core constructs of pragmatic competence. It helps English as a foreign language (EFL) learners, teachers, researchers, and teacher trainers to actively focus on the promotion of L2 pragmatic routines. Moreover, this monograph presents some theories and practices that EFL educators may draw on to increase learners’ reception and production of routines in EFL contexts.

Expertise-dependent visuocognitive performance of chess players in mating tasks: evidence from eye movements during task processing.

Visuocognitive performance is closely related to expertise in chess and has been scrutinized by several investigations in the last decades. The results indicate that experts' decision-making benefits from the chunking process, perception and visual strategies. Despite numerous studies which link these concepts, most of these investigations have employed common research designs that do not use real chess play, but create artificial laboratory conditions via screen-based chess stimuli and obtrusive stationary eye tracking with or without capturing of decision-making or virtual reality settings. The present study assessed the visuocognitive performance of chess novices, intermediates and experts in a real chess setting. Instead of check detection, find-the-best-move tasks or to distinguish between regions of a chessboard that were relevant or irrelevant to the best move in previous studies, we introduced n-mate tasks and sequentially manipulated their difficulty. Due to the complexity of the tasks, we monitored players' visual strategies in a fine-graded initial phase (different time intervals instead of analysing a fixed number of first fixations) of task-solving and for complete trials, employing non-obtrusive mobile eye tracking, multi-sensor observation and full-automatic annotation of decision-making. The results revealed significant expertise-dependent differences in visuocognitive performance based on a circumstantial spatial and temporal analysis. In order to provide more detailed results, for the first time the analyses were performed under the special consideration of different time intervals and spatial scalings. In summary, experts showed a significantly higher number of fixations on areas of interest and empty squares between pieces in the task processing than less-skilled players. However, they had a strikingly low total number of fixations on the whole board and in complete trials. As a conclusion, experts apply different visual search strategies in problem-solving. Moreover, experts' visuocognitive processing benefits from stored chunks of mating constellations.

The next station: chunking of değİl ‘not’ collocations in Turkish Sign Language

Abstract More recently, grammaticalization theorists have become increasingly aware of the role of collocations in grammatical development. One of these roles is to define phonetic reductions and fusion in frequent collocations as constructionalization. Based on frequency of occurrences, the present study explores the implications of high-frequency collocations in Turkish Sign Language for grammaticalization and offers a novel account of constructional change of değİl ‘not’ on usage-based grounds. Specifically, the study suggests that (i) the chunking process is not language-specific within the spoken modality, as noted previously in the literature, (ii) the frequency of collocations is strongly correlated with phonetic reduction and duration, (iii) the fusional characteristics of [sign + değİl] collocations can be classified under four reduced constructional schemas, (iv) the monosyllabicity of a scheme appears to be criterion for it to be productive in signed modality and (v) the semantic changes of frequent [sign + değİl] collocations are related to the notion of subjectification in TİD.

Chunking up speech in real time: linguistic predictors and cognitive constraints

AbstractThere 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.

Chunks, pauses, and holistic processing in Mandarin spontaneous speech.

Chunks are multiword sequences with independent meaning and function, or formulaic based on the intuition of native speakers, hypothesized to be holistically restored and retrieved in the mental lexicon. Previous studies suggest that pauses and intonational boundaries tend to occur at the boundaries of chunks, but less discussion was made on the influence of chunk categories over mental processing and on pause placement associated with intonational continuity. This study adopted spontaneous monologs of Mandarin natives in formal and informal settings. It examined the co-occurrence of chunks and pause-defined processing units and pause placement around chunks to explore to what extent chunks are holistically processed. The results showed that Mandarin chunks were likely to be situated within a single processing unit, indicating chunks as smaller units than processing units in spontaneous speech. Major chunk categories exhibited significantly different patterns in co-occurring with processing units, indicating the influence of chunk properties on the mental processing of chunks. In addition, chunks tended to be fluently processed in spontaneous speech production as fewer hesitations occurred before and during chunk production. Major chunk categories shared a similar threshold in encountering hesitations before chunk production and differed significantly in hesitation distribution during chunk production. Hesitations in the middle of chunks were more likely to be situated within intonation units compared to those before chunk production. Speakers' effort to maintain the intonational continuity of chunks when they encounter processing difficulties reveals the mental reality of the holistic nature of chunks. Furthermore, the co-occurrence of chunks and processing units differed significantly between the formal and informal speech genres, indicating genre influence on the mental processing of chunks. Altogether, the findings of this study have provided implications for theories on chunks and the syntactic-prosody interface and contributed to implications for the design of Mandarin instructions and teaching.

Traceback and Chunk-Based Learning: Comparing Usage-Based Computational Approaches to Child Code-Mixing

Recent years have seen increased interest in code-mixing from a usage-based perspective. In usage-based approaches to monolingual language acquisition, a number of methods have been developed that allow for detecting patterns from usage data. In this paper, we evaluate two of those methods with regard to their performance when applied to code-mixing data: the traceback method, as well as the chunk-based learner model. Both methods make it possible to automatically detect patterns in speech data. In doing so, however, they place different theoretical emphases: while traceback focuses on frame-and-slot patterns, chunk-based learner focuses on chunking processes. Both methods are applied to the code-mixing of a German–English bilingual child between the ages of 2;3 and 3;11. Advantages and disadvantages of both methods will be discussed, and the results will be interpreted against the background of usage-based approaches.

What does the processing of chunks by learners of Chinese tell us? An acceptability judgment investigation

Abstract Chunks are multi-word sequences that constitute an important component of the mental lexicon. In second language (L2) acquisition, chunking is essential for attaining fluency and idiomaticity. In the present study, in order to examine whether chunks provide a processing advantage over non-chunks for L2 learners at different levels of proficiency, three groups (beginner, intermediate, and advanced) English-speaking learners of Chinese participated in an online acceptability judgment task and a familiarity rating task. Our results revealed that the participants in all three groups processed chunks faster and with fewer errors than they did non-chunks. It was also found that the observed processing advantage of chunks could not be explained by a familiarity effect alone, thus suggesting that L2 learners across the board store chunks as holistic units. The implications of chunk instruction in relation to input frequency and variability in L2 settings are also discussed.

Robust Processing Advantage for Binomial Phrases with Variant Conjunctions.

Prior research has shown that various types of conventional multiword chunks are processed faster than matched novel strings, but it is unclear whether this processing advantage extends to variant multiword chunks that are less formulaic. To determine whether the processing advantage of multiword chunks accommodates variations in the canonical phrasal template, we examined the robustness of the processing advantage (i.e., Predictability) of binomial phrases with non-canonical conjunctions (e.g., salt and also pepper; salt as well as pepper). Results from the cloze study (Experiment 1) showed that there was a high tendency of producing the canonical conjunct (pepper), even in the binomials that contained non-formulaic conjunctions. Consistent with these findings, results from two eye tracking studies (Experiments 2a and 2b) showed that canonical conjuncts were read faster than novel conjuncts that were matched on word length (e.g., paprika), even in the binomials with variant conjunctions. This robust online processing advantage was replicated in a self-paced reading study that compared all three Conjunction Types (Experiment 3). Taken together, these findings show that binomials with variant function words also receive facilitated processing relative to matched novel strings, even though both types of strings are neither conventional nor relatively frequent. Exploratory analyses revealed that this processing speed advantage was driven by the lexical-semantic association between the canonical conjuncts (salt-pepper), rather than lexical and phrasal frequency. Overall, these results highlight flexibility in the processing of multiword chunks that current models of multiword storage and processing must take into account.

Online Simulation Quality Assessment of Illustration Patterns Based on Digital Art Design in Neural Network Perspective

Digital art illustration as new media is a major advancement in the history of illustration. It has broadened the application of graphic arts and the way of communication. At present, digital illustrators are constantly exploring new artistic expressions for digital illustration in order to pursue new digital effects. A wide range of integrated materials and experimental design concepts are gradually emerging, making digital illustration evolve in the competition. The artistic style of digital illustration has also shown the development trend of “diversification.” With the trend of commercialization of digital illustration, the quality of illustration patterns is gradually neglected. The traditional illustration pattern assessment relies on manual subjective judgment, with backward evaluation means and poor accuracy. This study proposes an illustration pattern evaluation method based on a deep neural network. In particular, this study proposes a reference-free image evaluation model with multiple feature fusion; specifically, we use CNN and information entropy-based methods for feature extraction and regularization methods for information fusion to solve the problem of missing reference images in applications. The chunking process is performed on the basis of considering the influence of information entropy on image quality, and the information entropy of multiple chunked features is calculated as importance weights, representing the degree of their influence on distorted image quality. The experimental results on the digital illustration pattern database show that the method in this study has strong robustness and can output a reasonable and reliable quality assessment score for distorted patterns.

Analyzing semantic similarity amongst textual documents to suggest near duplicates

<span>Data deduplication techniques removing repeated or redundant data from the storage. In recent days, more data has been generated and stored in the storage environment. More redundant and semantically similar content of the data occupied in the storage environment due to this storage efficiency will be reduced and cost of the storage will be high. To overcome this problem, we proposed a method hybrid bidirectional encoder representation from transformers for text semantics using graph convolutional network hybrid bidirectional encoder representation from transformers (BERT) model for text semantics (HBTSG) word embedding-based deep learning model to <span>identify near duplicates based on the semantic relationship between text documents. In this paper we hybridize the concepts of chunking and</span> <span>semantic analysis. The chunking process is carried out to split the documents into blocks. Next stage we identify the semantic relationship between</span> <span>documents using word embedding techniques. It combines the advantages of the chunking, feature extraction, and semantic relations to provide better results.</span></span>

Eye movements during text reading align with the rate of speech production.

Across languages, the speech signal is characterized by a predominant modulation of the amplitude spectrum between about 4.3 and 5.5 Hz, reflecting the production and processing of linguistic information chunks (syllables and words) every ~200 ms. Interestingly, ~200 ms is also the typical duration of eye fixations during reading. Prompted by this observation, we demonstrate that German readers sample written text at ~5 Hz. A subsequent meta-analysis of 142 studies from 14 languages replicates this result and shows that sampling frequencies vary across languages between 3.9 Hz and 5.2 Hz. This variation systematically depends on the complexity of the writing systems (character-based versus alphabetic systems and orthographic transparency). Finally, we empirically demonstrate a positive correlation between speech spectrum and eye movement sampling in low-skilled non-native readers, with tentative evidence from post hoc analysis suggesting the same relationship in low-skilled native readers. On the basis of this convergent evidence, we propose that during reading, our brain's linguistic processing systems imprint a preferred processing rate-that is, the rate of spoken language production and perception-onto the oculomotor system.

The association of eye movements and performance accuracy in a novel sight-reading task.

The present study investigated how eye movements were associated with performance accuracy during sight-reading. Participants performed a complex span task in which sequences of single quarter note symbols that either enabled chunking or did not enable chunking were presented for subsequent serial recall. In between the presentation of each note, participants sight-read a notated melody on an electric piano in the tempo of 70 bpm. All melodies were unique but contained four types of note pairs: eighth-eighth, eighthquarter, quarter-eighth, quarter-quarter. Analyses revealed that reading with fewer fixations was associated with a more accurate note onset. Fewer fixations might be advantageous for sight-reading as fewer saccades have to be planned and less information has to be integrated. Moreover, the quarter-quarter note pair was read with a larger number of fixations and the eighth-quarter note pair was read with a longer gaze duration. This suggests that when rhythm is processed, additional beats might trigger re-fixations and unconventional rhythmical patterns might trigger longer gazes. Neither recall accuracy nor chunking processes were found to explain additional variance in the eye movement data.

A Dual Simple Recurrent Network Model for Chunking and Abstract Processes in Sequence Learning.

Although many studies have provided evidence that abstract knowledge can be acquired in artificial grammar learning, it remains unclear how abstract knowledge can be attained in sequence learning. To address this issue, we proposed a dual simple recurrent network (DSRN) model that includes a surface SRN encoding and predicting the surface properties of stimuli and an abstract SRN encoding and predicting the abstract properties of stimuli. The results of Simulations 1 and 2 showed that the DSRN model can account for learning effects in the serial reaction time (SRT) task under different conditions, and the manipulation of the contribution weight of each SRN accounted for the contribution of conscious and unconscious processes in inclusion and exclusion tests in previous studies. The results of human performance in Simulation 3 provided further evidence that people can implicitly learn both chunking and abstract knowledge in sequence learning, and the results of Simulation 3 confirmed that the DSRN model can account for how people implicitly acquire the two types of knowledge in sequence learning. These findings extend the learning ability of the SRN model and help understand how different types of knowledge can be acquired implicitly in sequence learning.

Comparative Analysis for Content Defined Chunking Algorithms in Data Deduplication

Data deduplication works on eliminating redundant data and reducing storage consumption. Nowadays more data generated and it was stored in the cloud repeatedly, due to this large volume of storage will be consumed. Data deduplication tries to reduce data volumes disk space and network bandwidth can be to reduce costs and energy consumption for running storage systems. In the data deduplication method, data broken into small size of chunk or block. Hash ID will be calculated for all the blocks then it’s compared with existing blocks for duplication. Blocks may be fixed or variable size, compared with a fixed size of block variable size chunking gives a better result. So the chunking process is the initial task of deduplication to get an optimal result. In this paper, we discussed various content defined chunking algorithms and their performance based on chunking properties like chunking speed, processing time, and throughput.

Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data

In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide.

Towards a massive sentinel-2 LAI time-series production using 2-D convolutional networks

Biophysical parameters and more specifically the leaf area index provide an absolute quantification of the biomass of vegetation allowing an overview of the development status of a plant. However, the estimation of the leaf area index requires sophisticated and complex algorithms. This paper proposes a new procedure to estimate the leaf area index using Sentinel-2 data. The proposed procedure relies on the 2-D convolutional network known as the UNet algorithm for regression. The architecture of the UNet algorithm is adapted to account for the processing of large chunks of Sentinel-2 data. Moreover, the adopted procedure makes use of the dropout as a Bayesian approximation at the inference step in order to allow estimating the algorithm confidence interval, which is a very important quality indicator for the production of biophysical parameters. The proposed procedure is validated on multiple Sentinel-2 tiles and years and compared to the multilayer perceptron algorithm and the Sentinel Application Platform of the European Space Agency, also known as SNAP. The UNet and multilayer perceptron algorithms provide coherent results when compared to the results obtained using the SNAP software with an average correlation of 0.99 for both algorithms. However, the UNet algorithm provides better results in terms of average Euclidean distance, mean squared error and R2 score. One main advantage of the UNet algorithm is the vast reduction of inference time when compared to the SNAP software and the multilayer perceptron regressor. The estimation of the leaf area index of a Sentinel-2 tile at 20 m requires 18 s, 13.5 min and 15 min using the UNet, multilayer perceptron and SNAP, respectively. This advantage allows a massive production of temporal sequences of leaf area index based on Sentinel-2 images. Furthermore, experiments conducted on multiple crop types prove that the proposed approach can serve as a generic procedure to estimate the leaf area index regardless of the crop type.

Meaningfulness Beats Frequency in Multiword Chunk Processing.

Whereas a growing bulk of work has demonstrated that both adults and children are sensitive to frequently occurring word sequences, little is known about the potential role of meaning in the processing of such multiword chunks. Here, we take a first step toward assessing the contribution of meaningfulness in the processing of multiword sequences, using items that varied in chunk meaningfulness. In a phrasal-decision study, we compared reaction times for triads of three-word sequences, corresponding to idiomatic expressions, compositional phrases, and phrasal fragments, while controlling for phrase and substring frequencies. Chunk meaningfulness, as assessed by a separate subjective rating study, was found to speed up decision times for all three types of strings: The more meaningful a multiword sequence was judged to be, the faster it was processed, independently of whether it was idiomatic, compositional in nature, or a phrasal fragment. These results highlight the importance of taking meaning into account when considering the processing of multiword chunks, consistent with predictions of construction-based approaches to language.

How Do We Segment Text? Two-Stage Chunking Operation in Reading.

Chunking in language comprehension is a process that segments continuous linguistic input into smaller chunks that are in the reader’s mental lexicon. Effective chunking during reading facilitates disambiguation and enhances efficiency for comprehension. However, the chunking mechanisms remain elusive, especially in reading, given that information arrives simultaneously yet the written systems may not have explicit cues for labeling boundaries such as Chinese. What are the mechanisms of chunking that mediates the reading of the text that contains hierarchical information? We investigated this question by manipulating the lexical status of the chunks at distinct levels in four-character Chinese strings, including the two-character local chunk and four-character global chunk. Male and female human participants were asked to make lexical decisions on these strings in a behavioral experiment, followed by a passive reading task when their electroencephalography (EEG) was recorded. The behavioral results showed that the lexical decision time of lexicalized two-character local chunks was influenced by the lexical status of the four-character global chunk, but not vice versa, which indicated the processing of global chunks possessed priority over the local chunks. The EEG results revealed that familiar lexical chunks were detected simultaneously at both levels and further processed in a different temporal order, the onset of lexical access for the global chunks was earlier than that of local chunks. These consistent results suggest a two-stage operation for chunking in reading, the simultaneous detection of familiar lexical chunks at multiple levels around 100 ms followed by recognition of chunks with global precedence.