Meaningful Chunks Research Articles

Breaking (musical) boundaries by investigating brain dynamics of event segmentation during real-life music-listening

The perception of musical phrase boundaries is a critical aspect of human musical experience: It allows us to organize, understand, derive pleasure from, and remember music. Identifying boundaries is a prerequisite for segmenting music into meaningful chunks, facilitating efficient processing and storage while providing an enjoyable, fulfilling listening experience through the anticipation of upcoming musical events. Expanding on Sridharan et al.'s [Neuron 55, 521-532 (2007)] work on coarse musical boundaries between symphonic movements, we examined finer-grained boundaries. We measured the fMRI responses of 18 musicians and 18 nonmusicians during music listening. Using general linear model, independent component analysis, and Granger causality, we observed heightened auditory integration in anticipation to musical boundaries, and an extensive decrease within the fronto-temporal-parietal network during and immediately following boundaries. Notably, responses were modulated by musicianship. Findings uncover the intricate interplay between musical structure, expertise, and cognitive processing, advancing our knowledge of how the brain makes sense of music.

Updating predictions in a complex repertoire of actions and its neural representation

Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires.

Scaffolding as active learning in nursing education

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.

DATA MINING IN HEALTHCARE SECTOR

Disease detection is one of the applications where data mining techniques achieved more accurate and useful results. The healthcare sector collects massive volumes of healthcare data that are not mine to discover hidden data for better decision-making, a field of data mining introduces more efficiently and effectively to predict different kinds of diseases. Clustering medical data into small, meaningful chunks will help in pattern discovery by allowing for the retrieval of a large number of specific data points. The difference in using clustering the medical data from traditional data mining techniques is in extracting many features of the dataset that have been split into small segments to enable us to discover patterns by adding the data structure. By using clustering techniques, discovered overall correlations between data attributes. Selected data processing makes the mining process more efficient. The processed disease data are clustered using the K-means algorithm with the K values. Its ease of use and speed, which enable it to perform on a massive dataset. This paper highlights the theoretical side in using the K-Means Clustering algorithm in the context of data mining of disease detection and allowing for reliable and effective diagnosis.

Cortical auditory responses index the contributions of different RMS-level-dependent segments to speech intelligibility

Previous behavioral and neurophysiological studies indicated that the use of an appropriate segmentation method to parse speech streams into meaningful chunks is of vital importance for the examination of sentence perception and intelligibility. Researchers have recently proposed speech segmentation methods employing the relative root-mean-square (RMS) intensity to separate sentences into segments with distinct intelligibility information. However, the effects of different RMS-level segments containing distinct intelligibility information on neural oscillations are not clear. Using scalp-recorded electroencephalography (EEG) data, we investigated the hypothesis that perceptual responses to different RMS-level-dependent speech segments would have distinct EEG characteristics derived from the power values at each frequency band and the relationship between acoustics and neural oscillations at different response time and spatial distribution. We analyzed the EEG power and synchronized neural oscillations corresponding to auditory temporal fluctuations when subjects listened to Mandarin sentences with only high-RMS-level segments and only middle-RMS-level segments preserved, respectively. The results showed significantly stronger EEG spectral power in the delta and theta bands for high-RMS-level stimuli compared with middle-RMS-level stimuli, indicating that the former carry more speech-parsing information at the syllabic level. Differences in neural synchronization were also found between the high- and middle-RMS-level stimuli, allowing for the derivation of intelligibility indices for cortical responses corresponding to different RMS-level segments. These findings suggest that both high- and middle-RMS-level segments drive delta and theta rhythms to track stimuli, and that neural oscillations employ different tracking patterns for these two segment types during auditory sentence processing. Moreover, they suggest that neural oscillations can serve as effective indices for the identification of reliable intelligibility factors in RMS-level-dependent stimuli.

How the Human Brain Segments Continuous Experience.

The formation and subsequent recall of coherent episodes from our past (i.e., our episodic memories) relies on the ability to parse the continuous stream of experience in our daily lives into meaningful chunks or segments ([Zacks et al., 2010][1]). This segmentation is thought to determine how

Temporal Chunking Makes Life's Events More Memorable.

Declines in episodic memory accompany both healthy aging and age-related diseases, such as dementia. Given that memory complaints are common in the aging population, a wealth of research has evaluated the underlying mechanisms of these declines and explored strategy interventions that could offset them. In the current paper, we describe a newer approach to improving memory: event segmentation training. Event segmentation is an encoding strategy in which individuals parse continuous activity into meaningful chunks. The ability to segment activity is associated with later memory for the events, but unfortunately, this segmentation ability declines with age. Importantly, interventions designed to improve event segmentation have resulted in memory improvements for both young and older adults. We will review these past experiments as well as some new event segmentation training work that uses older adults' semantic knowledge to improve their segmentation and episodic memory. We believe that future research on event segmentation is a promising avenue for improving older adults' ability to remember everyday activities.

The Intention to Use Mobile Student Portal: A Mobile Human Computer Interaction Study in a University Context in Egypt

Mobile devices are increasingly being used as platforms for interactive services. However, factors affecting adoption of mobile services in some specific contexts, such as student portals, are still underresearched. This paper reports thematic analyses of semistructured interviews conducted with 52 undergraduate users of mobile student portal in a developing country. The results uncover design features affecting the use of mobile student portals, such as (1) Content Sharing, comprising an Integrative Design to share posts from the mobile portal to social media, (2) Personalized Content and Notification, for personalized notification based on student’s academic status, (3) Location-Aware notification, providing location-based updates, (4) User Control, allowing to choose content formats and details level, and (5) Context Switching and Interrupted Behavior, leading to the need for displaying content in small, meaningful chunks to help pick up after interruptions of messages and calls. The findings suggest design recommendations for mobile student portal, in specific.

Neural Models for Sequence Chunking

Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside- Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot filling tasks.

Acoustic landmarks drive delta–theta oscillations to enable speech comprehension by facilitating perceptual parsing

A growing body of research suggests that intrinsic neuronal slow (<10Hz) oscillations in auditory cortex appear to track incoming speech and other spectro-temporally complex auditory signals. Within this framework, several recent studies have identified critical-band temporal envelopes as the specific acoustic feature being reflected by the phase of these oscillations. However, how this alignment between speech acoustics and neural oscillations might underpin intelligibility is unclear. Here we test the hypothesis that the ‘sharpness’ of temporal fluctuations in the critical band envelope acts as a temporal cue to speech syllabic rate, driving delta–theta rhythms to track the stimulus and facilitate intelligibility. We interpret our findings as evidence that sharp events in the stimulus cause cortical rhythms to re-align and parse the stimulus into syllable-sized chunks for further decoding. Using magnetoencephalographic recordings, we show that by removing temporal fluctuations that occur at the syllabic rate, envelope-tracking activity is reduced. By artificially reinstating these temporal fluctuations, envelope-tracking activity is regained. These changes in tracking correlate with intelligibility of the stimulus. Together, the results suggest that the sharpness of fluctuations in the stimulus, as reflected in the cochlear output, drive oscillatory activity to track and entrain to the stimulus, at its syllabic rate. This process likely facilitates parsing of the stimulus into meaningful chunks appropriate for subsequent decoding, enhancing perception and intelligibility.

Abstract structure

PurposeReflects on the process of summarising research.Design/methodology/approachA musical structure is used as a trope for the usual research cycle of distilling reams of data into a short article that is sectioned into meaningful chunks, all of which is summarised in an abstract and represented by a few keywords.FindingsThe reductive process ultimately sublates the empirical world to a series of keywords.Research limitations/implicationsProvides a reflexive break to academic work.Originality/valueReflects on the experience of summarising a lengthy research project to a few keywords. Hopefully, the thing is read at the end.

Use of concept mapping in an undergraduate introductory exercise physiology course

Physiology is often considered a challenging course for students. It is up to teachers to structure courses and create learning opportunities that will increase the chance of student success. In an undergraduate exercise physiology course, concept maps are assigned to help students actively process and organize information into manageable and meaningful chunks and to teach them to recognize the patterns and regularities of physiology. Students are first introduced to concept mapping with a commonly relatable nonphysiology concept and are then assigned a series of maps that become more and more complex. Students map the acute response to a drop in blood pressure, the causes of the acute increase in stroke volume during cardiorespiratory exercise, and the factors contributing to an increase in maximal O(2) consumption with cardiorespiratory endurance training. In the process, students draw the integrative nature of physiology, identify causal relationships, and learn about general models and core principles of physiology.

Use of Concept Mapping in an Undergraduate Introductory Exercise Physiology Course

Physiology is often considered a challenging course for students. It is up to teachers to structure courses and create learning opportunities that will increase the chance of student success. In an undergraduate exercise physiology course, concept maps are assigned to help students actively process and organize information into manageable and meaningful chunks and to teach them to recognize the patterns and regularities of physiology. Students are first introduced to concept mapping with a commonly relatable non‐physiology concept and are then assigned a series of maps that become more and more complex. Students map the acute response to a drop in blood pressure, the causes of the acute increase in stroke volume during cardiorespiratory exercise, and the factors contributing to an increase in maximal oxygen consumption with cardiorespiratory endurance training. In the process, students draw the integrative nature of physiology, identify causal relationships, and learn about general models and core principles of physiology.

Salient phonetic features of Indian languages in speech technology

Speech signal is the basic study and analysis material in speech technology as well phonetics. To form meaningful chunks of language, the speech signal should have dynamically varying spectral characteristics, sometimes varying within a stretch of a few milliseconds. Phonetics groups these temporally varying spectral chunks into abstract classes roughly called as allophones. Distribution of these allophones into higher level classes called phonemes takes us closer to their function in a language. Phonemes and letters in the scripts of literate languages – languages which use writing have varying degrees of correspondence. As such a relationship exists, a major part of speech technology deals with the correlation of script letters with chunks of time-varying spectral stretches in that language. Indian languages are said to have a more direct correlation between their sounds and letters. Such similarity gives a false impression of similarity of text-to-sound rule sets across these languages. A given letter which has parallels across various languages may have different degrees of divergence in its phonetic realization in these languages. We illustrate such differences and point out the problem areas where speech scientists need to pay greater attention in building their systems, especially multilingual systems for Indian languages.

Classification, Disease, and Diagnosis

Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

From spreading of behavior to dyadic interaction-A robot learns what to imitate

Imitation learning is a promising way to learn new behavior in robotic multiagent systems and in human-robot interaction. However, imitating agents should be able to decide autonomously which behavior, observed in others, is interesting to copy. This paper shows a method for extraction of meaningful chunks of information from a continuous sequence of observed actions by using a simple recurrent network (Elman Net). Results show that, independently of the high level of task-specific noise, Elman nets can be used for learning through prediction a reoccurring action patterns, observed in another robotic agent. We conclude that this primarily robot to robot interaction study can be generalized to human-robot interaction and show how we use these results for recognizing emotional behaviors in human-robot interaction scenarios. The limitations of the proposed approach and the future directions are discussed. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Employing cognitive chunking techniques to enhance sight-reading performance of undergraduate group-piano students

The purpose of this study was to compare the effect of cognitive chunking techniques among first-semester group-piano music majors. The ability to group discrete pieces of information into larger, more meaningful chunks is essential for efficient cognitive processing. Since reading keyboard music and playing the piano is a cognitively complex task, the ability to recognize patterns and execute the necessary motor skills is critical for proficiency in sight-playing. Three groups of beginning group-piano students ( N = 43) worked on 12 sight-reading examples during six class sessions. A control group rehearsed the examples without benefit of rhythm or pitch drills. Two experimental groups drilled either rhythm or pitch patterns prior to practicing the sight-reading exercises. Following the treatment phase, all subjects were tested on three sightreading examples to determine whether rhythmic and pitch chunking drills impacted reading performance at the keyboard. Subjects were evaluated on rhythmic accuracy, pitch accuracy and continuity. There were significant improvements from pretest to posttest in several subcategories. The pitch experimental group improved significantly in pitch, rhythm and continuity accuracy. The rhythm experimental group improved in rhythm and continuity, while the control group improved only in pitch accuracy. Experimental subjects reported significant engagement in rhythm and pitch chunking even if lack of motor skills impaired the actual performance.

Metaphor awareness in teaching vocabulary

Patterns of language are usually perceived, learned and used as meaningful chunks that are processed as a whole, resulting in a reduced learning burden and increased fluency. The ability to comprehend and produce lexical chunks or groups of words which are commonly found together is an important part of language acquisition. This paper demonstrates how an awareness of conceptual metaphor and grouping of various words and expressions in a metaphorical chunk may improve the process of vocabulary acquisition. Since words that appear in language as a result of metaphorical extensions resemble other etymologically related words, this method may help learners in establishing mental associations and speed up learning, especially if students already know words to which new vocabulary is related. In this way, learners' prior knowledge would assist in assimilating new information by reducing burden on limited-capacity working memory. The current paper offers a specific example of implementing this approach, and demonstrates how Russian words and idiomatic expressions can be presented in metaphorical chunks to facilitate cognitively efficient learning. It suggests that a similar approach may potentially be extended to syntactic properties of words that could be explained via conceptual metaphors encoded in their origin. By exploring this connection, a metaphorical approach could also be used in teaching grammar.

ZemPod: A semantic web approach to podcasting

In this paper we present a semantic web approach to solve some current limitations of podcasting. The main shortcomings of podcasts are two. The first one is that there is no formal description of the contents of a podcast session, apart from a textual description only available in HTML. The second problem is that a podcast session consists of a single audio file. Thus, it is very difficult to seek into one of the music tracks that compose a podcast. Our proposal to cope with these problems uses traditional audio signal processing – such as speech versus music segmentation, and audio identification –, and semantic web techniques to automatically describe and decompose the audio content of a podcast session. Yet, we believe that adding semantics to the podcast to explain its content, and decomposing it into smaller and meaningful chunks (that permits seeking into the inner parts of the file) will ease important music information retrieval tasks, such as recommendation, filtering and discovery.

ZemPod: A Semantic Web Approach to Podcasting

In this paper we present a semantic web approach to solve some current limitations of podcasting. The main shortcomings of podcasts are two. The first one is that there is no formal description of the contents of a podcast session, apart from a textual description only available in HTML. The second problem is that a podcast session consists of a single audio file. Thus, it is very difficult to seek into one of the music tracks that compose a podcast. Our proposal to cope with these problems uses traditional audio signal processing – such as speech versus music segmentation, and audio identification – and semantic web techniques to automatically describe and decompose the audio content of a podcast session. Yet, we believe that adding semantics to the podcast to explain its content, and decomposing it into smaller and meaningful chunks (that permits seeking into the inner parts of the file) will ease important music information retrieval tasks, such as recommendation, filtering and discovery.