Commonality and variation in mental representations of music revealed by a cross-cultural comparison of rhythm priors in 15 countries

Music is present in every known society but varies from place to place. What, if anything, is universal to music cognition? We measured a signature of mental representations of rhythm in 39 participant groups in 15 countries, spanning urban societies and Indigenous populations. Listeners reproduced random 'seed' rhythms; their reproductions were fed back as the stimulus (as in the game of 'telephone'), such that their biases (the prior) could be estimated from the distribution of reproductions. Every tested group showed a sparse prior with peaks at integer-ratio rhythms. However, the importance of different integer ratios varied across groups, often reflecting local musical practices. Our results suggest a common feature of music cognition: discrete rhythm 'categories' at small-integer ratios. These discrete representations plausibly stabilize musical systems in the face of cultural transmission but interact with culture-specific traditions to yield the diversity that is evident when mental representations are probed across many cultures. [Abstract copyright: © 2024. The Author(s).

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Automatic detection of clusters and switches in Turkish semantic verbal fluency data

Verbal fluency tests are popular measures of executive function. These tests involve listing as many words from a given category as possible in a short time, typically 60 seconds. In phonemic verbal fluency tests, these words should begin with the same letter; in semantic verbal fluency tests (SVF), they should belong to the same category, e.g., animals. SVF is quick to administer, amenable to semi-automated analysis, and can be used to screen for cognitive impairments such as dementia. Troyer and collaborators proposed a fine-grained analysis method for SVF sequences that divides them into clusters, i.e., sequences of more closely semantically related words. Useful metrics that can be derived from such an analysis include mean cluster size and the number of switches between clusters. The aim of this thesis is to develop semiautomated methods to extract cluster- and switch-related metrics from Turkish SVF sequences. First, we conducted a systematic review of studies that report SVF performance of healthy adult native Turkish speakers, using international and Turkish databases including unpublished theses. We particularly focused on normative data and commonly used methods for collecting and analysing SVF data. We found that all included papers reported SVF sequences using the animal category, followed by first names. Considering the size of Turkish diaspora, there was a lack of studies comparing monolingual speakers to bilingual speakers. Detailed analyses beyond word count, such as perseverations, category violations, and clustering/switching were only rarely reported. Semi-automatic and automatic approaches were almost never used. The thesis therefore fills a clear gap in the literature. For our work on Turkish, we chose two computational approaches that can be easily adapted to languages with comparatively few corpus resources: a simple bigram method and a vector-space model (word2vec). We initially implemented and tested those methods on a Spanish dataset which included 50 healthy participants and 14 participants diagnosed with familial AD. Both computational models positioned switches very similarly to manual annotations, achieving F1=0.756 for Bigram and F1=0.8309 for Word2vec. There is no difference in terms of cluster sizes (p>0.01), but healthy participants produce significantly more switches (p<0.001). These findings hold both for the manual analysis and the automatic analysis. Since there are no public datasets of Turkish SVF data, we collected SVF data online from native speakers of Turkish with no self-reported cognitive impairments living both in Turkey and abroad. To the best of our knowledge, this is the first online spoken corpus of SVF for Turkish. The study used the three most frequently used categories in Turkish SVF data that have also been reported for other languages, namely animals, fruits and vegetables, and supermarket items. The study had two parts, an initial Qualtrics survey for screening and collecting relevant participant information, and a web-based app for collecting three SVF sequences. 286 participants consented to take part in the survey, and 137 (47.9%) continued on to the SVF app. In total, we collected 311 SVF sequences (Animals=105, Vegetables and Fruits=105, Supermarket Items=101) from 137 adults. The mean number of items produced per category is 25.04 (SD=X) for animals, 25.32 (SD=Y) for fruits and vegetables, and 25.97 (SD=Z) for supermarket items. Overall, data quality of the recorded sequences was good. The reasons for the drop off between survey and SVF data collections need to be investigated in further work. Finally, we adapted the computational techniques used for Spanish to the Turkish SVF data and assessed their ability to replicate clustering and switching based metrics. We found that both bigram and word2vec performed satisfactorily. There was no significant difference in cluster sizes, and switch numbers were highly correlated (p<0.001). In terms of predicting switch position, word2vec reached F1=0.738 and Bigram achieved F1=0.66. Next, we examined whether findings obtained from manual annotation of clusters and switches could be replicated using metrics derived using the two computational methods. Specifically, we investigated cluster size and switch numbers between male and female participants (sex) and between mono- and multilingual participants (multilinguality). Based on the manual analysis, we established that male participants created larger clusters than female participants, but used a similar number of switches. There were no significant differences between monolingual and multilingual participants. Both findings are in line with the existing literature on Turkish SVF. While bigram and word2vec yielded a similar result regarding number of switches, only word2vec-derived metrics replicated the difference in cluster size between male and female participants. In future work, other computational approaches, such as large language models, should be explored, automatic speech recognition should be integrated to eliminate the need for manual transcription, and additional speech-based features can be investigated. Finally, user experience research may help to improve online data collection and reduce the number of participants who drop out of the study before speech data collection

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Attention Restraint, Working Memory Capacity, and Mind Wandering: Do Emotional Valence or Intentionality Matter?

Attention restraint appears to mediate the relationship between working memory capacity (WMC) and mind wandering (Kane et al., 2016). Prior work has identifed two dimensions of mind wandering—emotional valence and intentionality. However, less is known about how WMC and attention restraint correlate with these dimensions. Te current study examined the relationship between WMC, attention restraint, and mind wandering by emotional valence and intentionality. A confrmatory factor analysis demonstrated that WMC and attention restraint were strongly correlated, but only attention restraint was related to overall mind wandering, consistent with prior fndings. However, when examining the emotional valence of mind wandering, attention restraint and WMC were related to negatively and positively valenced, but not neutral, mind wandering. Attention restraint was also related to intentional but not unintentional mind wandering. Tese results suggest that WMC and attention restraint predict some, but not all, types of mind wandering

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality