The effects of individual differences in native perception on discrimination of a novel non-native contrast

Native (L1) phonetic categories can constrain the perception of non-native contrasts which deviate from the listener’s L1 (Best & Tyler, 2007; Flege, 1995). Yet, some individuals are remarkably successful at accurately perceiving non-native sounds (e.g., Bongaerts, van Summeren, Planken, & Schils, 1997). We hypothesize that compact L1 categories give an initial advantage in distinguishing non-native contrasts. Sixty-eight Spanish monolinguals were tested on the degree of compactness of their native category /i/, measured through a goodness-of-fit rating task. Participants listened to synthesized variants of the Spanish /i/ vowel (differing in F1, F2, or both) and rated them as either good or bad exemplars of their representation of this category. An individual /i/ compactness index was calculated for each participant and related to the individual perceived dissimilarity score for the novel Russian contrast /i – ɨ/. The Russian contrast /i – ɨ/ is a problematic contrast to perceive for Spanish speakers due to the absence of /ɨ/ in the Spanish vowel inventory, a sound acoustically very similar to /i/. In this study, the compactness of the L1 category /i/ weakly predicted perceptual sensitivity (dissimilarity scores) for the Russian contrast /i – ɨ/

Phonaesthetics and personality—Why we do not only prefer Romance languages

Introduction: Previous aesthetic research has set its main focus on visual and auditory, primarily music, stimuli with only a handful of studies exploring the aesthetic potential of linguistic stimuli. In the present study, we investigate for the first time the effects of personality traits on phonaesthetic language ratings. Methods: Twenty-three under-researched, “rarer” (less learned and therefore less known as a foreign language or L2) and minority languages were evaluated by 145 participants in terms of eroticism, beauty, status, and orderliness, subjectively perceived based on language sound. Results: Overall, Romance languages (Catalan, Portuguese, Romanian) were still among the top six erotic languages of the experiment together with “Romance-sounding,” but less known languages like Breton and Basque. Catalan and Portuguese were also placed among the top six most beautiful languages. The Germanic languages (Swedish, Norwegian, Danish, and Icelandic) were perceived as more prestigious/higher in terms of status, however to some degree conditioned by their recognition/familiarity. Thus, we partly replicated the results of our earlier studies on the Romance language preferences (the so-called Latin Lover effect) and the perceived higher status of the Germanic languages and scrutinized again the effects of familiarity/language recognition, thereby calling into question the above mentioned concepts of the Latin Lover effect and the status of Germanic languages. We also found significant effects of personality traits (neuroticism, extraversion, and conscientiousness) on phonaesthetic ratings. Different personality types appreciated different aspects of languages: e.g., whereas neurotics had strong opinions about languages' eroticism, more conscientious participants gave significantly different ratings for status. Introverts were more generous in their ratings overall in comparison to extroverts. We did not find strong connections between personality types and specific languages or linguistic features (sonority, speech rate). Overall, personality traits were largely overridden by other individual differences: familiarity with languages (socio-cultural construals, the Romanization effect—perceiving a particular language as a Romance language) and participants' native language/L1. Discussion: For language education in the global context, our results mean that introducing greater linguistic diversity in school and universities might result in greater appreciation and motivation to learn lesser-known and minority languages. Even though we generally prefer Romance languages to listen to and to study, different personality types are attracted to different language families and thus make potentially successful learners of these languages

Eros, Beauty, and Phon-Aesthetic Judgements of Language Sound. We Like It Flat and Fast, but Not Melodious. Comparing Phonetic and Acoustic Features of 16 European Languages

This paper concerns sound aesthetic preferences for European foreign languages. We investigated the phonetic-acoustic dimension of the linguistic aesthetic pleasure to describe the “music” found in European languages. The Romance languages, French, Italian, and Spanish, take a lead when people talk about melodious language – the music-like effects in the language (a.k.a., phonetic chill). On the other end of the melodiousness spectrum are German and Arabic that are often considered sounding harsh and un-attractive. Despite the public interest, limited research has been conducted on the topic of phonaesthetics, i.e., the subfield of phonetics that is concerned with the aesthetic properties of speech sounds (Crystal, 2008). Our goal is to fill the existing research gap by identifying the acoustic features that drive the auditory perception of language sound beauty. What is so music-like in the language that makes people say “it is music in my ears”? We had 45 central European participants listening to 16 auditorily presented European languages and rating each language in terms of 22 binary characteristics (e.g., beautiful – ugly, funny - boring) plus indicating their language familiarities, L2 backgrounds, speaker voice liking, demographics and musicality levels. Findings revealed that all factors in complex interplay explain a certain percentage of variance: familiarity and expertise in foreign languages, speaker voice characteristics, phonetic complexity, musical acoustic properties, and finally musical expertise of the listener. The most important discovery was the trade-off between speech tempo and so-called linguistic melody (pitch variance): the faster the language, the flatter/more atonal it is in terms of the pitch (speech melody), making it highly appealing acoustically (sounding beautiful and sexy), but not so melodious in a “musical” sense

Collecting Big Data Through Citizen Science: Gamification and Game-based Approaches to Data Collection in Applied Linguistics

Gamification of behavioral experiments has been applied successfully to research in a number of disciplines, including linguistics. We believe that these methods have been underutilized in applied linguistics, in particular second-language acquisition research. The incorporation of games and gaming elements (gamification) in behavioral experiments has been shown to mitigate many of the practical constraints characteristic of lab settings, such as limited recruitment or only achieving small-scale data. However, such constraints are no longer an issue with gamified and game-based experiments, and as a result, data collection can occur remotely with greater ease and on a much wider scale, yielding data that are ecologically valid and robust. These methods enable the collection of data that are comparable in quality to the data collected in more traditional settings while engaging far more diverse participants with different language backgrounds that are more representative of the greater population. We highlight three successful applications of using games and gamification with applied linguistic experiments to illustrate the effectiveness of such approaches in a greater effort to invite other applied linguists to do the same

Multicenter Validation of Metabolic Abnormalities Related to PSP According to theMDS-PSPCriteria

It remains unclear whether the supportive imaging features described in the diagnostic criteria for progressive supranuclear palsy (PSP) are suitable for the full clinical spectrum. The aim of the current study was to define and cross-validate the pattern of glucose metabolism in the brain associated with a diagnosis of different PSP variants. A retrospective multicenter cohort study performed on 73 PSP patients who were referred for a fluorodeoxyglucose positron emission tomography PET scan: PSP–Richardson's syndrome, n = 47; PSP–parkinsonian variant, n = 18; and progressive gait freezing, n = 8. In addition, we included 55 healthy controls and 58 Parkinson's disease (PD) patients. Scans were normalized by global mean activity. We analyzed the regional differences in metabolism between the groups. Moreover, we applied a multivariate analysis to obtain a PSP-related pattern that was cross-validated in independent populations at the individual level. Group analysis showed relative hypometabolism in the midbrain, basal ganglia, thalamus, and frontoinsular cortices and hypermetabolism in the cerebellum and sensorimotor cortices in PSP patients compared with healthy controls and PD patients, the latter with more severe involvement in the basal ganglia and occipital cortices. The PSP-related pattern obtained confirmed the regions described above. At the individual level, the PSP-related pattern showed optimal diagnostic accuracy to distinguish between PSP and healthy controls (sensitivity, 80.4%; specificity, 96.9%)s and between PSP and PD (sensitivity, 80.4%; specificity, 90.7%). Moreover, PSP–Richardson's syndrome and PSP–parkinsonian variant patients showed significantly more PSP-related pattern expression than PD patients and healthy controls. The glucose metabolism assessed by fluorodeoxyglucose PET is a useful and reproducible supportive diagnostic tool for PSP–Richardson's syndrome and PSP–parkinsonian variant

Multicenter Validation of Metabolic Abnormalities Related to PSP

It remains unclear whether the supportive imaging features described in the diagnostic criteria for progressive supranuclear palsy (PSP) are suitable for the full clinical spectrum. The aim of the current study was to define and cross-validate the pattern of glucose metabolism in the brain associated with a diagnosis of different PSP variants. A retrospective multicenter cohort study performed on 73 PSP patients who were referred for a fluorodeoxyglucose positron emission tomography PET scan: PSP–Richardson's syndrome, n = 47; PSP–parkinsonian variant, n = 18; and progressive gait freezing, n = 8. In addition, we included 55 healthy controls and 58 Parkinson's disease (PD) patients. Scans were normalized by global mean activity. We analyzed the regional differences in metabolism between the groups. Moreover, we applied a multivariate analysis to obtain a PSP-related pattern that was cross-validated in independent populations at the individual level. Group analysis showed relative hypometabolism in the midbrain, basal ganglia, thalamus, and frontoinsular cortices and hypermetabolism in the cerebellum and sensorimotor cortices in PSP patients compared with healthy controls and PD patients, the latter with more severe involvement in the basal ganglia and occipital cortices. The PSP-related pattern obtained confirmed the regions described above. At the individual level, the PSP-related pattern showed optimal diagnostic accuracy to distinguish between PSP and healthy controls (sensitivity, 80.4%; specificity, 96.9%)s and between PSP and PD (sensitivity, 80.4%; specificity, 90.7%). Moreover, PSP–Richardson's syndrome and PSP–parkinsonian variant patients showed significantly more PSP-related pattern expression than PD patients and healthy controls. The glucose metabolism assessed by fluorodeoxyglucose PET is a useful and reproducible supportive diagnostic tool for PSP–Richardson's syndrome and PSP–parkinsonian variant

An application of generalized matrix learning vector quantization in neuroimaging

BACKGROUND AND OBJECTIVE: Neurodegenerative diseases like Parkinson's disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ). METHODS: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination's validity, we analyze FDG-PET data of Parkinson's disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists. RESULTS: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients. CONCLUSION: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully

An application of generalized matrix learning vector quantization in neuroimaging

Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ). Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists. Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients. Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully

An application of generalized matrix learning vector quantization in neuroimaging

Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ). Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists. Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients. Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully