Sound symbolism in Japanese names: Machine learning approaches to gender classification
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ABSTRACT:
SUBMITTER: Ngai C
PROVIDER: S-EPMC10927153 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Although the arbitrariness of language has been considered one of its defining features, studies have demonstrated that certain phonemes tend to be associated with certain kinds of meaning. A well-known example is the Bouba/Kiki effect, in which nonwords like bouba are associated with round shapes while nonwords like kiki are associated with sharp shapes. These sound symbolic associations have thus far been limited to nonwords. Here we tested whether or not the Bouba/Kiki effect extends to existing lexical stimuli; in particular, real first names. We found that the roundness/sharpness of the phonemes in first names impacted whether the names were associated with round or sharp shapes in the form of character silhouettes (Experiments 1a and 1b). We also observed an association between femaleness and round shapes, and maleness and sharp shapes. We next investigated whether this association would extend to the features of language and found the proportion of round-sounding phonemes was related to name gender (Analysis of Category Norms). Finally, we investigated whether sound symbolic associations for first names would be observed for other abstract properties; in particular, personality traits (Experiment 2). We found that adjectives previously judged to be either descriptive of a figuratively 'round' or a 'sharp' personality were associated with names containing either round- or sharp-sounding phonemes, respectively. These results demonstrate that sound symbolic associations extend to existing lexical stimuli, providing a new example of non-arbitrary mappings between form and meaning.
| S-EPMC4446333 | biostudies-literature
Project description:The aim of the present paper is to experimentally test whether sound symbolism has selective effects on labels with different ranges-of-reference within a simple noun-hierarchy. In two experiments, adult participants learned the make up of two categories of unfamiliar objects ('alien life forms'), and were passively exposed to either category-labels or item-labels, in a learning-by-guessing categorization task. Following category training, participants were tested on their visual discrimination of object pairs. For different groups of participants, the labels were either congruent or incongruent with the objects. In Experiment 1, when trained on items with individual labels, participants were worse (made more errors) at detecting visual object mismatches when trained labels were incongruent. In Experiment 2, when participants were trained on items in labelled categories, participants were faster at detecting a match if the trained labels were congruent, and faster at detecting a mismatch if the trained labels were incongruent. This pattern of results suggests that sound symbolism in category labels facilitates later similarity judgments when congruent, and discrimination when incongruent, whereas for item labels incongruence generates error in judgements of visual object differences. These findings reveal that sound symbolic congruence has a different outcome at different levels of labelling within a noun hierarchy. These effects emerged in the absence of the label itself, indicating subtle but pervasive effects on visual object processing.
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Project description:Sound symbolism, or the nonarbitrary link between linguistic sound and meaning, has often been discussed in connection with language evolution, where the oral imitation of external events links phonetic forms with their referents (e.g., Ramachandran & Hubbard, 2001). In this research, we explore whether sound symbolism may also facilitate synchronic language learning in human infants. Sound symbolism may be a useful cue particularly at the earliest developmental stages of word learning, because it potentially provides a way of bootstrapping word meaning from perceptual information. Using an associative word learning paradigm, we demonstrated that 14-month-old infants could detect Köhler-type (1947) shape-sound symbolism, and could use this sensitivity in their effort to establish a word-referent association.
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Project description:ObjectiveA method to estimate absolute left ventricular (LV) pressure and its maximum rate of rise (LV dP/dtmax) from epicardial accelerometer data and machine learning is proposed.MethodsFive acute experiments were performed on pigs. Custom-made accelerometers were sutured epicardially onto the right ventricle, LV, and right atrium. Different pacing configurations and contractility modulations, using isoflurane and dobutamine infusions, were performed to create a wide variety of hemodynamic conditions. Automated beat-by-beat analysis was performed on the acceleration signals to evaluate amplitude, time, and energy-based features. For each sensing location, bootstrap aggregated classification tree ensembles were trained to estimate absolute maximum LV pressure (LVPmax) and LV dP/dtmax using amplitude, time, and energy-based features. After extraction of acceleration and pressure-based features, location specific, bootstrap aggregated classification ensembles were trained to estimate absolute values of LVPmax and its maximum rate of rise (LV dP/dtmax) from acceleration data.ResultsWith a dataset of over 6,000 beats, the algorithm narrowed the selection of 17 predefined features to the most suitable 3 for each sensor location. Validation tests showed the minimal estimation accuracies to be 93% and 86% for LVPmax at estimation intervals of 20 and 10 mmHg, respectively. Models estimating LV dP/dtmax achieved an accuracy of minimal 93 and 87% at estimation intervals of 100 and 200 mmHg/s, respectively. Accuracies were similar for all sensor locations used.ConclusionUnder pre-clinical conditions, the developed estimation method, employing epicardial accelerometers in conjunction with machine learning, can reliably estimate absolute LV pressure and its first derivative.
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Project description:Sound symbolism is the systematic and non-arbitrary link between word and meaning. Although a number of behavioral studies demonstrate that both children and adults are universally sensitive to sound symbolism in mimetic words, the neural mechanisms underlying this phenomenon have not yet been extensively investigated. The present study used functional magnetic resonance imaging to investigate how Japanese mimetic words are processed in the brain. In Experiment 1, we compared processing for motion mimetic words with that for non-sound symbolic motion verbs and adverbs. Mimetic words uniquely activated the right posterior superior temporal sulcus (STS). In Experiment 2, we further examined the generalizability of the findings from Experiment 1 by testing another domain: shape mimetics. Our results show that the right posterior STS was active when subjects processed both motion and shape mimetic words, thus suggesting that this area may be the primary structure for processing sound symbolism. Increased activity in the right posterior STS may also reflect how sound symbolic words function as both linguistic and non-linguistic iconic symbols.
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Project description:Sound symbolism is a non-arbitrary correspondence between sound and meaning. The majority of studies on sound symbolism have focused on consonants and vowels, and the sound-symbolic properties of suprasegmentals, particularly phonation types, have been largely neglected. This study examines the size and shape symbolism of four phonation types: modal and creaky voices, falsetto, and whisper. Japanese speakers heard 12 novel words (e.g., /íbi/, /ápa/) pronounced with the four types of phonation and rated the size and roundedness/pointedness each of the 48 stimuli seemed to represent on seven-point scales. The results showed that phonation types as well as consonantal and vocalic features influenced the ratings. Creaky voice was associated with larger and more pointed images than modal voice, which was in turn associated with larger and more pointed images than whisper. Falsetto was also associated with roundedness but not with smallness. These results shed new light on the acoustic approaches to sound symbolism and suggest the significance of phonation types and other suprasegmental features in the phenomenon.
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Project description:The analysis and identification of different attributes of produce such as taxonomy, vendor, and organic nature is vital to verifying product authenticity in a distribution network. Though a variety of analysis techniques have been studied in the past, we present a novel data-centric approach to classifying produce attributes. We employed visible and near infrared (NIR) spectroscopy on over 75,000 samples across several fruit and vegetable varieties. This yielded 0.90-0.98 and 0.98-0.99 classification accuracies for taxonomy and farmer classes, respectively. The most significant factors in the visible spectrum were variations in the produce color due to chlorophyll and anthocyanins. In the infrared spectrum, we observed that the varying water and sugar content levels were critical to obtaining high classification accuracies. High quality spectral data along with an optimal tuning of hyperparameters in the support vector machine (SVM) was also key to achieving high classification accuracies. In addition to demonstrating exceptional accuracies on test data, we explored insights behind the classifications, and identified the highest performing approaches using cross validation. We presented data collection guidelines, experimental design parameters, and machine learning optimization parameters for the replication of studies involving large sample sizes.
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Project description:Alzheimer's disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.
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Project description:The notion that linguistic forms and meanings are related only by convention and not by any direct relationship between sounds and semantic concepts is a foundational principle of modern linguistics. Though the principle generally holds across the lexicon, systematic exceptions have been identified. These "sound symbolic" forms have been identified in lexical items and linguistic processes in many individual languages. This paper examines sound symbolism in the languages of Australia. We conduct a statistical investigation of the evidence for several common patterns of sound symbolism, using data from a sample of 120 languages. The patterns examined here include the association of meanings denoting "smallness" or "nearness" with front vowels or palatal consonants, and the association of meanings denoting "largeness" or "distance" with back vowels or velar consonants. Our results provide evidence for the expected associations of vowels and consonants with meanings of "smallness" and "proximity" in Australian languages. However, the patterns uncovered in this region are more complicated than predicted. Several sound-meaning relationships are only significant for segments in prominent positions in the word, and the prevailing mapping between vowel quality and magnitude meaning cannot be characterized by a simple link between gradients of magnitude and vowel F2, contrary to the claims of previous studies.
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Project description:Sound symbolism refers to the non-arbitrary mappings that exist between phonetic properties of speech sounds and their meaning. Despite there being an extensive literature on the topic, the acoustic features and psychological mechanisms that give rise to sound symbolism are not, as yet, altogether clear. The present study was designed to investigate whether different sets of acoustic cues predict size and shape symbolism, respectively. In two experiments, participants judged whether a given consonant-vowel speech sound was large or small, round or angular, using a size or shape scale. Visual size judgments were predicted by vowel formant F1 in combination with F2, and by vowel duration. Visual shape judgments were, however, predicted by formants F2 and F3. Size and shape symbolism were thus not induced by a common mechanism, but rather were distinctly affected by acoustic properties of speech sounds. These findings portray sound symbolism as a process that is not based merely on broad categorical contrasts, such as round/unround and front/back vowels. Rather, individuals seem to base their sound-symbolic judgments on specific sets of acoustic cues, extracted from speech sounds, which vary across judgment dimensions.
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