Adults and toddlers systematically associate pseudowords such as "bouba" and "kiki" with round and spiky shapes, respectively, a sound symbolic phenomenon known as the "bouba-kiki effect". To date, whether this sound symbolic effect is a property of the infant brain present at birth or is a learned aspect of language perception remains unknown. Yet, solving this question is fundamental for our understanding of early language acquisition. Indeed, an early sensitivity to such sound symbolic associations could provide a powerful mechanism for language learning, playing a bootstrapping role in the establishment of novel sound-meaning associations. The aim of the present meta-analysis (SymBouKi) is to provide a quantitative overview of the emergence of the bouba-kiki effect in infancy and early childhood. It allows a high-powered assessment of the true sound symbolic effect size by pooling over the entire set of 11 extant studies (six published, five unpublished), entailing data from 425 participants between 4 and 38 months of age. The quantitative data provide statistical support for a moderate, but significant, sound symbolic effect. Further analysis found a greater sensitivity to sound symbolism for bouba-type pseudowords (i.e., round sound-shape correspondences) than for kiki-type pseudowords (i.e., spiky sound-shape correspondences). For the kiki-type pseudowords, the effect emerged with age. Such discrepancy challenges the view that sensitivity to sound symbolism is an innate language mechanism rooted in an exuberant interconnected brain. We propose alternative hypotheses where both innate and learned mechanisms are at play in the emergence of sensitivity to sound symbolic relationships.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/desc.12659 | DOI Listing |
J Acoust Soc Am
January 2025
Department of Apparel and Space Design, Kyoto Women's University, Kyoto, Kyoto 605-8501, Japan.
Ever since de Saussure [Course in General Lingustics (Columbia University Press, 1916)], theorists of language have assumed that the relation between form and meaning of words is arbitrary. However, recently, a body of empirical research has established that language is embodied and contains iconicity. Sound symbolism, an intrinsic link language users perceive between word sound and properties of referents, is a representative example of iconicity in language and has offered profound insights into theories of language pertaining to language processing, language acquisition, and evolution.
View Article and Find Full Text PDFJ Exp Child Psychol
January 2025
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, LMU University Hospital, LMU Munich, 80336 München, Germany.
Early spelling depends on the ability to understand the alphabetic principle and to translate speech sounds into visual symbols (letters). Thus, the ability to associate sound-symbol pairs might be an important predictor of spelling development. Here, we examined the relation between sound-symbol learning (SSL) and early spelling skills.
View Article and Find Full Text PDFNoise Health
January 2025
Department of Neurology, Faculty of Medicine, Ondokuz Mayis University, Samsun, Turkey.
Background: Patients with multiple sclerosis (MS) experience difficulties in understanding speech in noise despite having normal hearing.
Aim: This study aimed to determine the relationship between speech discrimination in noise (SDN) and medial olivocochlear reflex levels and to compare MS patients with a control group.
Material And Methods: Sixty participants with normal hearing, comprising 30 MS patients and 30 healthy controls, were included.
Entropy (Basel)
December 2024
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Can we turn AI black boxes into code? Although this mission sounds extremely challenging, we show that it is not entirely impossible by presenting a proof-of-concept method, MIPS, that can synthesize programs based on the automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on a benchmark of 62 algorithmic tasks that can be learned by an RNN and find it highly complementary to GPT-4: MIPS solves 32 of them, including 13 that are not solved by GPT-4 (which also solves 30). MIPS uses an integer autoencoder to convert the RNN into a finite state machine, then applies Boolean or integer symbolic regression to capture the learned algorithm.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Mathematics and Statistics, College of Science, Taif University, 11099, 21944, Taif, Saudi Arabia.
In this work, we use the ansatz transformation functions to investigate different analytical rational solutions by symbolic computation. For the (2+1)-dimensional Calogero-Bogoyavlenskii Schiff (CBS) model, we derive a variety of rational solutions, such as homoclinic breather solutions (HBs), M-shaped rational solutions (MSRs), periodic cross-rationals (PCRs), multi-wave solutions (MWs), and kink cross-rational solutions (KCRs). Their dynamic is shown in figures by selecting appropriate values for the pertinent parameters.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!