Biology subjects

Kumar, G. V.

Publications and source records attributed to Kumar, G. V..

2 recordsLinked to original sources

bioRxiv2026

DECODING HOW THE SOUNDS OF WORDS AND PSEUDOWORDS SIGNIFY SHAPE: AN fMRI STUDY

Iconicity refers to systematic links between word form and meaning. Although evidence for iconicity in natural language continues to grow, its neural basis remains unclear. Using functional magnetic resonance imaging (fMRI) and multivariate pattern analysis (MVPA), we examined iconic shape associations of auditory real words and pseudowords. The pseudowords were matched to the real words in phonemic and phonotactic properties, while differing primarily in the absence of learned semantic representations. Participants listened to each item and judged whether it sounded rounded or pointed. Searchlight MVPA revealed significant decoding for both stimulus types. For real words, iconic shape associations were decoded above chance in regions associated with visual and haptic shape processing (left lateral occipital complex and left anterior intraparietal sulcus), visual imagery (bilateral precuneus), phonological processing (bilateral supramarginal gyri), and semantic processing (left middle frontal and right superior frontal gyri). For pseudowords, significant decoding was found in regions associated with multisensory feature organization (right posterior intraparietal sulcus) and language processing (left angular and inferior frontal gyri). Together, these findings provide evidence for neural mechanisms mediating iconic associations, with language-related areas involved for both real words and pseudowords, and visual processing for real words.

neuroscience↗

bioRxiv2024

Acoustic parameter combinations underlying mapping of auditory pseudoword sounds to multiple domains of meaning: a machine learning approach

In spoken language, iconicity, referring to the resemblance between the sound structure of words and their meaning, is often studied using pseudowords. Previously, we showed that representational dissimilarity matrices (RDMs) of the shape ratings of pseudowords correlated significantly with RDMs of acoustic parameters reflecting spectro-temporal variations; the ratings also correlated significantly with voice quality parameters. Here, we examined how perceptual ratings relate to these parameters of pseudowords across eight meaning domains. We largely replicated our previous findings for shape, while observing different patterns for other domains. Using a k-nearest-neighbor (KNN) machine-learning algorithm, we compared 4095 combinations of twelve acoustic parameters (3 spectro-temporal and 9 characterizing vocal quality) to determine the optimal combination associated with iconicity ratings in each domain. We found that iconic mappings were linked to domain-specific combinations of acoustic parameters. One spectro-temporal parameter, the fast Fourier transform, contributed to all domains, indicating the importance of time-varying spectral properties for iconicity judgments. We applied the KNN approach to generate shape ratings for 160 real words. These generated ratings strongly correlated with perceptual ratings of real words, indicating the value of the KNN approach to assess iconic mapping in natural languages. Our findings support the relevance of iconicity to language.

neuroscience↗