Category: All

We report a new publication by Bonnie, Youngah, Arthur, and John in Topics in Cognitive Science. The paper titled “An expanded model for perceptual norming: Insights from Japanese ideophones.” It investigates how sensory experience is encoded in Japanese ideophones, a class of vivid words often described as sound‑symbolic.

Using perceptual strength ratings across 13 sensory dimensions, the study moves beyond the traditional five‑ or six‑sense model. The results show that so‑called visual dominance is driven mainly by movement, while other visual properties such as shape and light or colour behave differently and connect to other senses in distinct ways. For example, movement patterns with sound and internal bodily sensations, shape with touch, and colour with taste and smell. The study also finds meaningful structure within interoception, with pain separating from emotions and bodily feelings.

Overall, the findings demonstrate that finer‑grained sensory models reveal cross‑modal relationships that are hidden in coarser approaches, highlighting the value of expanded perceptual norming for the study of iconicity and meaning.

McLaren, B., Do, Y., Thompson, A.L., & Husman, J. (2026). An expanded model for perceptual norming: Insights from Japanese ideophones. Topics in Cognitive Science. open_in_new DOI

We are pleased to share a new publication from Ivy, Frank and Youngah in the Journal of Experimental Psychology: Learning, Memory, and Cognition. The paper, titled “Modeling the impact of prenatal audio attenuation on speech sound learning,” examines how human infants appear to have substantial knowledge of the sound structure of their native language at birth, despite the fact that the uterine environment strongly limits auditory input to low-frequency sounds.

The study explores whether this prenatal low-frequency exposure may actually support later speech sound learning rather than hinder it. To address this question, the authors trained neural network models in two stages designed to simulate prenatal and postnatal learning. During the prenatal stage, models were exposed to speech that was either naturally low-pass filtered, artificially high-pass filtered, or unfiltered. After birth, all models were trained on full-frequency speech. Three different neural network architectures were examined, including a long short-term memory network, a convolutional neural network, and a residual neural network, to test whether the effects generalised across learning systems.

Across architectures, the results showed that prenatal exposure to low-frequency speech led to faster and more effective phonetic learning once full-frequency input became available. In contrast, exposure to high-frequency–only speech was less beneficial during prenatal learning. These findings suggest that the low-frequency sounds available before birth may provide a useful foundation that helps infants extrapolate to the richer speech input they encounter after birth, offering a computational explanation for early speech sound knowledge.

Zheng, S., Tan, F. & Do, Y. (2026). Modeling the impact of prenatal audio attenuation on speech sound learning. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication. open_in_newDOI

We are pleased to announce the publication of a new paper by Frank Lihui Tan and Youngah Do in the journal Linguistics Vanguard. The paper, titled “Attention-LSTM autoencoder simulation for phonotactic learning from raw audio input,” explores a novel approach to phonotactic learning using an attention-based long short-term memory (LSTM) autoencoder trained on raw audio input.

Unlike previous models that rely on abstract phonological representations, this study simulates early phonotactic acquisition stages by processing continuous acoustic signals. The research focuses on an English phonotactic pattern, specifically the distribution of aspirated and unaspirated voiceless stops. The model implicitly acquires phonotactic knowledge through reconstruction tasks, demonstrating its ability to capture essential phonotactic relations via attention mechanisms. The findings suggest that the model initially relies heavily on contextual cues to identify phonotactic patterns but gradually internalizes these constraints, reducing its dependence on specific phonotactic cues over time.

This study provides valuable insights into both computational modeling and infants’ phonotactic acquisition, highlighting the feasibility of early phonotactic learning models based on raw auditory input.

Tan, F. & Do, Y. (2025). Attention-LSTM autoencoder simulation for phonotactic learning from raw audio input. Linguistics Vanguard. open_in_newDOI