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Ideophones are characterized by sound symbolism, a non-arbitrary relationship between speech sounds and meaning, which is observed across languages worldwide. Despite the increasing interest in sound symbolism and ideophones in cognitive science, Japanese ideophone research has lacked a robust quantitative approach. This paper proposes a dictionary-based method to quantify the sound-symbolic system of Japanese ideophones and presents statistical results that support and extend previous findings. The proposed method aims to advance the objective and comprehensive analysis of ideophones, sound symbolism, and iconicity, offering valuable contributions to ongoing discussions in the cognitive sciences.

CogSci 2025

A Dictionary-based Quantitative Analysis of the Sound-symbolic System of Japanese Ideophones

semantics of language

embodied cognition

statistics

psychology

linguistics

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

poster

In this study, we developed a computational implementation for a model of metaphor comprehension based on the theory of indeterminate natural transformation (TINT) proposed by Fuyama et al. We simplified the algorithms implementing the model to be closer to the original theory and verified it through data fitting and simulations. For details, we proposed a method to replace the deterministic operation of existing model with a probabilistic (softmax) operation. The outputs of the algorithms are evaluated with three measures: data-fitting with experimental data, the size of the correspondence of the metaphor comprehension result, and the novelty of the comprehension (i.e. the correspondence of the associative structure of the source and target of the metaphor). The improved algorithm outperformed the existing one in all the three measures. We suggest that the metaphor comprehension process in humans is based on more probabilistic procedure.

Computational Implementation of a Model of Category-Theroretic Metaphor Comprehension

This study examines scaffolding techniques to support students’ analogical comparisons with science images. Analogical comparison involves identifying deep relational structure over superficial similarities, which can be challenging without guidance. Across experiments, participants compared analogous evolutionary science images with or without various scaffolds: describing the relation, completing a mapping table, and spatial support for alignment. Results demonstrated that describing the relation and completing a mapping table, especially in combination, significantly enhanced scientific interpretations of the images. However, spatial support was ineffective. The findings highlight practical strategies for improving conceptual learning from science visuals.

Scaffolding to Support Analogical Comparisons with Science Images

Detecting dynamic spatial relationships, such as pursuit, can be cognitively demanding (Scholl & Gao, 2013). Research has identified visual cues that influence pursuit detection, including the distance between the pursuer and the target (Meyerhoff, Schwan & Huff, 2014) and the number of objects in a scene (Gao, Baker, et al., 2019; Kon, Khemlani & Lovett, 2024). We turn to data visualization research to explore techniques to improve pursuit detection in dynamic scenes. For example, displaying trajectory histories can aid processing by reducing cognitive load (Heer & Robertson, 2007). We conducted a study in which participants viewed visualizations of six moving dots, either with or without trace lines, and determined whether one dot was chasing another. Preliminary findings suggest that trace lines improve the speed and accuracy of pursuit detection. Our results bridge visualization and vision science, suggesting that trace lines might enhance pursuit detection by providing less transient shape-based visual cues.

Visualizing Motion Traces Enhances Pursuit Detection in Dynamic Scenes

Children with Developmental Language Disorder (DLD) exhibit longer reaction times (RTs) than age-matched neurotypical children. Drift diffusion models estimate parameters influencing RT distributions: drift-rate represents speed of information accumulation, non-decision time represents other factors contributing to longer RTs, e.g., poor attention or motor coordination. Using a hierarchical Bayesian framework, we modeled RT data from visual search and mental rotation tasks completed by 3rd graders (N = 248). Children with impaired verbal abilities without accompanying nonverbal impairment (DLD) and those with global verbal/nonverbal impairments were compared to neurotypical children. Across tasks, children with DLD exhibited a lower drift rate than neurotypical children, indicating slower information accumulation, and no difference in non-decision time. Children with global impairments showed lower drift rates and higher non-decision times than neurotypical children. Results suggest directions for nuanced tests of the generalized slowing hypothesis of DLD. Clinical implications for diagnosis and treatment of DLD are discussed.

Modeling Processing Speed in Developmental Language Disorder using Drift Diffusion Modeling

The Transformer architecture used in LLMs has garnered
widespread attention due to these model’s human-like
conceptual knowledge and language understanding, yet
understanding how these models’ capabilities result from
experience-guided learning, and connecting this learning
process with the structure in their training data, can seem
intractable. Here we present preliminary steps to
characterizing the developmental trajectory of a minimal
Transformer trained on a next-token prediction task, using a
simple dataset with quantifiable uncertainty and a simple,
intuitively characterizable structure that captures some aspects
of natural semantic structure learned by LLMs from large
datasets. We show how the dynamic learning process of this
model is a predictable consequence of the structure of the
training data, exhibiting attested features of human semantic
development, as captured in a theory of neural network
learning dynamics (Saxe et. al. 2019) previously used to
capture such dynamics in a network originally introduced by
Rumelhart & Todd (1993).

Extending a Mathematical Theory of the Emergence of Knowledge from the Experience to Capture Learning Dynamics in Transformers 

This study explores the role of coherence-based reasoning in belief updating within uncertain environments. We develop a novel computational model where agents update their beliefs based on observed evidence, with some evaluating the coherence of their belief set before accepting new evidence. Our results show that coherence-based evidence filtering improves belief accuracy in noisy environments and when agents' prior beliefs are accurate. However, when agents encounter systematically misleading evidence, coherence considerations lead to less accurate beliefs. These findings shed light on how coherence interacts with evidence quality and belief accuracy.

Coherence-Based Evidence Filtering: A Computational Exploration

In the studies of aesthetic appreciation, poetry has received less attention than other art forms.Furthermore, although a number of recent studies have focused on the factors that influence poetry appreciation, very few attempts have been made to explain the appreciation process.In this study, to explore the cognitive process of poetry appreciation, we propose a computational model of poetry appreciation based on the incongruity resolution theory, which has been proposed to explain the process of evoking poetic effects.Assuming that poetry reading comprehension is a process of evoking mental imagery from words in a poem, we model it as spreading activation in a semantic network.The notion of incongruity resolution is then modeled as the difference of the diversity of activation in the network. The computational model was tested using multiple regression analysis with simulation results as independent variables and human ratings for aesthetic appeal as a dependent variable. The analysis demonstrated that the degree of incongruity resolution computed by the model accounted for a significant portion of the variance in aesthetic appeal. This finding provides evidence for the validity of the model and suggests its potential for a more comprehensive model of poetic appreciation in literary works.

A computational model of poetry appreciation based on a spreading activation network and the incongruity resolution theory

Bilinguals perceive events differently in their native (L1) and second languages (L2; e.g., Dylman & Bjärtå, 2019). A possible explanation for this “foreign language effect” is that L2 makes the described event feel less personal or emotional, leading to more analytical thinking. Prior research has (1) focused predominantly on negative emotions, and (2) not considered individual differences in how relatable an event feels, potentially modulating emotional intensities. This study tests both positive and negative events and how relatable each event feels. Participants, randomly assigned to the L1 or L2 condition, read 8 happy and 8 sad stories, and rated the emotion they felt for each story and its similarity to their personal experience. Preliminary descriptive results (N=17) suggest that emotion is felt more intensely in L1, regardless of valence. With the final dataset, we will examine whether Language, Valence, and Relatability independently and interactively predict the felt emotion.

Experiencing Positive and Negative Emotions in L1 vs. L2

This study investigates how stress and anxiety influence the interplay between visual working memory and decision-making in human participants. Using the Socially Evaluated Cold Pressor Test (SECPT) to induce acute stress, we examined cognitive performance on a computerized behavioral paradigm (the Marble Jar Task) requiring the storage, manipulation, and utilization of visual information. Results revealed that while experimentally induced stress did not significantly affect overall accuracy, higher self-reported state anxiety was correlated with both lower decision-making accuracy and poorer visual memory performance. Interestingly, higher state anxiety was also correlated with increased attention towards high-value outcomes in decision-making. This work highlights the importance of understanding how stress and anxiety affect the interaction between interconnected cognitive functions, rather than studying isolated cognitive phenomena.

Examining the Influence of Stress and Anxiety on Visual Working Memory and Decision-Making

Grammaticalization is the process by which a lexical item (e.g., noun) acquires a more functional role (e.g., preposition) over time. Grammaticalization is considered largely unidirectional, that is, change from functional to lexical is far less common (Hopper & Traugott, 2003). What is the cause of this unidirectionality? Our experiment tests whether individuals have a preference in the direction of grammaticalization when performing semantic extension in communication. We focus on the phenomenon of using body part nouns as a source of spatial prepositions. We predicted that participants extending body parts to use as prepositions would find the task easier and more intuitive than participants extending prepositions to use as body parts. However, our results show no directional bias, indicating that the historical unidirectional tendency for body parts to be used as spatial relations does not originate in a bias that individuals have for using one to refer to the other.

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Next from CogSci 2025

Computational Implementation of a Model of Category-Theroretic Metaphor Comprehension

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