Phenomena commonly referred to as illusions, fallacies, and cognitive biases have been extensively investigated as valuable tools for elucidating the characteristics of human perception and cognition. Nevertheless, since these phenomena are analyzed and utilized from an interdisciplinary standpoint, these terms are used for phenomena of different characteristics, leading to confusion regarding the definition of “illusion.” In this paper, the author introduces the concept of sakkaku-sei (cognitive multiplicity), which characterizes these phenomena. The author classifies these phenomena into three primary types: (i) multiple interpretations inside and outside a human cognitive system, (ii) multiple interpretations inside a single human cognitive system, and (iii) multiple interpretations between multiple human cognitive systems. Employing the proposed categorizations, approximately 40 types of illusions and cognitive biases phenomena are classified. This classification offers insights into what sense a specific phenomenon is regarded as illusions, fallacies, or cognitive biases, thereby facilitating an understanding of these phenomena from various perspectives. Moreover, the classification provides clues for system design intervening in human perception and cognition in domains such as human-computer interaction and virtual reality.

The neural basis of our language comprehension system has been explored using neuroimaging techniques, such as functional magnetic resonance imaging. Despite having identified brain regions and systems related to various linguistic information aspects, the entire image of a neurocomputational model of language comprehension remains unsolved. Contrastingly, in machine learning, the rapid development of natural language models using deep learning allowed sentence generation models to generate high-accuracy sentences. Mainly, this study aimed to build a method that reconstructs stimulus sentences directly only from neural representations to evaluate a neurocomputational model for understanding linguistic information using these text generation models. Consequently, the variational autoencoder model combined with pre-trained deep neural network models showed the highest decoding accuracy, and we succeeded in reconstructing stimulus sentences directly only from neural representations using this model. Although we only achieved topic-level sentence generation, we still exploratorily analyzed the characteristics of neural representations in language comprehension, considering this model as a neurocomputational model.

We often cheer for others in our daily lives, not only for our favorite celebrities but also for our family, friends, and even strangers. In actual cheering situations, some people cheer enthusiastically while others are less interested in cheering, indicating that there are individual differences in cheering tendencies. This study developed a cheering tendency scale to examine the factors of willingness to cheer for others. The exploratory factor analysis showed that the cheering tendency includes the following four factors: a) interest in a likable person, b) attunement to other observers, c) promoting a positive situation of others, and d) overcoming a negative situation of others. The result of Cronbach’s 𝛼 coefficient and correlation analysis with the related scales showed sufficient reliability and validity of our developed scale. Moreover, we conducted a cluster analysis, with the result that the respondents could be classified into the following four clusters: a) active cheering group, b) self-sufficient cheering group, c) passive cheering group, and d) non-cheering group. We conclude that the developed scale is sufficiently reliable and valid to characterize cheering behavior, personality traits, and the tendency to have a “fave” by cluster, which can be used for further cognitive studies.

This study aimed to explore young children’s vocabulary development using a machine learning technique, variational autoencoder (VAE). The VAE is an unsupervised neural network that maps high-dimensional input data onto a dimension-reduced latent space and then regenerates the data. The complex input features could be visualized in a low-dimensional latent space while maintaining its interpretability. We used parent-reported questionnaire data extracted from a publicly available database, involving American young children (𝑁= 5,520) and applied VAE. The two-dimensional latent space in the adopted model demonstrated that vocabulary development had a quasi-one-dimensional structure shaped by an arc. Its rotation and radial directions represented changes in total vocabulary size and individual differences, respectively. We found that some categories in the questionnaire (e.g., Sounds, Animals) were more likely to develop earlier in the outer path of the arc, whereas others (e.g., Action words, Pronouns) tended to develop predominantly in the inner path of the arc. Furthermore, a simulation case study using longitudinal data suggested that some specific lexical items were crucial in characterizing the universality and diversity of different developmental trajectories in the latent space. Our approach will contribute to quantitatively depicting the development of children’s vocabulary in a more fine-grained and nuanced manner, providing a synergetic bridge between machine learning and developmental science.

Schoolteachers have engaged in their daily teaching practices to achieve the global education goal of deepening students’ understanding. If they can classify students based on the depth of students’ understanding, they can effectively provide students with classroom instructions and learning strategies tailored to students’ specific needs. Such a classification can be achieved by a class of discrete latent variable models called a cognitive diagnostic model, which aims to evaluate students’ mastery states of some cognitive abilities and perform clustering based on the estimated mastery states. To apply this model to regular classroom tests, we specified a Q-matrix –which is required for data analysis with this model and determines which cognitive abilities are necessary to answer testitems– considering the depth of understanding with schoolteachers and using a Q-matrix estimation method. A discussion with schoolteachers indicated the potential usefulness of the estimated mastery states to grasp the tendency of students’ understanding in the classroom intuitively and easily.

In the field of HAI, the “model of others” is widely researched and used. In this paper, the author pointed out the problems of the “model of others” based research. These problems are as follows: inconsistency of real-world interaction, the problem about synchronous and asynchronous, and ethical problems. The author discusses these problems and suggests the prospect of updating the “model of others”.

After reading the commentary paper, the author became convinced that the model of others was unnecessary. This paper will discuss the unnecessary and dangerous nature of the model of others through a reply to the commentary paper.