The present study examined college and graduate students’ discrimination learning of variability of visual stimulus sets. Each stimulus set consisted of 16 black and white icons. The variability of icons was manipulated across six levels by adjusting the number of identical and different icons in each set. Participants were trained to discriminate between high variability sets (with entropy values of 2.5, 3.0, and 3.5) and low variability sets (with entropy values of 0.5, 1.0, and 1.5). 15 out of 16 participants attained a learning criterion of 80% correct within four sessions. They also showed full transfer of the discrimination learning to test sets consisting of novel icons. During the test sessions, response rates and reaction times significantly varied across the different entropy values within the same response categories (high or low). The results show that the discrimination of stimulus sets of entropy values of 1.5 and 2.5 were relatively difficult for the participants and that humans can discriminate variability among visual stimuli indexed by entropy values. The implications of the present findings on comparative studies of same/different discrimination learning in human and nonhuman animals were discussed.

Humans easily perceive the color of objects. The ‘surface color’ perception has been defined as a problem of recovering the physical reflectance of a surface from the image, and its mechanism has been examined using artificial stimuli with nearly flat and perfectly uniform reflectance. However, recent studies using a variety of real-world surfaces suggest that the visual system estimates the surface color based on low- and high-level image features, similar to object recognition. The present paper reviews the experimental and theoretical development of surface-color perception research, and discusses how the ecological validity and diversity of stimuli is important for the reproducibility and validity of psychological findings.

This paper on non-pharmacological therapies and dementia introduces specific cases illustrating how acoustic elements, including music therapy, can improve the quality of life for dementia patients and alleviate symptoms. It has been demonstrated that, in addition to pharmacological therapies, non-pharmacological therapies such as cognitive training, exercise therapy, reminiscence therapy, and music therapy are effective in treating dementia. Exercise, in particular, has been observed to reduce the incidence of dementia, while music therapy contributes to the improvement of behavioral and psychological symptoms. Moreover, interventions combining exercise and music have been shown to be effective in enhancing cognitive functions and maintaining daily living activities. Furthermore, a systematic review of the effects of music therapy on the behavioral and psychological symptoms of dementia suggests its usefulness. However, further research is needed to obtain stronger evidence. Given the global increase in dementia, there is a demand for the development of effective non-pharmacological therapies, with an emphasis on the research of complementary treatments such as music therapy.

With advances in cochlear implant technology, individuals with severe hearing loss can expect to achieve good speech perception skills with cochlear implants (CIs). Because of the biomechanical limitations of CI devices, however, CI users may experience some difficulty in decoding prosodic cues and tonal aspects of music. This paper summarizes evidence on perception and production of speech prosody and music in both normally hearing children and pediatric CI users. While future studies with rigorous methods are needed to determine causal effects of music training, evidence from recent studies suggests that pediatric CI users enjoy music and can benefit from musical experiences.

Reports of pure cases of amusia without the involvement of multiple, higher brain functions have led to the hypothesis that music perception is mediated by independent brain processes. Expressive amusia, defined as the loss of the ability to sing, is often caused by damage to the right temporal or parietal lobe. We report herein a case of expressive amusia with concomitant disorders of reading and writing music resulting from a right frontal lobe infarction. We created test of musical imagery as means of assessing “music concept representation,” which provided insights into the brain mechanisms involved in music perception.

The patterns of amplitude modulation (AM) are known to provide essential information for speech comprehension. Neurophysiological studies have shown that neurons in various brainstem nuclei respond selectively to modulation frequencies. Our modelling study suggests that the neural characteristics may have evolved as the auditory system is optimized for natural sound recognition. Temporal fine structure (TFS; the rapid oscillation of the sound waveform) is considered to play an essential role for pitch perception and sound localization. Sensitivity to TFS declines in individuals with sensorineural hearing loss and in older adults. Cochlear implants (CIs),which provide limited TFS information, often pose a challenge for pitch perception, although there are CI users with good pitch perception in some cases. The pitch sensitivity of CI users may depend on the development of efficient biological mechanism extracting pitch-related information that is not represented as a form of TFS. Understanding the plasticity of the central auditory nervous system may contribute to the solution of hearing-related problems.

Touch is a multisensory experience involving both thermal and mechanical inputs, which are essential for perceiving and interacting with the environment. Thermal and tactile sensations represent distinct modalities with fundamentally different spatial and temporal characteristics, yet they are integrated seamlessly to create a cohesive perceptual experience. This integration plays a critical role in object recognition and environmental awareness. This paper reviews the distinct properties of thermal and tactile sensations, exploring how these two modalities interact and integrate. Key findings related to their temporal and spatial integration are discussed, highlighting the brain’s ability to combine these inputs into a unified sensory experience. Understanding the mechanisms underlying this multisensory integration not only provides insights into human perception but also has potential implications for improving haptic technologies and enhancing virtual and remote interaction systems.

Awareness of the body is essential for accurate motor control. However, it is unclear how the awareness of the body influences motor control. Specifically, (i) how is the awareness of the body formed through multiple sensory sources of bodily signals; (ii) how are senses of body ownership and agency related to motor control; and (iii) how is the awareness of the body represented in the brain during motor actions. To address these issues, the present study used excellent psychophysical techniques such as optimal cue combination and eye-hand coordination. The present article introduces recent works that have been revealed using these techniques.

The present article is a summary of the Second Forum 2023 of the Japanese Psychonomic Society. The implicit social cognition research has revealed that our behavior and decision making are unconsciously influenced by social categories such as gender, age, race, nationality, and so on. In the forum, Professor Mahzarin R. Banaji of Harvard University, a world authority on implicit social cognition research, gave a lecture on the history and development of the research over the past half century. How did the research begin, how was it established, and how it collaborated with other fields? This will provide an overview of the implicit social cognition research.

Bayesian inference has been proposed as a computational priciple of the brain in many fields of perception and action. In particular, in the field of multisensory integration, researchers have shown that Bayesian causal inference (BCI), which enables the consideration of causal relationships between multiple stimuli, can explain experimental data successfully. However, fitting complex models such as BCI models to experimental data requires knowledge about formalizing and optimizing objective functions, which may be a substantial obstacle to entering the field for people who do not have sufficient experience in the computational field. In this article, we explain the methods and practices to fit Bayesian inference models to experimental data using a BCI model as an example. To make the example easier to understand, we also refer to actual Python code for model fitting, which we made publicly available. We also discuss how to modify and extend the code to different model formulations and experimental tasks.

Twenty-two years have passed since the establishment of the “Attention & Cognition” Research Meeting Group. The existence of the study group is now well known among people in related research fields, and it is called “A&C” for short. Since I have the opportunity to introduce the contents of the “A&C” Research Meeting Group now, I would like to reiterate the background of its establishment and its characteristics on behalf of the “A&C”.