Affective communication, communicating with emotion, during face-to-face communication is critical for social interaction. Advances in artificial intelligence have made it essential to develop affective human–virtual agent communication. A person’s belief during human–virtual agent interaction that the agent is a computer program affects social-cognitive processes. Whether this belief interferes with affective communication is an open question. We hypothesized that the imitation of a positive emotional expression by a virtual agent induces a positive emotion, regardless of the belief. To test this hypothesis, we conducted an fMRI study with 39 healthy volunteers, who were made to believe that a virtual agent was either a person or a computer. They were instructed to smile, and immediately afterwards, the virtual agent displayed a positive, negative, or neutral expression. The participants reported a positive emotion only when their smile was imitated by the agent’s positive expression regardless of their belief. This imitation activated the participants’ medial prefrontal cortex and precuneus, which are involved in anthropomorphism and contingency, respectively. These results suggest that a positive congruent response by a virtual agent can overcome the effect of believing that the agent is a computer program and thus contribute to achieving affective human–virtual agent communication.

Functional near-infrared spectroscopy (fNIRS) is an imaging technique that noninvasively measures the product of cerebral hemodynamics (concentration changes of oxygenated and deoxygenated hemoglobin; $\mathrm{\Delta }{C}_{{\mathrm{O}}_2\mathrm{Hb}}$ and $\mathrm{\Delta }{C}_{\mathrm{HHb}}$) and optical path length ($L$), using light in the near-infrared spectrum (650 to 900 nm).¹ 2. ^{– 3} Because hemodynamic changes are related to local neuronal activity through neurovascular coupling,^4,5 fNIRS is commonly used to interpret brain activity and function. fNIRS has been widely used in research, clinical, and educational purposes⁶ due to its cost-effectiveness, safety, flexibility, higher spatial resolution than electroencephalography, and better temporal resolution than functional magnetic resonance imaging (fMRI).⁷ fNIRS systems are more compact than fMRI, which enables practical and continuous bedside monitoring even in infants and young children.⁸ 9. ^{– 10} fNIRS provides better motion tolerance; therefore, fNIRS is suitable for examining the challenging patients with restless symptoms^11,12 and subjects actively engaging in movement such as walking and running.^13,14 Furthermore, cochlear implant patients can safely undergo fNIRS measurement because there is no magnetic field that may endanger patients.^15,16

Functional near-infrared spectroscopy (fNIRS) noninvasively measures the product of concentration changes of cerebral hemoglobin ($\text{oxygenated}/{\mathrm{O}}_2\mathrm{Hb}$ and deoxygenated/HHb) and optical length using two or more near-infrared spectra (650 to 950 nm).^{1 – 4} It measures blood-related signals that indirectly correspond to brain activation (i.e., neurovascular coupling theorem).^5,6 After $>20$ years of development,⁷ fNIRS has gained much attention in broad applications including studies of neuropsychiatry and cognition in infants and children.^{8 – 10} Compared to other functional imaging techniques, it shows significant advantages in terms of system flexibility (without confinement or head restrainers) and motion tolerability since probes are tightly adhered on the scalp.^{11 – 13} However, pediatric studies have shown that infants and children sometimes become restless, thus causing probe detachment and unavoidable occurrence of motion artifacts. As the name implies, this is especially true for studies of children with attention deficit/hyperactivity disorder (ADHD).

Online meetings have become increasingly prevalent, especially during the coronavirus disease 2019 pandemic. Although they offer convenience and effectiveness in various contexts, there is a pertinent question about whether they truly replicate the richness of in-person communication. This study delves into the distinctions between online and face-to-face interactions, with a particular focus on the synchronization of brain activity. Previous research has indicated a connection between synchronization and the quality of communication. Therefore, our hypothesis posits that face-to-face interactions lead to greater brain synchronization compared to online interactions, which often lack certain social cues. To investigate this, we conducted a study using functional near-infrared spectroscopy hyperscanning during an eye-contact task involving 28 male participants organized into 14 pairs. We assessed brain signal synchronization using wavelet coherence analysis. After comparing face-to-face and online conditions, our findings revealed significantly higher synchronization in face-to-face scenarios, particularly within the right temporoparietal region. These results align with the outcomes of other hyperscanning studies and suggest that face-to-face communication elicits a higher level of brain synchronization compared with online communication. In the future, this approach holds promise for evaluating the effectiveness of online meeting tools in achieving a more authentic virtual communication experience.

Fine motor skills have been suggested to be related to human cognitive abilities. To develop an objective method for evaluating fine motor skills, we applied a flexible tactile sensor based on electrical impedance tomography (EIT) and the contact resistance principle to a cylinder designed to mimic the peg used in the Functional Dexterity Test. Six pinching motions were classified to confirm the feasibility of the prototype system. Two types of classification were performed: classification using reconstructed images and classification using measured voltage vectors. The feasibility of the classification method was evaluated using adult participants, and it was demonstrated that the system can accurately classify various types of pinching motions. The results revealed that utilizing reconstructed images for classification achieved a classification accuracy of 79.4%, while employing measured voltage vectors for classification resulted in a classification accuracy of 91.4%. These findings underscore the potential for developing an automated finger motion analysis system using EIT-based tactile sensor.