The social support provided by chatbots is typically designed to mimic the way humans support others. However, individuals have more conflicting attitudes toward chatbots providing emotional support (e.g., empathy and encouragement) compared to informational support (e.g., useful information and advice). This difference may be related to whether individuals associate a certain type of support with the realm of the human mind and whether they attribute human-like minds to chatbots. In the present study, we investigated whether perceiving human-like minds in chatbots affects users’ acceptance of various support provided by the chatbot. In the experiment, the chatbot posed questions about participants’ interpersonal stress events, prompting them to write down their stressful experiences. Depending on the experimental condition, the chatbot provided two kinds of social support: informational support or emotional support. Our results showed that when participants explicitly perceived a human-like mind in the chatbot, they considered the support to be more helpful in resolving stressful events. The relationship between implicit mind perception and perceived message effectiveness differed depending on the type of support. More specifically, if participants did not implicitly attribute a human-like mind to the chatbot, emotional support undermined the effectiveness of the message, whereas informational support did not. The present findings suggest that users’ mind perception is essential for understanding the user experience of chatbot social support. Our findings imply that informational support can be trusted when building social support chatbots. In contrast, the effectiveness of emotional support depends on the users implicitly giving the chatbot a human-like mind.

Head movements can provide a significant amount of information in communication. This study investigated the social capabilities of a robot’s head interaction, including the direction, movement, and timing of the movement. Using a newly designed robot, which has a minimal head movement mechanism in the three axes of x (pitch), y (roll), and z (yaw), we explored the participants’ perception of the robot’s head movements (i.e., nodding, shaking, and tilting) and movement timing (i.e., head movement prior to utterance and head movement simultaneous with utterance). The results revealed that head movements of the robot increased participants’ perceptions of likeability, anthropomorphism, animacy, and perceived intelligence of the robot compared with the non-movement condition. When the robot performed head movement prior to utterance, the rating of perceived naturalness was higher compared with that when the robot’s head movement occurred simultaneously with utterance. The findings imply that by implementing the features of head movements and movement timings, even simple robots that lack humanoid features can achieve better social interactions with humans.

In the present study, we examined age-related differences in utilizing learned associations to guide visual search. Participants viewed an object cue that was associated with 1 or 2 target colors, followed by a search array. Older adults showed slower RTs, larger visual search slopes, and reduced cue–target association knowledge than did younger adults. We also found increased search RTs and higher error rates when the cue was associated with 2 colors instead of 1. However, visual search slopes did not vary with the number of associated colors. This indicates that participants could activate multiple templates simultaneously to guide the search or retrieve associative information directly from the long-term memory. Furthermore, regression analyses showed that VWMC can predict both visual search performances and cue–target association knowledge. More broadly, our results demonstrate that VWMC can modify the effects of age on cued visual search performance.