Date：2025/04/30 (Wed)
Venue：N100, Department of Psychology
14:30-15:00
Speaker：Yi-Hsuan Wen
Title：How Does Voice Endorsement Trigger Employee Voice in the Chinese Workplace? A Dual-Pathway Model
Abstract：
While previous research has mainly focused on the antecedents of managerial voice endorsement, little attention has been given to how voicers respond to managerial voice endorsement through subsequent different types of voice in the context of Chinese supervisor-subordinate relationships. Based on the Chinese cultural system, we integrate social exchange theory and power-dependence theory and propose that supervisors' voice endorsement initiates two relational pathways – affective pathway and power pathway – that enhance subordinates’ subsequent supportive and challenging voice, respectively. We also suggest that supervisor-subordinate guanxi can serve as the base of interpersonal interactions, moderating the two relational pathways. The results from a time-lagged multisource field study with 201 supervisor-subordinate dyads mostly support our hypothesized model. Regarding the affective pathway, our results indicate that voice endorsement enhances subordinates’ affective connection toward supervisors, which, in turn, increases future supportive voice. As for the power pathway, voice endorsement promotes subordinates’ sense of power, which encourages them to exert challenging voice. Besides, personal-life inclusion exhibits a substitution effect rather than the expected strengthening effect. Our research demonstrates that voice endorsement by supervisors activates different psychological mechanisms, influencing the ways in which employees interact with and respond to their supervisors in a relational context.
15:00-15:30
Speaker：Chien-Chun Yang
Title：From Brightness to Faces: What We Perceive in a Split-Second Summary
Abstract：
Humans can rapidly form ensemble representations by extracting statistical information—such as the average—across sets of visual stimuli, even at a single glance. For instance, individuals can accurately perceive the average emotional expression of a crowd, despite being unable to recall any specific face from the display. However, the methodologies commonly used to assess ensemble perception often parallel those designed for evaluating individual elements. Critically, there is a lack of standardized units and metrics for comparing performance across different visual features. This lack of a unified measurement framework across various visual features poses challenges in isolating and comparing the fundamental mechanisms of ensemble processing across different levels of the visual hierarchy. To address this issue, we normalized performance in feature-mean extraction relative to that in single-object reproduction. Based on this normalization, we developed ensemble indices that captured accuracy, precision, and spatial weighting. These indices allowed us to assess what aspects of performance were enhanced or diminished during ensemble integration, and to compare them across visual features at different hierarchical levels. Five experiments employing an adjustment paradigm were conducted to examine ensemble processing across different visual features of multiple objects: (1) brightness, (2) size, (3) shape, (4) perceived facial age, and (5) perceived facial masculinity. The results revealed clear distinctions in ensemble processing between lower-level visual features (brightness and size) and higher-level features (shape, perceived facial age, and perceived facial masculinity). For lower-level features, participants integrated information from all displayed objects with relatively uniform weighting; however, their mean estimations were systematically biased during integration—brightness was underestimated, whereas size was overestimated. In contrast, ensemble perception of higher-level features showed a central-location bias, with greater weight assigned to centrally located items, and resulted in surprisingly accurate mean estimations. Notably, estimation precision was impaired for lower-level ensemble formation but improved for facial feature ensembles. Collectively, these findings suggest that ensemble processing varies along the visual hierarchy in terms of its weighting, biases, and precision during integration. This variation implies the involvement of distinct mechanisms for computing ensemble representations at different levels of the visual processing hierarchy.