Prior research finds that hierarchical representation-the vertical distribution of minorities across a hierarchy-can influence team attractiveness. Extending these findings, we offer a novel account for why these perceptions arise: teams with minorities clustered in low-ranking positions are perceived as less diverse and more conflict-prone than equally diverse teams with hierarchical representation. Across five studies (N = 2946), participants perceived teams with low hierarchical representation as less attractive than teams with hierarchical representation, regardless of participant race. Teams with low hierarchical representation were considered just as unattractive as teams with lower numerical diversity (Study 2). Individuals also underestimated the percentage of Black employees present in teams with low hierarchical representation, signaling a "diversity deflation" effect (Study 3). Conversely, teams with hierarchical representation were considered as attractive as diverse teams with flatter hierarchies (Study 4). The effect of hierarchical representation on attractiveness weakens for teams portrayed as conflict-laden (Study 5).
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1177/01461672241287581 | DOI Listing |
Sci Rep
January 2025
College of Computer Sciences, Anhui University, Hefei, 230039, China.
Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 131 Dong'an Road, 200032 Shanghai, China.
Proteins can be represented in different data forms, including sequence, structure, and surface, each of which has unique advantages and certain limitations. It is promising to fuse the complementary information among them. In this work, we propose a framework called ProteinF3S for enzyme function prediction that fuses the complementary information across protein sequence, structure, and surface.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America.
Characterizing neuronal responses to natural stimuli remains a central goal in sensory neuroscience. In auditory cortical neurons, the stimulus selectivity of elicited spiking activity is summarized by a spectrotemporal receptive field (STRF) that relates neuronal responses to the stimulus spectrogram. Though effective in characterizing primary auditory cortical responses, STRFs of non-primary auditory neurons can be quite intricate, reflecting their mixed selectivity.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, United States.
In plant breeding and genetics, predictive models traditionally rely on compact representations of high-dimensional data, often using methods like Principal Component Analysis (PCA) and, more recently, Autoencoders (AE). However, these methods do not separate genotype-specific and environment-specific features, limiting their ability to accurately predict traits influenced by both genetic and environmental factors. We hypothesize that disentangling these representations into genotype-specific and environment-specific components can enhance predictive models.
View Article and Find Full Text PDFSci Rep
December 2024
School of Computer Science and Technology, Xinjiang University, Urumqi, 830017, China.
Event Causality Identification (ECI) aims to predict causal relations between events in a text. Existing research primarily focuses on leveraging external knowledge such as knowledge graphs and dependency trees to construct explicit structured features to enrich event representations. However, this approach underestimates the semantic features of the original input sentences and performs poorly in capturing implicit causal relations.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!