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Oversampling of minority categories drives misperceptions of group compositions.

ABSTRACT: The ability to estimate proportions informs our immediate impressions of social environments (e.g., of the diversity of races or genders within a crowded room). This study examines how the distribution of attention during brief glances shapes estimates of group gender proportions. Performance-wise, subjects exhibit a canonical pattern of judgment errors: small proportions are overestimated while large values are underestimated. Subjects' eye movements at sub-second timescales reveal that these biases follow from a tendency to visually oversample members of the gender minority. Rates of oversampling dovetail with average levels of error magnitudes, response variability, and response times. Visual biases are thus associated with the inherent difficulty in estimating particular proportions. All results are replicated at a within-subjects level with non-human ensembles using natural scene stimuli; the observed attentional patterns and judgment biases are thus not exclusively guided by face-specific visual properties. Our results reveal the biased distribution of attention underlying typical judgment errors of group proportions.

SUBMITTER: Khaw MW

PROVIDER: S-EPMC8628853 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Oversampling of minority categories drives misperceptions of group compositions.

Khaw Mel W MW Kranton Rachel R Huettel Scott S

Cognition 20210508

The ability to estimate proportions informs our immediate impressions of social environments (e.g., of the diversity of races or genders within a crowded room). This study examines how the distribution of attention during brief glances shapes estimates of group gender proportions. Performance-wise, subjects exhibit a canonical pattern of judgment errors: small proportions are overestimated while large values are underestimated. Subjects' eye movements at sub-second timescales reveal that these b ...[more]

PMID: 33971528

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Project description:BackgroundCarbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington's disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins.ResultsIn this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversampling technique (SMOTE-KSU) were incorporated to balance the proportions of K, P, R, and T carbonylated training samples. Next, the integrated classifier system Rotation Forest uses "support vector machine" subclassifications to divide three types of feature spaces into several subsets. CarSite-II gained Matthew's correlation coefficient (MCC) values of 0.2287/0.3125/0.2787/0.2814, False Positive rate values of 0.2628/0.1084/0.1383/0.1313, False Negative rate values of 0.2252/0.0205/0.0976/0.0608 for K/P/R/T carbonylation sites by tenfold cross-validation, respectively. On our independent test dataset, CarSite-II yield MCC values of 0.6358/0.2910/0.4629/0.3685, False Positive rate values of 0.0165/0.0203/0.0188/0.0094, False Negative rate values of 0.1026/0.1875/0.2037/0.3333 for K/P/R/T carbonylation sites. The results show that CarSite-II achieves remarkably better performance than all currently available prediction tools.ConclusionThe related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http://47.100.136.41:8081/.

Project description:BackgroundDental caries is a microbe and sugar-mediated biofilm-dependent oral disease. Of particular significance, a virulent type of dental caries, known as severe early childhood caries (S-ECC), is characterized by the synergistic polymicrobial interaction between the cariogenic bacterium, Streptococcus mutans, and an opportunistic fungal pathogen, Candida albicans. Although cross-sectional studies reveal their important roles in caries development, these exhibit limitations in determining the significance of these microbial interactions in the pathogenesis of the disease. Thus, it remains unclear the mechanism(s) through which the cross-kingdom interaction modulates the composition of the plaque microbiome. Here, we employed a novel ex vivo saliva-derived microcosm biofilm model to assess how exogenous pathogens could impact the structural and functional characteristics of the indigenous native oral microbiota.ResultsThrough shotgun whole metagenome sequencing, we observed that saliva-derived biofilm has decreased richness and diversity but increased sugar-related metabolism relative to the planktonic phase. Addition of S. mutans and/or C. albicans to the native microbiome drove significant changes in its bacterial composition. In addition, the effect of the exogenous pathogens on microbiome diversity and taxonomic abundances varied depending on the sugar type. While the addition of S. mutans induced a broader effect on Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog abundances with glucose/fructose, S. mutans-C. albicans combination under sucrose conditions triggered unique and specific changes in microbiota composition/diversity as well as specific effects on KEGG pathways. Finally, we observed the presence of human epithelial cells within the biofilms via confocal microscopy imaging.ConclusionsOur data revealed that the presence of S. mutans and C. albicans, alone or in combination, as well as the addition of different sugars, induced unique alterations in both the composition and functional attributes of the biofilms. In particular, the combination of S. mutans and C. albicans seemed to drive the development (and perhaps the severity) of a dysbiotic/cariogenic oral microbiome. Our work provides a unique and pragmatic biofilm model for investigating the functional microbiome in health and disease as well as developing strategies to modulate the microbiome. Video Abstract.

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Oversampling of minority categories drives misperceptions of group compositions.

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Oversampling of minority categories drives misperceptions of group compositions.

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