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Stereotypes have been shown to result in inaccurate or biased judgments of a person’s true skill or ability, as previously found in teacher evaluations of a student (Bonefeld & Dickhauser, 2018; Quinn, 2020; Glock, Krolak-Schwerdt, & Pit-ten Cate, 2015; Wenz & Hoenig, 2020) as well as in correspondence tests in job applications (Zchirnt & Ruedin, 2016). This has been found in situations where evaluators are making evaluations that have no concrete rule-based criterion, meaning additional and unrelated information, like stereotypes, can affect said evaluation. The purpose of the current research project is to assess whether said relationship between stereotypes and non-rule-based criterion appears in the peer evaluation context.

Additional file 1: Figure S1. Characterization of iBMEC phenotype following differentiation in 1 mL or 2 mL of medium. Figure S2. TEER and permeability measurements of 2D iBMEC confluent monolayers. Figure S3. Media volume effect on phenotype of 2D iBMEC confluent monolayers. Figure S4. Details of bulk RNA sequencing and iPC characterization. Figure S5. Benchmarking iBMEC gene expression to brain microvessel, endothelial, and epithelial datasets. Figure S6. Abundance measurements of endothelial and epithelial transcripts across cell sources and model types. Figure S7. Images of angiogenic sprouts across models. Figure S8. Assessment of tight junction dynamics in confluent iBMEC monolayers. Figure S9. Time course of morphological metrics across cell types. Figure S10. Violin plots of metrics across cell types. Figure S11. Response of three-dimensional iBMEC microvessels to wound formation by laser ablation. Figure S12. Comparison of morphological metrics between homeostasis, ablation, and melittin exposure. Figure S13. Monolayer area dynamics during wound healing and melittin exposure. Table S1. Summary of bulk RNA transcriptomes used in this study. Table S2. Antibodies used in this study. Note S1. Validation of media volume effect on TEER. Note S2. Variability of iBMEC differentiation. Note S3. Mechanisms of media volume effect on iBMEC phenotype. Note S4. Calculation of permeability in 2D and 3D. iBMEC differentiation. Transwell barrier characterization. Oxygen and glucose recordings. Pericyte differentiation and characterization.

Deep convolutional neural networks (DCNNs) have been used to model the ventral visual stream. However, there have been relatively few computational models of the dorsal visual stream, preventing a wholistic understanding of the human visual system. Additionally, current DCNN models of the ventral stream have shortcomings (such as an over-reliance on texture data) which can be ameliorated by incorporating dorsal stream information. The current study aims to investigate two questions: 1) does incorporating action information improve computational models of the ventral visual system? 2) how do the ventral and dorsal streams influence each other during development? Three models will be created: a two-task neural network trained to both perform object recognition and to generate human grasp points; a single-task neural network trained to perform only image recognition; and a lesioned neural network, which will be identical to the two-task neural network except that the units with greatest representation contribution towards grasp point generation will be deactivated. All networks will be evaluated on performance metrics such as accuracy (evaluated with ImageNet and Stylized-ImageNet), transfer learning, and robustness against distortions. The networks will also be evaluated on representational metrics such as representation contribution analysis and representational similarity analysis. We expect the two-task network will score higher on performance measures than either the lesioned or single-task network. Additionally, for the two-task network we predict more units will contribute towards grasp point generation than object recognition. Lastly, we expect representations in the two-task network to better reflect human data than the single-task network.