Publications by authors named "B Balas"

As mazes are typically complex, cluttered stimuli, solving them is likely limited by visual crowding. Thus, several aspects of the appearance of the maze - the thickness, spacing, and curvature of the paths, as well as the texture of both paths and walls - likely influence the performance. In the current study, we investigate the effects of perceptual aspects of maze design on maze-solving performance to understand the role of crowding and visual complexity.

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We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240 hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset.

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Article Synopsis
  • Researchers looked at how well people with eating disorders recognize emotions, not just if they got answers right.
  • They asked 87 college students to do tasks that test their ability to categorize emotions from faces while also looking at their eating disorder symptoms.
  • The study found that how well the students used facial features to recognize emotions didn’t really connect to eating disorder symptoms, but they suggested looking at this more in future studies, especially with people who have actual eating disorders.
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Material depictions in artwork are useful tools for revealing image features that support material categorization. For example, artistic recipes for drawing specific materials make explicit the critical information leading to recognizable material properties (Di Cicco, Wjintjes, & Pont, 2020) and investigating the recognizability of material renderings as a function of their visual features supports conclusions about the vocabulary of material perception. Here, we examined how the recognition of materials from photographs and drawings was affected by the application of the Portilla-Simoncelli texture synthesis model.

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The N190 is a body-sensitive ERP component that responds to images of human bodies in different poses. In natural settings, bodies vary in posture and appear within complex, cluttered environments, frequently with other people. In many studies, however, such variability is absent.

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