Many applied screening tasks (e.g., medical image or baggage screening) involve challenging searches for which standard laboratory search is rarely equivalent. For example, whereas laboratory search frequently requires observers to look for precisely defined targets among isolated, non-overlapping images randomly arrayed on clean backgrounds, medical images present unspecified targets in noisy, yet spatially regular scenes. Those unspecified targets are typically oddities, elements that do not belong. To develop a closer laboratory analogue to this, we created a database of scenes containing subtle, ill-specified “oddity” targets. These scenes have similar perceptual densities and spatial regularities to those found in expert search tasks, and each includes 16 variants of the unedited scene wherein an oddity (a subtle deformation of the scene) is hidden. In Experiment 1, eight volunteers searched thousands of scene variants for an oddity. Regardless of their search accuracy, they were then shown the highlighted anomaly and rated its subtlety. Subtlety ratings reliably predicted search performance (accuracy and response times) and did so better than image statistics. In Experiment 2, we conducted a conceptual replication in which a larger group of naïve searchers scanned subsets of the scene variants. Prior subtlety ratings reliably predicted search outcomes. Whereas medical image targets are difficult for naïve searchers to detect, our database contains thousands of interior and exterior scenes that vary in difficulty, but are nevertheless searchable by novices. In this way, the stimuli will be useful for studying visual search as it typically occurs in expert domains: Ill-specified search for anomalies in noisy displays.
Social media has become a primary mode of journalism and political discourse as evidenced by recent online political movements. Platforms like YouTube, by monetizing content through advertising revenue, have fostered a new group of online political professionals, including journalists and commentators. Recently, to limit the spread of misinformation and hateful content, these platforms have begun revising their content monetization, recommendation, and removal policies. To explore the impact on creators, we present an interview based study of journalists and political commentators on YouTube. Our participants report that these policies' implementations are inadequate technically and impacting their ability to survive financially. In their view, these policies may result in the suppression of legitimate reporting and discourse that dissents from mainstream consensus. We identify potential systemic effects of these polices and develop implications for the future design of online media.
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