How does the prevalence of a target influence how it is perceived and categorized? A substantial body of work, mostly in visual search, shows that a higher proportion of targets are missed when prevalence is low. This classic low prevalence effect (LPE) involves a shift to a more conservative decision criterion that makes it less likely that observers will call an ambiguous item a target. In contrast, Levari et al. (Science, 360[6396], 1465-1467, 2018 recently reported the opposite effect in a simple categorization task. In their hands, at low prevalence, observers adopted a more liberal criterion, making observers more likely to label ambiguous dots on a blue-purple continuum "blue." They called this "prevalence-induced concept change" (PICC).Here, we report that the presence or absence of feedback is critical. With feedback, observers become more conservative at low prevalence, as in the LPE. Without feedback, they become more liberal, identifying a wider range of stimuli as targets, as in Levari's PICC studies. Stimuli from a shape continuum ranging from rounded ("Bouba") to bumpy ("Kiki") shapes produced similar results. Other variables: response type (2AFC vs. go/no-go), color (blue-purple vs. red-green), and stimuli type (solid color vs. texture) did not influence the criterion shifts. Understanding these effects of prevalence and ways they can be controlled illuminates the context-specific nature of perceptual decisions and may be useful in socially important, low prevalence tasks like cancer screening, airport security, and disease diagnosis in pathology.

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This work identifies and explores several aeroacoustic metrics that allow for urban air mobility (UAM) vehicle noise prediction. An increase in production and use of UAM and distributed electric propulsion (DEP) vehicles within populated civilian areas stands behind the need to minimize the noise produced by these vehicles. The FAA's strict noise regulations on UAM aircraft compels designers to place a significant emphasis, early in the design phase, on the characterization and analysis of the external noise generated by these vehicles, namely, to ensure their design viability. To accomplish this, the present study focuses on the analysis and interpretation of predicted noise signals using a set of characteristic metrics that can be instrumental at guiding the design process. Following a thorough review of metrics standardized by the International Civil Aviation Organization (ICAO) as well as the Federal Aviation Association (FAA), seven general metrics are identified, evaluated, and discussed in the context of UAM noise prediction. When used in conjunction with a modern surface-vorticity panel code, these metrics are shown to provide an effective assortment of tools to concisely describe UAM-based acoustic signal properties.

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