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Previous findings on people perception show that perceivers are attuned to the social categories of group members, which subsequently influences social judgments. An outstanding question is whether perceivers are also attuned to visual cue variability (e.g., gender typicality). In two studies (n = 165), perceivers viewed 12-person ensembles (500 ms) of varying White men-to-women ratios. Importantly, faces of one gender/sex were morphed to appear either more masculine or more feminine. Consistent with prior work, results indicated that judgments varied by the actual gender/sex ratio. In addition, perceivers' judgments varied as a function of manipulated gender cues. Ensembles composed of masculine, compared to feminine White men, were judged to have more men, higher perceived masculinity, and to be more threatening. Complementary results were found for ensembles composed of feminine, compared to masculine White women. These findings highlight the impact of both social categories and visual phenotypic cue variability on people perception. 

Abstract. Post-fire changes to the transport regime of dry ravel, which describes the gravity-driven transport of individual particles downslope, are poorly constrained but critical to understand as ravel may contribute to elevated sediment fluxes and associated debris flow activity observed post-fire in the western United States. In this study, we evaluated post-fire variability in dry ravel travel distance exceedance probabilities and disentrainment rates in the Diablo Range of central coastal California following the Santa Clara Unit Lightning Complex fire of August 2020. Between March 2021 and March 2022, we conducted repeat field experiments simulating ravel with in situ particles (3–35 mm diameter) on a range of experimental surface gradients (0.38–0.81) on both grassy south-facing slopes and oak woodland north-facing slopes. We characterized post-fire evolution in particle transport by fitting a probabilistic Lomax distribution model to the empirical travel distance exceedance probabilities for each experimental particle size, surface gradient, and time period. The resulting Lomax shape and scale parameters were used to identify the transport regime for each subset of simulated ravel, ranging from “bounded” (light-tailed or local) to “runaway” (heavy-tailed or nonlocal) motion. Our experimental results indicated that as vegetation recovered over the first 2 years post-fire, the behavior of small particles (median intermediate axis of 6 mm) became less similar across the experimental sites due to different vegetation structures, whereas medium and large particles (median intermediate axes of 13 and 28 mm, respectively) exhibited a general transition from more runaway to more bounded transport, and large particles became less sensitive to surface gradient. All particle sizes exhibited a decrease in the length scale of transport with time. Of all particle subsets, larger particles on steeper slopes were more likely to experience nonlocal transport, consistent with previous observations and theory. These findings are further corroborated by hillslope and channel deposits, which suggest that large particles were preferentially evacuated from the hillslope to the channel during or immediately after the fire. Our results indicate that nonlocal transport of in situ particles likely occurs in the experimental study catchment, and the presence of wildfire increases the likelihood of nonlocal transport, particularly on steeper slopes. 

Abstract Cesario claims that all bias research tells us is that people “end up using the information they have come to learn as being probabilistically accurate in their daily lives” (sect. 5, para. 4). We expose Cesario's flawed assumptions about the relationship between accuracy and bias. Through statistical simulations and empirical work, we show that even probabilistically accurate responses are regularly accompanied by bias. 

Numerous attempts to improve diversity by way of changing the hearts of decision makers have fallen short of the desired outcome. One underappreciated factor that contributes to bias resides not in decision makers’ hearts, but instead in their minds. People possess images, or mental representations, for specific roles and professions. Which mental image or representation springs spontaneously to mind depends on the current status quo within a field. Whether or not an individual or groups’ appearance matches visual stereotypes results in perceptually mediated preferences and prejudices, both of which harbor pernicious assumptions about who belongs in a professional setting and why. Leveraging these scientific insights can enact change. Shifting visible exemplars can change people’s mental representations and their heart’s evaluative reactions to others. 

Developing computational methods for bodily expressed emotion understanding can benet from knowledge and approaches of multiple fields, including computer vision, robotics, psychology/psychiatry, graphics, data mining, machine learning, and movement analysis. The panel, consisting of active researchers in some closely related fields, attempts to open a discussion on the future of this new and exciting research area. This paper documents the opinions expressed by the individual panelists. 

Abstract Over the last several decades, the study of Earth surface processes has progressed from a descriptive science to an increasingly quantitative one due to advances in theoretical, experimental, and computational geosciences. The importance of geomorphic forecasts has never been greater, as technological development and global climate change threaten to reshape the landscapes that support human societies and natural ecosystems. Here we explore best practices for developing socially relevant forecasts of Earth surface change, a goal we are calling “earthcasting”. We suggest that earthcasts have the following features: they focus on temporal (∼1–∼100 years) and spatial (∼1 m–∼10 km) scales relevant to planning; they are designed with direct involvement of stakeholders and public beneficiaries through the evaluation of the socioeconomic impacts of geomorphic processes; and they generate forecasts that are clearly stated, testable, and include quantitative uncertainties. Earthcasts bridge the gap between Earth surface researchers and decision‐makers, stakeholders, researchers from other disciplines, and the general public. We investigate the defining features of earthcasts and evaluate some specific examples. This paper builds on previous studies of prediction in geomorphology by recommending a roadmap for (a) generating earthcasts, especially those based on modeling; (b) transforming a subset of geomorphic research into earthcasts; and (c) communicating earthcasts beyond the geomorphology research community. Earthcasting exemplifies the social benefit of geomorphology research, and it calls for renewed research efforts toward further understanding the limits of predictability of Earth surface systems and processes, and the uncertainties associated with modeling geomorphic processes and their impacts.