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The use of the proximity model to represent the relationship between citizens' policy attitudes and the positions of candidates on the issues of the day has considerable appeal because it offers a bridge between theoretical models of political behavior and empirical work. However, there is little consensus among applied researchers about the appropriate representation of voter behavior with respect to the measurement of issue distance, candidate location, or whether to allow heterogeneity in the weight that each individual places on particular issues. Each of these choices suggests a different, and reasonably complicated, nonlinear relationship between voter utility and candidate and voter issue positions which may have a meaningful influence on the substantive conclusions drawn by the researcher. Yet, little attention has been given to the best way to represent the proximity model in applied work. The purpose of this paper is to identify which forms of the proximity model work best, with particular consideration given to the identification of functional forms that are invariant to the choice of scale for the independent variables.

Recent research on spatial language has progressed along two largely separate fronts. First, there has been an effort to examine the role of attention within spatial language, focusing primarily on abstract geometric representations of objects. Second, there have been numerous demonstrations that spatial language is influenced by the intended function of objects as well as their geometry, but the mechanism by which this occurs has been left largely unspecified. We bring together these two lines of inquiry, and argue that attention may integrate geometric and functional information. Specifically, we argue that preferential attention to the functional parts of objects may explain effects of object function on the interpretation of spatial terms.We show this empirically and computationally, using an attentional model of spatial language.

In this article, the authors present and test a formal model that holds that people use information about category boundaries in estimating inexactly represented stimuli. Boundaries restrict stimuli that are category members to fall within a particular range. This model posits that people increase the average accuracy of stimulus estimates by integrating fine-grain values with boundary information, eliminating extreme responses. The authors present 4 experiments in which people estimated sizes of squares from 2 adjacent or partially overlapping stimulus sets. When stimuli from the 2 sets were paired in presentation, people formed relative size categories, truncating their estimates at the boundaries of these categories. Truncation at the boundary of separation between the categories led to exaggeration of differences between stimuli that cross categories. Yet truncated values are shown to be more accurate on average than unadjusted values.

Introduction During the COVID-19 pandemic, governments have experimented with a wide array of policies to further public health goals. This research offers an application of multilevel regression with post-stratification (MRP) analysis to assess state-level support for commonly implemented policies during the pandemic. Methods We conducted a national survey of U.S. adults using The Harris Poll panel from June 17–29, 2020. Respondents reported their support for a set of measures that were being considered in jurisdictions in the U.S. at the time the survey was fielded. MRP analysis was then used to generate estimates of state-level support. Results The research presented here suggests generally high levels of support for mask mandates and social distancing measures in June 2020—support that was consistent throughout the United States. In comparison, support for other policies, such as changes to the road environment to create safer spaces for walking and bicycling, had generally low levels of support throughout the country. This research also provides some evidence that higher support for coronavirus-related policies could be found in more populous states with large urban centers, recognizing that there was low variability across states. Conclusion This paper provides a unique application of MRP analysis in the public health field, uncovering noteworthy state-level patterns, and offering several avenues for future research. Future research could examine policy support at a small geographic level, such as by counties, to understand the distribution of support for public policies within states.