Several studies have shown that our visual system may construct a “summary statistical representation” over groups of visual objects. Although there is a general understanding that human observers can accurately represent sets of a variety of features, many questions on how summary statistics, such as an average, are computed remain unanswered. This study investigated sampling properties of visual information used by human observers to extract two types of summary statistics of item sets, average and variance. We presented three models of (...) ideal observers to extract the summary statistics: a global sampling model without sampling noise, global sampling model with sampling noise, and limited sampling model. We compared the performance of an ideal observer of each model with that of human observers using statistical efficiency analysis. Results suggest that summary statistics of items in a set may be computed without representing individual items, which makes it possible to discard the limited sampling account. Moreover, the extraction of summary statistics may not necessarily require the representation of individual objects with focused attention when the sets of items are larger than 4. (shrink)

In this volume, a group of international scholars address issues relating to community well being and the role of politics, law and economics in Europe and Japan in achieving human-centered symbiotic governance. Case-studies and suggestions for reform are presented in the arenas of economy, government administration, management, university governance, health, agriculture, the environment and urban planning.

Efforts aimed at broadening participation in science, technology, engineering, and mathematics (STEM) require a holistic presentation of the state of racial and gender participation. Statistics currently used to describe participation often include raw counts of degrees and the percentages of demographic groups receiving STEM degrees. While these data provide insights into demographic trends, they do not present the complete picture because these “traditional” statistics do not capture how well a field of study reflects—or is proportionally similar to—a larger body, such (...) as the college population. If the goal of broadening participation in STEM education is to ensure that all racial and gender groups are proportionally represented, analysts require direct measures of representation. In this article, we present a novel metric that assesses the degree to which groups are overrepresented or underrepresented in a given field. This metric calculates field-specific representation by comparing the proportion of degrees awarded to members of a demographic group in a specific field of study with the proportion of all degrees awarded to that group. Using data from the National Science Foundation and the Department of Education, we demonstrate the efficacy of this representation metric and show that it provides new insights into STEM participation levels for women and other groups considered to be underrepresented. While traditional measurements show the increasing number of degrees awarded to and the increasing share of underrepresented minority students in STEM, our metric revealed that underrepresented minorities remain underrepresented in STEM fields, especially in engineering and the natural sciences. (shrink)