Associative learning in baboons and humans: Species differences in learned attention to visual features

We examined attention shifting in baboons and humans during the learning of visual categories. Within a conditional matching-to-sample task, participants of the two species sequentially learned two two-feature categories which shared a common feature. Results showed that humans encoded both features of the initially learned category, but predominantly only the distinctive feature of the subsequently learned category. Although baboons initially encoded both features of the first category, they ultimately retained only the distinctive features of each category. Empirical data from the two species were analyzed with the 1996 ADIT connectionist model of Kruschke. ADIT fits the baboon data when the attentional shift rate is zero, and the human data when the attentional shift rate is not zero. These empirical and modeling results suggest species differences in learned attention to visual features

Constraining bridges between levels of analysis : a computational justification for locally Bayesian learning

Different levels of analysis provide different insights into behavior: computational-level analyses determine the problem an organism must solve and algorithmic-level analyses determine the mechanisms that drive behavior. However, many attempts to model behavior are pitched at a single level of analysis. Research into human and animal learning provides a prime example, with some researchers using computational-level models to understand the sensitivity organisms display to environmental statistics but other researchers using algorithmic-level models to understand organisms’ trial order effects, including effects of primacy and recency. Recently, attempts have been made to bridge these two levels of analysis. Locally Bayesian Learning (LBL) creates a bridge by taking a view inspired by evolutionary psychology: Our minds are composed of modules that are each individually Bayesian but communicate with restricted messages. A different inspiration comes from computer science and statistics: Our brains are implementing the algorithms developed for approximating complex probability distributions. We show that these different inspirations for how to bridge levels of analysis are not necessarily in conflict by developing a computational justification for LBL. We demonstrate that a scheme that maximizes computational fidelity while using a restricted factorized representation produces the trial order effects that motivated the development of LBL. This scheme uses the same modular motivation as LBL, passing messages about the attended cues between modules, but does not use the rapid shifts of attention considered key for the LBL approximation. This work illustrates a new way of tying together psychological and computational constraints

Finding a New Home For the Abortion Right Under the Ninth Amendment

This essay advocates locating the foundation of the constitutional right to an abortion in the Ninth Amendment. Using the Ninth Amendment to recognize the right to an abortion, this article argues, is a better path than using the Fourteenth Amendment because it takes the determination of whether an abortion is a protected right outside the moral realm. The analysis under the Fourteenth Amendment of whether a right is “deeply rooted in the tradition” of the United States inevitably stirs a debate about whether the public considers abortion morally acceptable. In recognizing the right to an abortion under the Ninth Amendment, no such analysis is necessary. The text of the Ninth Amendment allows the U.S. Supreme Court to recognize this protected right without an inquiry into historical tradition. Instead, the Court can use natural law principles, as contemplated by the Founders, to recognize that private conduct is worthy of constitutional protection and acknowledge that the Ninth Amendment affords these rights to the people

Using Bayes to get the most out of non-significant results

No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors

Three Essays on the Accounting of Partisan Gerrymandering and Consequences of Redistricting

Partisan gerrymandering is the construction of political districts to benefit one party at the expense of another. It has been a fixture of American politics for over two centuries, and, in part, determines who is elected to office. In spite of its historical prevalence and broad implications for America's political landscape, there is no consensus on how gerrymandering should be measured. Among the various metrics that exist for this task, one of the most popular is a fairly recent addition the field: the efficiency gap. Still, the efficiency gap has been garnered considerable criticism for its inability to consistently measure gerrymandering and potential to provide unintuitive results. Chapter One of this dissertation documents the efficiency gap's mathematical properties and its shortcomings. It then presents a straightforward modification to the efficiency gap that ensures its consistency. Chapter Two presents individual metrics for the strategies underlying gerrymandering, packing and cracking, and then combines them to create a novel metric of gerrymandering, the cracking differential. Encouragingly, the cracking differential provides results that are qualitatively similar to the efficiency gap, but robust to its most common critiques. Chapter Three utilizes the cracking differential to quantify the causal effects of redistricting reform in Arizona's congressional elections. This is among the first papers to provide a rigorously study the causal link between redistricting reform and gerrymandering, and provides suggestive evidence that Arizona's independent redistricting commission reduced partisan gerrymandering within the state's congressional elections

An analysis of uncertainties and skill in forecasts of winter storm losses

This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic prediction of damages, even if the forecasted gust wind speed contains no uncertainty. Thus, to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions (meteorological and damage model uncertainty) to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill measured by means of a reduced Brier score if both meteorological and damage model uncertainties are taken into account. It is demonstrated that skilful predictions on district level (dividing the area of Germany into 439 administrative districts) are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made

Challenging Land Contracting on the Basis of Disparate Impact After Texas Department of Housing and Community Affairs v. Inclusive Communities Project, Inc.: A Viable Option or a Dead End?

Article published in the Michigan State Law Review

Discontinuous Daily Temperatures in the WATCH Forcing Datasets

The Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differences in daily mean temperature are typically between 10% and 40% larger than their corresponding within-months average temperature differences. However, regions with differences up to 200% can be found in tropical Africa. Particularly in regions where snowmelt is a relevant player for hydrology, a few degrees Celsius difference can be decisive for triggering this process. Daily maximum and minimum temperatures are affected in the same regions, but in a less severe way

Negative storm surges in the Elbe estuary — Large-scale meteoro-logical conditions and future climate change

This article belongs to the Special Issue "Impacts of Climate Change on Transportation Infrastructure, Networks and Nodes"; https://www.mdpi.com/journal/atmosphere/special_issues/climate_transportatio