A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/ n ” data

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze “pick any/ n ” choice data (e.g., consumers rendering buy/no buy decisions concerning a number of actual products). A maximum likelihood procedure is formulated to estimate a joint space of both individuals (represented as vectors) and stimuli (represented as points). The relevant psychometric literature concerning the spatial treatment of such binary choice data is reviewed. The nonlinear probit type model is described, as well as the conjugate gradient procedure used to estimate parameters. Results of Monte Carlo analyses investigating the performance of this methodology with synthetic choice data sets are presented. An application concerning consumer choices for eleven competitive brands of soft drinks is discussed. Finally, directions for future research are presented in terms of further applications and generalizing the model to accommodate three-way choice data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45743/1/11336_2005_Article_BF02294452.pd

Ten simple rules to study distractor suppression

Distractor suppression refers to the ability to filter out distracting and task-irrelevant information. Distractor suppression is essential for survival and considered a key aspect of selective attention. Despite the recent and rapidly evolving literature on distractor suppression, we still know little about how the brain suppresses distracting information. What limits progress is that we lack mutually agreed upon principles of how to study the neural basis of distractor suppression and its manifestation in behavior. Here, we offer ten simple rules that we believe are fundamental when investigating distractor suppression. We provide guidelines on how to design conclusive experiments on distractor suppression (Rules 1–3), discuss different types of distractor suppression that need to be distinguished (Rules 4–6), and provide an overview of models of distractor suppression and considerations of how to evaluate distractor suppression statistically (Rules 7–10). Together, these rules provide a concise and comprehensive synopsis of promising advances in the field of distractor suppression. Following these rules will propel research on distractor suppression in important ways, not only by highlighting prominent issues to both new and more advanced researchers in the field, but also by facilitating communication between sub-disciplines

Electrochemical Sensors for the Detection of Lead and Other Toxic Heavy Metals: The Next Generation of Personal Exposure Biomonitors

To support the development and implementation of biological monitoring programs, we need quantitative technologies for measuring xenobiotic exposure. Microanalytical based sensors that work with complex biomatrices such as blood, urine, or saliva are being developed and validated and will improve our ability to make definitive associations between chemical exposures and disease. Among toxic metals, lead continues to be one of the most problematic. Despite considerable efforts to identify and eliminate Pb exposure sources, this metal remains a significant health concern, particularly for young children. Ongoing research focuses on the development of portable metal analyzers that have many advantages over current available technologies, thus potentially representing the next generation of toxic metal analyzers. In this article, we highlight the development and validation of two classes of metal analyzers for the voltammetric detection of Pb, including: a ) an analyzer based on flow injection analysis and anodic stripping voltammetry at a mercury-film electrode, and b ) Hg-free metal analyzers employing adsorptive stripping voltammetry and novel nanostructure materials that include the self-assembled monolayers on mesoporous supports and carbon nanotubes. These sensors have been optimized to detect Pb in urine, blood, and saliva as accurately as the state-of-the-art inductively coupled plasma-mass spectrometry with high reproducibility, and sensitivity allows. These improved and portable analytical sensor platforms will facilitate our ability to conduct biological monitoring programs to understand the relationship between chemical exposure assessment and disease outcomes