Revisiting The Bell Curve Debate Regarding the Effects of Cognitive Ability on Wages

In The Bell Curve, Herrnstein and Murray (1994) claim, based on evidence from cross-sectional regressions, that differences in wages in the U.S. labor market are predominantly explained by general intelligence. Cawley, Heckman, and Vytlacil (1999), using evidence from random effects panel regressions, reject this claim, in part because returns to general intelligence vary by racial and gender subgroups in their results. In this article, we examine the regression methods used by both sides of the debate and conclude that neither is the appropriate method to analyze the NLSY data that both use. We introduce the Hausman-Taylor estimator to obtain consistent estimated coefficients on the time-invariant general intelligence-related variables and also extend the analysis up through 2002. While many additional socio-economic factors are important explanatory variables in determining the wage rate, the effect of general intelligence on wages is larger in the Hausman-Taylor specification for the 1979-1994 panel than in either the cross-sectional or random effects models, though it becomes statistically insignificant for the 1994-2002 panel. The Hausman-Taylor analysis also indicates no significantly different returns to intelligence by race or gender group.wages, cognitive ability, education

Multi-view Regularized Gaussian Processes

Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view learning. In this paper, we present a new GP model for multi-view learning. Unlike existing methods, it combines multiple views by regularizing marginal likelihood with the consistency among the posterior distributions of latent functions from different views. Moreover, we give a general point selection scheme for multi-view learning and improve the proposed model by this criterion. Experimental results on multiple real world data sets have verified the effectiveness of the proposed model and witnessed the performance improvement through employing this novel point selection scheme

Why do people reject mixed gambles?

Decision makers often reject mixed gambles offering equalprobabilities of a larger gain and a smaller loss. This importantbehavioral pattern is generally seen as evidence for lossaversion, a psychological mechanism according to whichlosses are given higher utility weights than gains. In this paperwe consider an alternate mechanism capable of generatinghigh rejection rates: A predecisional bias towards rejectionwithout the calculation of utility. We use a drift diffusionmodel of decision making to simultaneously specify and testfor the effects of these two psychological mechanisms in agambling task. Our results indicate that high rejection rates formixed gambles result from multiple different psychologicalmechanisms, and that a predecisional bias applied prior to thecomputation of utility (rather than loss aversion) is the primarydeterminant of this important behavioral tendency

Towards a space of contextual effects on choice behavior: Insights from the driftdiffusion model

Choice behavior can be influenced by many different types of incidental contextual effects, including those pertaining topresentation format, emotion, social belief, and cognitive capacity. Many of these contextual effects form the basis ofnudges, used by academics and practitioners to shape choice. In this paper, we use data from a very large-scale choiceexperiment to uncover a space of contextual effects. We construct this space by analyzing fifteen contextual effects usingthe parameters of the drift diffusion model (DDM). DDM is a quantitative theory of decision making whose parametersoffer a theoretically compelling characterization of the cognitive underpinnings of choice behavior. By representing a largenumber of contextual effects in terms of how they influence the parameters of the DDM, our space is able to preciselymeasure, quantify, and compare the contextual effects, and interpret these effects in terms of their behavioral, mechanistic,and statistical implications

Effects of Lactobacillus rhamnosus GG supplementation on cow's milk allergy in a mouse model

<p>Abstract</p> <p>Background</p> <p>Cow's milk allergy (CMA) is one of the most prevalent human food-borne allergies, particularly in infants and young children from developed countries. Our study aims to evaluate the effects of <it>Lactobacillus rhamnosus </it>GG (LGG) administration on CMA development using whole cow's milk proteins (CMP) sensitized Balb/C mice by two different sensitization methods.</p> <p>Methods</p> <p>LGG supplemented mice were either sensitized orally with CMP and cholera toxin B-subunit (CTB) as adjuvant, or intraperitoneally (IP) with CMP but without the adjuvant. Mice were then orally challenged with CMP and allergic responses were accessed by monitoring hypersensitivity scores, measuring the levels of CMP-specific immunoglobulins (IgG1, IgG2a and IgG) and total IgE from sera, and cytokines (IL-4 and IFN-γ) from spleen lysates.</p> <p>Results</p> <p>Sensitization to CMP was successful only in IP sensitized mice, but not in orally sensitized mice with CMP and CTB. Interestingly, LGG supplementation appeared to have reduced cow's milk allergy (CMA) in the IP group of mice, as indicated by lowered allergic responses.</p> <p>Conclusions</p> <p>Adjuvant-free IP sensitization with CMP was successful in inducing CMA in the Balb/C mice model. LGG supplementation favourably modulated immune reactions by shifting Th2-dominated trends toward Th1-dominated responses in CMP sensitized mice. Our results also suggest that oral sensitization by the co-administration of CMP and CTB, as adjuvant, might not be appropriate to induce CMA in mice.</p

The opportunities and challenges of using Drosophila to model human cardiac diseases

The Drosophila heart tube seems simple, yet it has notable anatomic complexity and contains highly specialized structures. In fact, the development of the fly heart tube much resembles that of the earliest stages of mammalian heart development, and the molecular-genetic mechanisms driving these processes are highly conserved between flies and humans. Combined with the fly’s unmatched genetic tools and a wide variety of techniques to assay both structure and function in the living fly heart, these attributes have made Drosophila a valuable model system for studying human heart development and disease. This perspective focuses on the functional and physiological similarities between fly and human hearts. Further, it discusses current limitations in using the fly, as well as promising prospects to expand the capabilities of Drosophila as a research model for studying human cardiac diseases

Decomposing loss aversion from gaze allocation and pupil dilation

Loss-averse decisions, in which one avoids losses at the expense of gains, are highly prevalent. However, the underlying mechanisms remain controversial. The prevailing account highlights a valuation bias that overweighs losses relative to gains, but an alternative view stresses a response bias to avoid choices involving potential losses. Here we couple a computational process model with eye-tracking and pupillometry to develop a physiologically grounded framework for the decision process leading to accepting or rejecting gambles with equal odds of winning and losing money. Overall, loss-averse decisions were accompanied by preferential gaze toward losses and increased pupil dilation for accepting gambles. Using our model, we found gaze allocation selectively indexed valuation bias, and pupil dilation selectively indexed response bias. Finally, we demonstrate that our computational model and physiological biomarkers can identify distinct types of loss-averse decision makers who would otherwise be indistinguishable using conventional approaches. Our study provides an integrative framework for the cognitive processes that drive loss-averse decisions and highlights the biological heterogeneity of loss aversion across individuals