Randall R. Rojas

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Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract(More)
The Sloan Digital Sky Survey now extends over a large enough region that galaxies in low density environments can be detected. Rojas et al. have developed a technique to extract more than 1000 void galaxies with δρ/ρ < −0.6 on scales r > 7h −1 Mpc from the SDSS. In this paper, we present the luminosity function of these galaxies and compare it to that of(More)
1.1 Directed Acyclic Graph (DAG). Illustration of a DAG consisting of two potential causes C 1 and C 2 depicted by the top nodes and respective effect E indicated by the lower node. Edges represent conditional dependencies and arrows represent the causal relations 3.1 An illustration of the generative models. The different models combine R 1 and R 2 in(More)
Wide-angle, moderately deep redshift surveys such as that conducted as part of the Sloan Digital Sky Survey (SDSS) allow study of the relationship between the structural elements of the large-scale distribution of galaxies – including groups, cluster, superclusters, and voids – and the dependence of galaxy formation and evolution on these enviroments. We(More)
Using a nearest neighbor analysis, we construct a sample of void galaxies from the Sloan Digital Sky Survey (SDSS) and compare the photometric properties of these galaxies to the population of non-void (wall) galaxies. We trace the density field of galaxies using a volume-limited sample with z max = 0.089. Galaxies from the flux-limited SDSS with z ≤ z max(More)
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