Accuracy in perceptions of a partner’s sexual goals

Intimate partners engage in sex for a variety of reasons, and their perceptions of each other’s sexual goals play an important role in intimate relationships. How accurate are these perceptions of a partner’s sexual goals and is accuracy associated with relationship quality and sexual satisfaction for the couple? To answer these questions, we conducted a 21-day dyadic daily experience study of 121 couples, which we analyzed using two different approaches to examine accuracy: the profile approach and the Truth and Bias Model. Results from these two approaches demonstrated that people’s perceptions of their partner’s sexual goals were indeed accurate, but that accuracy was not associated with relationship quality or sexual satisfaction for the perceiver or their partner. Rather, perceiving a partner’s sexual goals in normative (or socially desirable) ways was associated with relationship quality and sexual satisfaction for both the perceiver and their partner. Implications of these findings are discussed.

Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model

While aligning sequences has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods have much appeal in terms of simplifying the process of inference, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for some emerging forms of data such as genome skims, which cannot be assembled. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is that they typically rely on simplified models of sequence evolution such as Jukes-Cantor. It is possible to compute pairwise distances under more complex models by computing frequencies of base substitutions provided that these quantities can be estimated in the alignment-free setting. A particular limitation is that for many forms of genome-wide data, which arguably present the best use case for alignment-free methods, the strand of DNA sequences is unknown. Under such conditions, the so-called no-strand bias models are the most complex models that can be used. Here, we show how to calculate distances under a no-strain bias restriction of the General Time Reversible (GTR) model called TK4 without relying on alignments. The method relies on replacing letters in the input sequences, and subsequent computation of Jaccard indices between k-mer sets. For the method to work on large genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance. We show in simulation that these alignment-free distances can be highly accurate when genomes evolve under the assumed models, and we examine the effectiveness of the method on real genomic data.

Language and Categorization

This chapter is focussed on linguistic bias in intergroup relations. It is based on the linguistic intergroup bias model, according to which people use different words for describing people and their behaviour on the basis of group membership. In particular, they attribute positive behaviours of ingroup members and negative behaviours of outgroup members to stable enduring characteristics, while attributing negative behaviours of ingroup members and positive behaviours of outgroup members to transitory characteristics dependent on situation or context. This kind of linguistic bias may occur not only in informal communication but also in the mass media, where it can reinforce positive or negative social stereotypes without viewers or readers necessarily being aware how this process is taking place. The chapter concludes that recognizing and limiting the use of such biased language is an important component in producing quality journalism.

Number line tasks and their relation to arithmetics in second to fourth graders

Considering the importance of mathematical knowledge for STEM careers, we aimed to better understand the cognitive mechanisms underlying the commonly observed relation between number line estimations (NLEs) and arithmetics. We used a within-subject design to model NLEs in an unbounded and bounded task and to assess their relations to arithmetics in second to fourth grades. Our results mostly agree with previous findings, indicating that unbounded and bounded NLEs likely index different cognitive constructs at this age. Bounded NLEs were best described by cyclic power models including the subtraction bias model, likely indicating proportional reasoning. Conversely, mixed log-linear and single scalloped power models provided better fits for unbounded NLEs, suggesting direct estimation. Moreover, only bounded but not unbounded NLEs related to addition and subtraction skills. This thus suggests that proportional reasoning probably accounts for the relation between NLEs and arithmetics, at least in second to fourth graders. This was further confirmed by moderation analysis, showing that relations between bounded NLEs and subtraction skills were only observed in children whose estimates were best described by the cyclic power models. Depending on the aim of future studies, our results suggest measuring estimations on unbounded number lines if one is interested in directly assessing numerical magnitude representations. Conversely, if one aims to predict arithmetic skills, one should assess bounded NLEs, probably indexing proportional reasoning, at least in second to fourth graders. The present outcomes also further highlight the potential usefulness of training the positioning of target numbers on bounded number lines for arithmetic development.

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: 1000 multi-tracer mock catalogues with redshift evolution and systematics for galaxies and quasars of the final data release

ABSTRACT We produce 1000 realizations of synthetic clustering catalogues for each type of the tracers used for the baryon acoustic oscillation and redshift space distortion analysis of the Sloan Digital Sky Surveys-iv extended Baryon Oscillation Spectroscopic Survey final data release (eBOSS DR16), covering the redshift range from 0.6 to 2.2, to provide reliable estimates of covariance matrices and test the robustness of the analysis pipeline with respect to observational systematics. By extending the Zel’dovich approximation density field with an effective tracer bias model calibrated with the clustering measurements from the observational data, we accurately reproduce the two- and three-point clustering statistics of the eBOSS DR16 tracers, including their cross-correlations in redshift space with very low computational costs. In addition, we include the gravitational evolution of structures and sample selection biases at different redshifts, as well as various photometric and spectroscopic systematic effects. The agreements on the auto-clustering statistics between the data and mocks are generally within $1\, \sigma$ variances inferred from the mocks, for scales down to a few $h^{-1}\, {\rm Mpc}$ in configuration space, and up to $0.3\, h\, {\rm Mpc}^{-1}$ in Fourier space. For the cross correlations between different tracers, the same level of consistency presents in configuration space, while there are only discrepancies in Fourier space for scales above $0.15\, h\, {\rm Mpc}^{-1}$. The accurate reproduction of the data clustering statistics permits reliable covariances for multi-tracer analysis.

Cross-Correlation of Planck CMB Lensing with DESI-Like LRGs

Cross-correlations between the lensing of the cosmic microwave background (CMB) and other tracers of large-scale structure provide a unique way to reconstruct the growth of dark matter, break degeneracies between cosmology and galaxy physics, and test theories of modified gravity. We detect a cross-correlation between DESI-like luminous red galaxies (LRGs) selected from DECaLS imaging and CMB lensing maps reconstructed with the Planck satellite at a significance of S/N = 27.2 over scales ℓmin = 30, ℓmax = 1000. To correct for magnification bias, we determine the slope of the LRG cumulative magnitude function at the faint limit as s = 0.999 ± 0.015, and find corresponding corrections on the order of a few percent for $C^{\kappa g}_{\ell }, C^{gg}_{\ell }$ across the scales of interest. We fit the large-scale galaxy bias at the effective redshift of the cross-correlation zeff ≈ 0.68 using two different bias evolution agnostic models: a HaloFit times linear bias model where the bias evolution is folded into the clustering-based estimation of the redshift kernel, and a Lagrangian perturbation theory model of the clustering evaluated at zeff. We also determine the error on the bias from uncertainty in the redshift distribution; within this error, the two methods show excellent agreement with each other and with DESI survey expectations.