An introduction to mixed models for experimental psychology

This chapter describes a class of statistical model that is able to account for most of the cases of nonindependence that are typically encountered in psychological experiments, linear mixed-effects models, or mixed models for short. It introduces the concepts underlying mixed models and how they allow accounting for different types of nonindependence that can occur in psychological data. The chapter discusses how to set up a mixed model and how to perform statistical inference with a mixed model. The most important concept for understanding how to estimate and how to interpret mixed models is the distinction between fixed and random effects. One important characteristic of mixed models is that they allow random effects for multiple, possibly independent, random effects grouping factors. Mixed models are a modern class of statistical models that extend regular regression models by including random-effects parameters to account for dependencies among related data points

Bias in Confidence: A Critical Test for Discrete-State Models of Change Detection

Ongoing discussions on the nature of storage in visual working memory have mostly focused on 2 theoretical accounts: On one hand we have a discrete-state account, postulating that information in working memory is supported with high fidelity for a limited number of discrete items by a given number of "slots," with no information being retained beyond these. In contrast with this all-or-nothing view, we have a continuous account arguing that information can be degraded in a continuous manner, reflecting the amount of resources dedicated to each item. It turns out that the core tenets of this discrete-state account constrain the way individuals can express confidence in their judgments, excluding the possibility of biased confidence judgments. Importantly, these biased judgments are expected when assuming a continuous degradation of information. We report 2 studies showing that biased confidence judgments can be reliably observed, a behavioral signature that rejects a large number of discrete-state models. Finally, complementary modeling analyses support the notion of a mixture account, according to which memory-based confidence judgments (in contrast with guesses) are based on a comparison between graded, fallible representations, and response criteria

Testing the Foundations of Signal Detection Theory in Recognition Memory

Signal detection theory (SDT) plays a central role in the characterization of human judgments in a wide range of domains, most prominently in recognition memory. But despite its success, many of its fundamental properties are often misunderstood, especially when it comes to its testability. The present work examines five main properties that are characteristic of existing SDT models of recognition memory: (a) random-scale representation, (b) latent-variable independence, (c) likelihood-ratio monotonicity, (d) ROC function asymmetry, and (e) nonthreshold representation. In each case, we establish testable consequences and test them against data collected in the appropriately designed recognition-memory experiment. We also discuss the connection between yes–no, forced-choice, and ranking judgments. This connection introduces additional behavioral constraints and yields an alternative method of reconstructing yes–no ROC functions. Overall, the reported results provide a strong empirical foundation for SDT modeling in recognition memory. (PsycInfo Database Record (c) 2021 APA, all rights reserved

The impact of purifying and background selection on the inference of population history:Problems and prospects

Current procedures for inferring population history generally assume complete neutrality—that is, they neglect both direct selection and the effects of selection on linked sites.We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically.We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the distribution of fitness effect as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy. Key words: demographic inference, background selection, distribution of fitness effects, MSMC, fastsimcoal2, approximate Bayesian computation (ABC)

The mPower Study, Parkinson Disease Mobile Data Collected Using Researchkit

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health

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Identification of Stellar Flares Using Differential Evolution Template Optimization

We explore methods for the identification of stellar flare events in irregularly sampled data of ground-based time domain surveys. In particular, we describe a new technique for identifying flaring stars, which we have implemented in a publicly available Python module called "PyVAN". The approach uses the Differential Evolution algorithm to optimize parameters of empirically derived light-curve templates for different types of stars to fit a candidate light-curve. The difference of the likelihoods that these best-fit templates produced the observed data is then used to delineate targets that are well explained by a flare template but simultaneously poorly explained by templates of common contaminants. By testing on light-curves of known identity and morphology, we show that our technique is capable of recovering flaring status in $69\%$ of all light-curves containing a flare event above thresholds drawn to include $\lt1\%$ of any contaminant population. By applying to Palomar Transient Factory data, we show consistency with prior samples of flaring stars, and identify a small selection of candidate flaring G-type stars for possible follow-up.Comment: 15 figures, 24 page

LOAD CARRIAGE ALTERS TIBIOFEMORAL KINEMATICS DURING SLOW JOGGING IN ADULT MEN AND WOMEN

The purpose of this investigation was to determine the effects of load carriage on tibiofemoral kinematics during running. Nineteen healthy, recreationally active adults completed dynamic biplane radiography trials of the dominant limb knee with no load (BW), and an additional 55% of body weight (+55%BW) while running 10% above gait transition velocity. A volumetric model-based tracking technique was utilized to derive medial translation excursion, proximal (inferior-superior) translation excursion, anterior translation excursion, flexion, internal rotation and abduction. At heel strike, running with +55%BW exhibited a more flexed knee compared to BW. However, BW exhibited more proximal translation excursion compared to +55%BW. By contrast, +55%BW had greater anterior translation excursion compared to BW. There were no significant differences between BW and +55%BW for medial translation excursion, internal rotation angle/excursion or abduction angle/excursion The greater knee flexion angle at heel strike for +55%BW may serve as a mechanism to better attenuate the greater impact force via eccentric muscle action. However, reduced proximal translation excursion during +55%BW could suggest greater loading of the soft tissues