Supervised machine learning methods in psychology: A practical introduction with annotated R code

Machine learning methods for prediction and pattern detection are increasingly prevalent in psychological research. We provide an introductory overview of machine learning, its applications, and describe how to implement models for research. We review fundamental concepts of machine learning, such as prediction accuracy and out-of-sample evaluation, and summarize standard prediction algorithms including linear regressions, ridge regressions, decision trees, and random forests (plus additional algorithms in the supplementary materials). We demonstrate each method with examples and annotated R code, and discuss best practices for determining sample sizes; comparing model performances; tuning prediction models; preregistering prediction models; and reporting results. Finally, we discuss the value of machine learning methods in maintaining psychology’s status as a predictive science

Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized method

Analogical reasoning derives information from known relations and generalizes this information to similar yet unfamiliar situations. One of the first generalized ways in which deep learning models were able to solve verbal analogies was through vector arithmetic of word embeddings, essentially relating words that were mapped to a vector space (e.g., king - man + woman = __?). In comparison, most attempts to solve visual analogies are still predominantly task-specific and less generalizable. This project focuses on visual analogical reasoning and applies the initial generalized mechanism used to solve verbal analogies to the visual realm. Taking the Abstraction and Reasoning Corpus (ARC) as an example to investigate visual analogy solving, we use a variational autoencoder (VAE) to transform ARC items into low-dimensional latent vectors, analogous to the word embeddings used in the verbal approaches. Through simple vector arithmetic, underlying rules of ARC items are discovered and used to solve them. Results indicate that the approach works well on simple items with fewer dimensions (i.e., few colors used, uniform shapes), similar input-to-output examples, and high reconstruction accuracy on the VAE. Predictions on more complex items showed stronger deviations from expected outputs, although, predictions still often approximated parts of the item's rule set. Error patterns indicated that the model works as intended. On the official ARC paradigm, the model achieved a score of 2% (cf. current world record is 21%) and on ConceptARC it scored 8.8%. Although the methodology proposed involves basic dimensionality reduction techniques and standard vector arithmetic, this approach demonstrates promising outcomes on ARC and can easily be generalized to other abstract visual reasoning tasks.Comment: Data and code can be found on https://github.com/foger3/ARC_DeepLearnin

Accurately accounting for effects on times-of-flight caused by finite field-transition times during the ejection of ions from a storage trap: A study for TOF and MRTOF mass spectrometry

In applied forms of time-of-flight mass spectrometry utilizing ion storage devices prior to an analysis device, a non instantaneous electric ejection pulse applied in the region of ion storage is used to accelerate ions into the time-of-flight analyzer. The calculated mass value of the ions from the time-of-flight is dependent on the duration of the field transition up to full strength. For novel applications dedicated to precision measurements, such as multi-reflection time-of-flight mass spectrometry of short-lived isotopes, the goal is to continuously decrease the measurement uncertainty while providing a mass accuracy on the same order. Even though dynamic-field models for time-of-flight mass spectrometry have been considered in the past for technological advances, it is important to study the accuracy of the measured mass in this context. Using a simplified linear model for the field transition, we provide a basic investigation of the scenario, and discuss the deviation from the classical "mass-over-charge" dependency of the ions' time-of-flight, which becomes violated. The emerging mass discrepancy depends on the distance between the mass of the ion used for calibration and that of the ion of interest and, in extreme cases, can increase to about one percent for systems with short times-of-flight. However, for typical conditions in single-reference multi-reflection time-of-flight mass spectrometry, mass deviations caused by this effect typically remain below the 1 ppm level. If a mass calibration using two or more ion species is possible during the measurement, the effect becomes negligible for appropriate choices of reference masses.Comment: 14 pages, 9 figure

Social Ball: An immersive research paradigm to study social ostracism

We introduce “Social Ball,” a new research paradigm to study ostracism via an online ball tossing game based on Cyberball (Williams & Jarvis, 2006) designed with both researchers and participants in mind. For researchers, the game incorporates a variety of features which are easily accessible from the software’s interface. Some of these features have already been studied with Cyberball (e.g., tossing different objects) but some are novel (e.g., end-game communication or hand-waving during the game). From the participants’ perspective, the game was designed to be more visually and socially immersive to create a more video-game- like online environment. We discuss two previous implementations. Study 1 showed that Social Ball successfully induced need threat and negative affect among ostracized (vs included) participants (n = 247). Study 2 empirically demonstrated how a new feature of the game (i.e., hand-waving) can be used to answer various questions. The results suggested that people waved their hands to varying degrees yet the frequency of which was not associated with post game need satisfaction (n = 2578). Besides describing the features of the game, we also provide a configuration manual and an annotated R code (both as online supplementary materials) to make the paradigm and associated analyses more accessible, and in turn, to stimulate further research. In our discussion, we elaborate on the various ways in which Social Ball can contribute to the understanding of belonging and ostracism

Atomic masses of intermediate-mass neutron-deficient nuclei with relative uncertainty down to 35-ppb via multireflection time-of-flight mass spectrograph

High-precision mass measurements of $^{63}$Cu, $^{64-66}$Zn, $^{65}$Ga, $^{65-67}$Ge, $^{67}$As, $^{78,81}$Br, $^{80}$Rb, and $^{79}$Sr were performed utilizing a multireflection time-of-flight mass spectrograph combined with the gas-filled recoil ion separator GARIS-II. In the case of $^{65}$Ga, a mass uncertainty of 2.1 keV, corresponding to a relative precision of $\delta m / m = 3.5\times10^{-8}$, was obtained and the mass value is in excellent agreement with the 2016 Atomic Mass Evaluation. For $^{67}$Ge and $^{81}$Br, where masses were previously deduced through indirect measurements, discrepancies with literature values were found. The feasibility of using this device for mass measurements of nuclides more neutron-deficient side, which have significant impact on the $rp$-process pathway, is discussed.Comment: 15 pages, 6 figures, 1 tabl

Micron-sized atom traps made from magneto-optical thin films

We have produced magnetic patterns suitable for trapping and manipulating neutral atoms on a $1 \mu$m length scale. The required patterns are made in Co/Pt thin films on a silicon substrate, using the heat from a focussed laser beam to induce controlled domain reversal. In this way we draw lines and "paint" shaped areas of reversed magnetization with sub-micron resolution. These structures produce magnetic microtraps above the surface that are suitable for holding rubidium atoms with trap frequencies as high as ~1 MHz.Comment: 6 pages, 7 figure