Search · four archives
Search · four archives
17 papers · ranked by Valyu relevance
Inhan Kang, Minjeong Jeon
This article aims to provide an overview of the potential advantages and utilities of the recently proposed Latent Space Item Response Model (LSIRM) in the context of intelligence studies. The LSIRM integrates the traditional Rasch IRT model for psychometric data with the latent space model for network data. The model…
Nana Kim, Minjeong Jeon, Ivailo Partchev
There recently have been many studies examining conditional dependence between response accuracy and response times in cognitive tests. While most previous research has focused on revealing a general pattern of conditional dependence for all respondents and items, it is plausible that the pattern may vary across…
Inhan Kang, Minjeong Jeon
Conditional dependence (CD) reflects potential interactions between persons and items in measurement, offering valuable information for deriving personalized diagnoses, evaluations, and feedback. The recent integration of psychometric models with latent space provides an effective way to visualize and quantify…
Selena Wang
The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly…
Dongyoung Go, Minjeong Jeon, Saebyul Lee, Ick Hoon Jin + 2 more
'Hae-Jeong Park' 'James Mockridge'] We explore potential cross-informant discrepancies between child- and parent-report measures with an example of the Child Behavior Checklist (CBCL) and the Youth Self Report (YSR), parent- and self-report measures on children’s behavioral and emotional problems. We propose a new way…
Minjeong Jeon, Ick Hoon Jin, Michael Schweinberger, Samuel Baugh
Classic item response models assume that all items with the same difficulty have the same response probability among all respondents with the same ability. These assumptions, however, may very well be violated in practice, and it is not straightforward to assess whether these assumptions are violated, because neither…
Dongyoung Go, Jina Park, Junyong Park, Minjeong Jeon + 1 more
The latent space item response model (LSIRM; Jeon et al. (2021)) allows us to show interactions between respondents and items in item response data by embedding both items and respondents in a shared and unobserved metric space. The R package lsirm12pl implements Bayesian estimation of the LSIRM and its extensions for…
Ick Hoon Jin, Minjeong Jeon
Item response theory (IRT) models explain an observed item response as a function of a respondent's latent trait and the item's property. IRT is one of the most widely utilized tools for item response analysis; however, local item and person independence, which is a critical assumption for IRT, is often violated in…
Selena Wang, Plamena Powla, Tracy M. Sweet, Subhadeep Paul
variables Authors: ['Selena Wang' 'Plamena Powla' 'Tracy M. Sweet' 'Subhadeep Paul'] Relationships among teachers are known to influence their teaching-related perceptions. We study whether and how teachers' advising relationships (networks) are related to their perceptions of satisfaction, students, and influence over…
JBrandon Duck-Mayr, Roman Garnett, Jacob Montgomery
The goal of item response theoretic (IRT) models is to provide estimates of latent traits from binary observed indicators and at the same time to learn the item response functions (IRFs) that map from latent trait to observed response. However, in many cases observed behavior can deviate significantly from the…
Samuel Renaud, Rachael Mansbach
Current antibacterial treatments cannot overcome the rapidly growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have…
Balázs Török, Dávid G. Nagy, Mariann M. Kiss, Karolina Janacsek + 2 more
Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead approximate and transient ones are recruited. However, experimenters assume an ideal observer model, which…
Authors not listed
Predicting molecular dipole moments is essential for quantum chemistry and materials science applications. In this study, we introduce Q-DFTNet: a Chemistry-Informed Neural Network framework designed to systematically benchmark and interpret graph neural networks (GNNs) for molecular dipole prediction. Seven GNN…
Alexander J. Hess, Sandra Iglesias, Laura Köchli, Stephanie Marino + 7 more
Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling…
George Hutchings, Pantelis Samartsidis, Corinne Donnay, Laura Gaetano + 5 more
Probabilistic latent variable models are a powerful tool for uncovering structure in high-dimensional datasets, particularly in biomedical applications. The increasing availability of large-scale epidemiological studies, such as the UK Biobank, poses important modelling challenges, including mixed data types, high…
Paul S. Muhle-Karbe, Nicholas E. Myers, Mark G. Stokes
Extensive research has examined how information is maintained in working memory (WM), but it remains unknown how WM is used to guide behaviour. We addressed this question by combining human electrophysiology with pattern analyses, cognitive modelling, and a task requiring maintenance of two WM items and priority shifts…
Joram Soch, Carsten Allefeld, John-Dylan Haynes
Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the…