Skip to main content
Intended for healthcare professionals

Tutorials & Tools

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin imperdiet nibh sed ipsum molestie eu mattis justo malesuada. Curabitur id quam augue, ac eleifend justo. Integer eget metus sagittis velit semper auctor vel et nunc. Phasellus tempus felis at arcu fringilla at ndimentum libero placerat. Aenean ut imperdiet dolor. Nulla pretium mi vestibulum dui dictum sed ullamcorper tellus sodales. Duis non nibh id ipsum feugiat imperdiet id fermentum nunc. Maecenas id ultricies felis. Suspendisse lacinia rhoncus vestibulum. Vestibulum molestie vulputate convallis.Fusce et augue erat, nec mollis mi.

Submit Paper Register Interest
Export selected citations
Sort by:

Using Artificial Intelligence to Generate Affective Images: Methodology and Initial Library

  • Maciej Behnke
  • Maciej Kłoskowski
  • Michał Klichowski
  • Wadim Krzyżaniak
  • Kacper Szymański
  • Patryk Maciejewski
  • Patrycja Chwiłkowska
  • Marta Kowal
  • Rafał Jończyk
  • Jan Nowak
  • Szymon Kupiński
  • Dominika Kunc
  • Stanisław Saganowski
  • Aakash A. Chowkase
  • Farida Guemaz
  • Kevin S. Kertechian
  • Ameer I. M. T. Maadal
  • Leonardo A. Aguilar
  • Barnabas T. Alayande
  • Vimala Balakrishnan
  • Dana M. Basnight-Brown
  • Jordane Boudesseul
  • Tomás A. D’Amelio
  • Jovi C. Dacanay
  • Abhishek Dedhe
  • Shan Gao
  • Joao F. G. B. Takayanagi
  • Md. Rohmotul Islam
  • Alvaro Mailhos
  • Christine M. Mpyangu
  • Moises Mebarak
  • Arooj Najmussaqib
  • Ju Hee Park
  • Ekaterine Pirtskhalava
  • Eli Rice
  • Sohrab Sami
  • Yuki Yamada
  • Jan Baczyński
  • Lilianna Dera
  • Szymon Jęśko-Białek
  • Jakub Łączkowski
  • Hubert Marciniak
  • Filip Nowicki
  • Bartosz Wilczek
  • James J. Gross
  • Nicholas A. Coles

Abstract

We introduce a human-in-the-loop pipeline for creating context-aware (e.g., culture, sex, and age) affect-induction images and the initial Library of AI-Generated Affective Images. Current limitations in image-based research include weak to moderate ...
Open AccessResearch articleFirst published Mar 6, 2026
Open dataOpen materialsPreregistred
PDF / EPUB

Large Language Models as Psychological Simulators: A Methodological Guide

  • Zhicheng Lin

Abstract

Large language models (LLMs) offer emerging opportunities for psychological and behavioral research, but methodological guidance is lacking. In this article, I develop a framework for using LLMs as psychological simulators across two primary applications: ...
Open AccessResearch articleFirst published Feb 25, 2026
PDF / EPUB

Handling Item-Level Missing Data in Linear Regression: A Tutorial

  • Guyin Zhang
  • Lihan Chen
  • Dexin Shi

Abstract

With advances in methodology and statistical software, modern methods for handling missing data have become more accessible and straightforward to apply. In psychological studies, researchers often use questionnaires or scales composed of multiple items ...
Open AccessResearch articleFirst published Feb 18, 2026
PDF / EPUB

A Practical Guide to Specifying Random Effects in Longitudinal Dyadic Multilevel Modeling

  • Kareena S. del Rosario
  • Tessa V. West

Abstract

Analyzing over-time dyadic data can be challenging, particularly when using multilevel models with complex random-effect structures. In this tutorial, we discuss the best practices of model specification for longitudinal dyadic multilevel modeling, ...
Open AccessResearch articleFirst published Sep 9, 2025
Open dataOpen materials
PDF / EPUB

From Embeddings to Explainability: A Tutorial on Large-Language-Model-Based Text Analysis for Behavioral Scientists

  • Rudolf Debelak
  • Timo K. Koch
  • Matthias Aßenmacher
  • Clemens Stachl

Abstract

Large language models (LLMs) are transforming research in psychology and the behavioral sciences by enabling advanced text analysis at scale. Their applications range from the analysis of social media posts to infer psychological traits to the automated ...
Open AccessResearch articleFirst published Jul 24, 2025
Open dataOpen materials
PDF / EPUB

An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling

  • Russell J. Boag
  • Reilly J. Innes
  • Niek Stevenson
  • Giwon Bahg
  • Jerome R. Busemeyer
  • Gregory E. Cox
  • Chris Donkin
  • Michael J. Frank
  • Guy E. Hawkins
  • Andrew Heathcote
  • Craig Hedge
  • Veronika Lerche
  • Simon D. Lilburn
  • Gordon D. Logan
  • Dora Matzke
  • Steven Miletić
  • Adam F. Osth
  • Thomas J. Palmeri
  • Per B. Sederberg
  • Henrik Singmann
  • Philip L. Smith
  • Tom Stafford
  • Mark Steyvers
  • Luke Strickland
  • Jennifer S. Trueblood
  • Konstantinos Tsetsos
  • Brandon M. Turner
  • Marius Usher
  • Leendert van Maanen
  • Don van Ravenzwaaij
  • Joachim Vandekerckhove
  • Andreas Voss
  • Emily R. Weichart
  • Gabriel Weindel
  • Corey N. White
  • Nathan J. Evans
  • Scott D. Brown
  • Birte U. Forstmann

Abstract

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly ...
Open AccessResearch articleFirst published May 27, 2025
PDF / EPUB

Navigating Unmeasured Confounding in Nonexperimental Psychological Research: A Practical Guide to Computing and Interpreting E-Value

  • Kaiwen Bi
  • Gabriel J. Merrin
  • Tianyu Li
  • Xianlin Sun
  • Yi Chai
  • Zekai Lu
  • Mark Shuquan Chen

Abstract

Randomized experiments remain the “gold standard” for establishing causality, yet ethical and practical constraints in certain fields often require researchers to rely on observational data. Although psychologists recognize that correlation does not imply ...
Open AccessResearch articleFirst published Apr 14, 2025
Open materials
PDF / EPUB

The Social-Relations Model for Asymmetric-Block-Design Data: A Tutorial With R

  • Avraham N. Kluger
  • Robert A. Ackerman
  • David A. Kenny
  • Thomas E. Malloy
  • Paul W. Eastwick

Abstract

We provide a guide to estimating social-relations-model (SRM) parameters for data collected with the asymmetric block design using R. SRM estimates reflect how people differ in the social behaviors they emit and elicit from others and the extent to which ...
Open AccessResearch articleFirst published Mar 4, 2025
Open dataOpen materials
PDF / EPUB

A Guide to Causal Inference in Life-Event Studies

  • Mario Lawes
  • Stephen G. West
  • Michael Eid

Abstract

There is considerable interest in studying the impact of major life events (e.g., marriage, job loss) on people’s lives. This line of research is inherently causal: Its goal is to study whether life events cause changes in the examined outcomes. However, ...
Open AccessResearch articleFirst published Feb 20, 2025
PDF / EPUB

dockerHDDM: A User-Friendly Environment for Bayesian Hierarchical Drift-Diffusion Modeling

  • Wanke Pan
  • Haiyang Geng
  • Lei Zhang
  • Alexander Fengler
  • Michael J. Frank
  • Ru-Yuan Zhang
  • Hu Chuan-Peng

Abstract

Drift-diffusion models (DDMs) are pivotal in understanding evidence-accumulation processes during decision-making across psychology, behavioral economics, neuroscience, and psychiatry. Hierarchical DDMs (HDDMs), a Python library for hierarchical Bayesian ...
Open AccessResearch articleFirst published Feb 13, 2025
Open dataOpen materials
PDF / EPUB

Also from Sage