Treffer: Decoding Artificial Intelligence: A Tutorial on Neural Networks in Behavioral Research.

- Simplifying Complex Concepts: This tutorial helps to demystify ANNs by breaking down the backpropagation algorithm into manageable steps. Readers will gain hands-on experience in Python, empowering them to confidently replicate analyses for regression and classification tasks without feeling overwhelmed. - Building Confidence in Application: Designed for behavioral scientists, and even for other disciplines, this tutorial bridges theory and practice, alleviating anxiety around complex models. Learn to interpret results clearly and effectively, fostering a supportive environment for innovative applications of ANNs in research and beyond. Background: Artificial Neural Networks (ANNs), particularly multilayer perceptrons (MLPs) with backpropagation, are increasingly used in Behavioral and Health Sciences for data analysis. This paper provides a comprehensive tutorial on implementing backpropagation in MLP models for regression and classification tasks using Python. Method: The tutorial guides readers step-by-step through building a backpropagation MLP using a simulated data matrix (N = 1,000) with psychological variables, demonstrating ANNs' versatility in predicting continuous variables and classifying (binary and polytomous) patterns. Python scripts and detailed output interpretations are included. Results: MLP models trained with backpropagation show effectiveness in regression (R² =.71) and classification (binary AUC =.93, polytomous AUC range:.81-.93) on test sets. Conclusions: This tutorial aims to demystify ANNs and promote their use in Behavioral and Health Sciences and other fields, bridging the gap between theory and practical implementation. [ABSTRACT FROM AUTHOR]

Introducción: Las Redes Neuronales Artificiales (RNA), especialmente los perceptrones multicapa (MLPs) con retropropagación, son cada vez más utilizadas en Ciencias del Comportamiento y de la Salud para analizar datos. Este artículo presenta un tutorial completo sobre la implementación de modelos MLP con retropropagación para tareas de regresión y clasificación usando Python. Método: El tutorial guía paso a paso la construcción de un MLP con retropropagación utilizando una matriz de datos simulados (N = 1,000) con variables psicológicas, demostrando la versatilidad de las RNA en la predicción de variables continuas y clasificación de (binarios y politómicos). Se incluyen scripts de Python y su interpretación detallada. Resultados: Los modelos MLP muestran eficacia en regresión (R² = 0.71) y clasificación (AUC binaria =.93, rango AUC politómica:.81-.93) en los tests. Conclusiones: Este tutorial persigue desmitificar las RNA y promover su uso en Ciencias del Comportamiento y la Salud, facilitando la transición de la teoría a la práctica. [ABSTRACT FROM AUTHOR]

Copyright of Clinical & Health / Clínica y Salud is the property of Colegio Oficial de Psicologos de Madrid and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)