Rigorous exploration in a model-centric science via epistemic iteration.

J Appl Res Mem Cogn

Department of Mathematics and Statistical Science, University of Idaho.

Published: June 2023

Urgent attention is needed to address generalizability problems in psychology. However, the current dominant paradigm centered on dichotomous results and rapid discoveries cannot provide the solution because of its theoretical inadequacies. We propose a paradigm shift towards a model-centric science, which provides the sophistication to understanding the sources of generalizability and promote systematic exploration. In a model-centric paradigm, scientific activity involves iteratively building and refining theoretical, empirical, and statistical models that communicate with each other. This approach is transparent, and efficient in addressing generalizability issues. We illustrate the nature of scientific activity in a model-centric system and its potential for advancing the field of psychology.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10888142PMC
http://dx.doi.org/10.1037/mac0000121DOI Listing

Publication Analysis

Top Keywords

exploration model-centric
8
model-centric science
8
scientific activity
8
rigorous exploration
4
model-centric
4
science epistemic
4
epistemic iteration
4
iteration urgent
4
urgent attention
4
attention needed
4

Similar Publications

The Clever Hans effect occurs when machine learning models rely on spurious correlations instead of clinically relevant features and poses significant challenges to the development of reliable artificial intelligence (AI) systems in medical imaging. This scoping review provides an overview of methods for identifying and addressing the Clever Hans effect in medical imaging AI algorithms. A total of 173 papers published between 2010 and 2024 were reviewed, and 37 articles were selected for detailed analysis, with classification into two categories: detection and mitigation approaches.

View Article and Find Full Text PDF

A Data-Centric Approach to improve performance of deep learning models.

Sci Rep

September 2024

Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia.

The Artificial Intelligence has evolved and is now associated with Deep Learning, driven by availability of vast amount of data and computing power. Traditionally, researchers have adopted a Model-Centric Approach, focusing on developing new algorithms and models to enhance performance without altering the underlying data. However, Andrew Ng, a prominent figure in the AI community, has recently emphasized on better (quality) data rather than better models, which has given birth to Data Centric Approach, also known as Data Oriented technique.

View Article and Find Full Text PDF

Rigorous exploration in a model-centric science via epistemic iteration.

J Appl Res Mem Cogn

June 2023

Department of Mathematics and Statistical Science, University of Idaho.

Urgent attention is needed to address generalizability problems in psychology. However, the current dominant paradigm centered on dichotomous results and rapid discoveries cannot provide the solution because of its theoretical inadequacies. We propose a paradigm shift towards a model-centric science, which provides the sophistication to understanding the sources of generalizability and promote systematic exploration.

View Article and Find Full Text PDF

Data-Centric Machine Learning in Nursing: A Concept Clarification.

Comput Inform Nurs

May 2024

Author Affiliations: School of Nursing, University of Minnesota, Minneapolis (Ms Ball Dunlap); Center for Digital Health, Mayo Clinic, Rochester, MN (Ms Ball Dunlap); School of Nursing, University of Maryland, Baltimore (Dr Nahm); and Division of Nursing Research (Umberfield) and Department of Artificial Intelligence and Informatics (Dr Umberfield), Mayo Clinic, Rochester, MN. P.A.B.D. initially completed the article while a student at the University of Maryland, Baltimore.

The ubiquity of electronic health records and health information exchanges has generated abundant administrative and clinical healthcare data. The vastness of this rich dataset presents an opportunity for emerging technologies (eg, artificial intelligence and machine learning) to assist clinicians and healthcare administrators with decision-making, predictive analytics, and more. Multiple studies have cited various applications for artificial intelligence and machine learning in nursing.

View Article and Find Full Text PDF

High-accuracy, high-efficiency physics-based fluid-solid interaction is essential for reality modeling and computer animation in online games or real-time Virtual Reality (VR) systems. However, the large-scale simulation of incompressible fluid and its interaction with the surrounding solid environment is either time-consuming or suffering from the reduced time/space resolution due to the complicated iterative nature pertinent to numerical computations of involved Partial Differential Equations (PDEs). In recent years, we have witnessed significant growth in exploring a different, alternative data-driven approach to addressing some of the existing technical challenges in conventional model-centric graphics and animation methods.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!