Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

117
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
117
Modeling and Similitude01:12

Modeling and Similitude

373
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
373
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

892
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
892
Introduction to Learning01:18

Introduction to Learning

596
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
596

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Palladium-Catalyzed Decarboxylative C─H Alkenylation of Proaromatic Acids With Allyl Alcohols and N-Allyl Sulfonamides.

Chemistry, an Asian journal·2026
Same author

LDL-AURIS: a computational model, grounded in error-driven learning, for the comprehension of single spoken words.

Language, cognition and neuroscience·2025
Same author

The pluralization palette: unveiling semantic clusters in English nominal pluralization through distributional semantics.

Morphology (Dordrecht, Netherlands)·2024
Same author

Frequency effects in linear discriminative learning.

Frontiers in human neuroscience·2024
Same author

Language with vision: A study on grounded word and sentence embeddings.

Behavior research methods·2023
Same author

How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning.

Cognitive psychology·2023
Same journal

Adverse and positive childhood experiences in relation to adolescent mental health: sequential indirect associations.

Frontiers in psychology·2026
Same journal

Personality profiles and usage experience are associated with trust and dependence on generative AI: a latent profile analysis.

Frontiers in psychology·2026
Same journal

Editorial: Promoting replicability: empowering method and applied researchers in driving reliable results.

Frontiers in psychology·2026
Same journal

The mediating roles of the challenge appraisal in the relationship between the coach-athlete relationship and adolescent athletes' burnout.

Frontiers in psychology·2026
Same journal

Unpacking GenAI-enabled deep learning engagement: role perceptions, human-GenAI synergy strategies, and underlying mechanisms.

Frontiers in psychology·2026
Same journal

Violence exposure and cyberbullying among Chinese adolescents: the mediating role of moral disengagement.

Frontiers in psychology·2026
See all related articles

Related Experiment Video

Updated: Oct 11, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K

Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices.

Maria Heitmeier1, Yu-Ying Chuang1, R Harald Baayen1

  • 1Department of Linguistics, Eberhard-Karls Universität, Tübingen, Germany.

Frontiers in Psychology
|December 6, 2021
PubMed
Summary
This summary is machine-generated.

This study models German noun inflection using Linear Discriminative Learning, highlighting the importance of incremental learning for capturing frequency effects and word learning in context.

Keywords:
German nounsWidrow-Hoff learningfrequency of occurrencelinear discriminative learningmultivariate multiple regressionsemantic rolessemi-productivitywug task

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.3K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

Related Experiment Videos

Last Updated: Oct 11, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.3K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

Area of Science:

  • Computational Linguistics
  • Psycholinguistics
  • Cognitive Science

Background:

  • Modeling inflectional morphology presents methodological challenges, particularly concerning how form and meaning representations impact learning.
  • The semi-productive German noun system serves as a valuable case study for investigating these challenges.

Purpose of the Study:

  • To address methodological questions in modeling inflectional morphology using Linear Discriminative Learning.
  • To illustrate the influence of form and meaning representations on model performance.
  • To explore the modeling of frequency effects, contextual learning, and the wug task in inflectional morphology.

Main Methods:

  • Application of Linear Discriminative Learning to the German noun system.
  • Utilizing incremental learning to model frequency effects.
  • Developing a model to approximate the learning of inflected words in context.
  • Adapting the model to simulate the wug task.

Main Results:

  • Model performance is significantly influenced by decisions regarding the representation of form and meaning.
  • Incremental learning is crucial for accurately modeling frequency effects in morphological acquisition.
  • The model demonstrates strong memory for known words but limited generalization to unseen data.
  • The model's performance aligns with the semi-productivity of German noun inflection and native speaker generalization.

Conclusions:

  • Linear Discriminative Learning, with appropriate methodological choices, can effectively model aspects of inflectional morphology.
  • Incremental learning is a key factor in simulating the acquisition of morphologically rich languages.
  • The model's performance characteristics reflect real-world language acquisition phenomena, including semi-productivity and generalization patterns.