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

Aggregates Classification01:29

Aggregates Classification

348
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
348
Survival Tree01:19

Survival Tree

115
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
115
Introduction to Learning01:18

Introduction to Learning

474
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...
474
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Classification of Systems-I01:26

Classification of Systems-I

218
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
218
Classification of Systems-II01:31

Classification of Systems-II

178
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
178

You might also read

Related Articles

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

Sort by
Same author

Sources of imprecision in integrated value comparisons.

Cognition·2026
Same author

Spiking neural models for decision-making tasks with learning.

Journal of mathematical biology·2026
Same author

A compression account of development in the ability to group information in working memory.

Journal of experimental child psychology·2026
Same author

Linking space and ordinal position in working memory: A multi-level meta-analysis of the SPoARC effect.

Cognition·2025
Same author

Heterogeneous Multiscale Multivariate Autoregressive Model: existence, sparse estimation and application to functional connectivity in neuroscience.

Journal of mathematical biology·2025
Same author

Grouping by semantic and color similarity in visual working memory: An attentional mechanism, not compression mechanism.

Journal of experimental psychology. Learning, memory, and cognition·2025

Related Experiment Video

Updated: Jul 22, 2025

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

7.0K

How to fit transfer models to learning data: a segmentation/clustering approach.

Giulia Mezzadri1, Thomas Laloë2, Fabien Mathy3

  • 1Cognition and Decision Lab, Columbia University, New York, US. gm3026@columbia.edu.

Behavior Research Methods
|July 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a statistical framework for transfer models in category learning. The method effectively analyzes learning data, identifying performance shifts and "eureka" moments.

Keywords:
CategorizationCategory transfer modelsGeneralized Context Model (GCM)Rule-based versus similarity-based presentation orderSegmentation/Clustering

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.1K
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K

Related Experiment Videos

Last Updated: Jul 22, 2025

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

7.0K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.1K
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K

Area of Science:

  • Cognitive Psychology
  • Machine Learning
  • Statistical Modeling

Background:

  • Transfer models are valuable for category learning research but struggle with temporal dynamics and diverse processes.
  • Existing models are limited in capturing nuanced learning patterns and participant generalization performance over time.

Purpose of the Study:

  • To propose a novel statistical framework enabling the application of transfer models to category learning data.
  • To enhance the analytical capabilities of transfer models by incorporating a segmentation/clustering technique.
  • To investigate ordinal effects in category learning using an adjusted Generalized Context Model.

Main Methods:

  • Developed a segmentation/clustering technique specifically designed for category learning data.
  • Applied the framework to the Generalized Context Model (GCM) across three experiments manipulating ordinal effects.
  • Utilized backward learning curves to analyze segmentation/clustering outputs and identify learning dynamics.

Main Results:

  • The segmentation/clustering method successfully detected performance differences across experimental contexts.
  • The benefit of a rule-based order in learning was identified in two of the three experiments.
  • Analysis revealed sudden improvements in participant performance, indicative of "eureka" moments.

Conclusions:

  • The proposed statistical framework enhances transfer models' ability to fit category learning data.
  • The adjusted framework effectively captures relevant patterns, including ordinal effects and insight-driven learning.
  • This approach offers a more dynamic and detailed analysis of category learning processes.