Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Systems-I

Classification of Systems-I

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:

Classification of Systems-II

Classification of Systems-II

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,

How Data are Classified: Categorical Data

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Methods of Classification and Identification

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

Aggregates Classification

Aggregates Classification

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...

You might also read

Related Articles

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

Sort by

Same author

Creating Something Different: Similarity, Contrast, and Representativeness in Categorization.

Computational brain & behavior·2026

Same author

Featural relations in concept learning and generalization.

Cognition·2025

Same author

Relational encoding promotes creative insight for problem-solving.

Memory & cognition·2025

Same author

Simple Auto-Associative Networks Succeed at Universal Generalization of the Identity Function and Reduplication Rule.

Cognitive science·2025

Same author

Spontaneous transfer of relational category structures between category learning tasks: A novel approach to measure analogical transfer.

Quarterly journal of experimental psychology (2006)·2024

Same author

Object understanding: Investigating the path from percept to meaning.

Acta psychologica·2021

Search research articles

Related Experiment Video

Updated: Apr 24, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Observation versus classification in supervised category learning.

Kimery R Levering¹, Kenneth J Kurtz

¹Department of Psychology, Binghamton University, Binghamton, NY, USA, kimery.levering@marist.edu.

Memory & Cognition

|September 6, 2014

Summary

Learning how categories are formed differs based on the task. Observational learning, unlike classification with guess-and-correct cycles, enhances understanding of category structure and feature correlations.

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Apr 24, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Cognitive Psychology
Machine Learning Theory
Neuroscience

Background:

Traditional supervised classification focuses on discriminative learning, emphasizing prediction over deep understanding.
Generative learning, focusing on internal category structure, better reflects real-world concept formation.
The 'guess-and-correct' cycle in classification tasks may limit the depth of acquired knowledge.

Purpose of the Study:

To investigate how different supervised learning modes influence the nature of category representations.
To evaluate the generative/discriminative continuum as a framework for understanding learning outcomes.
To assess the impact of the 'guess-and-correct' cycle on category knowledge acquisition.

Main Methods:

Comparison of classification learning (with guess-and-correct) and supervised observational learning (without response).
Two experiments designed to elicit differences in category representations.
Analysis of sensitivity to feature distributions and correlations under different learning conditions.

Main Results:

Supervised observational learning led to greater sensitivity to feature distributional properties.
Observational learning enhanced the detection of correlations between features within categories.
The guess-and-correct cycle in classification learning resulted in narrower category knowledge.

Conclusions:

Subtle procedural variations in supervised learning significantly impact the depth and nature of acquired category knowledge.
The generative/discriminative continuum effectively models differences in learning outcomes.
Findings provide valuable constraints for computational models and theories of category learning.