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

Cognitive Learning01:21

Cognitive Learning

497
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
497
Purposive Learning01:22

Purposive Learning

196
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
196
Associative Learning01:27

Associative Learning

546
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
546
Deductive Reasoning01:16

Deductive Reasoning

58.1K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
58.1K
Inductive Reasoning00:59

Inductive Reasoning

62.4K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
62.4K
Storage01:23

Storage

128
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
128

You might also read

Related Articles

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

Sort by
Same author

A community machine learning challenge to predict the effects of gene perturbations on T cell differentiation for cancer immunotherapy.

bioRxiv : the preprint server for biology·2026
Same author

Author Correction: Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors.

Nature communications·2026
Same author

A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions.

bioRxiv : the preprint server for biology·2026
Same author

SpatialFusion: A lightweight multimodal foundation model for pathway-informed spatial niche mapping.

bioRxiv : the preprint server for biology·2026
Same author

Detecting chromatin state alterations in PBMCs associated with Type 2 Diabetes Mellitus.

Communications medicine·2026
Same author

Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.

Nature communications·2026
Same journal

Wavenumber-Explicit <i>hp</i>-FEM Analysis for Maxwell's Equations with Impedance Boundary Conditions.

Foundations of computational mathematics (New York, N.Y.)·2024
Same journal

Efficient Computation of the Zeros of the Bargmann Transform Under Additive White Noise.

Foundations of computational mathematics (New York, N.Y.)·2024
Same journal

Counting Real Roots in Polynomial-Time via Diophantine Approximation.

Foundations of computational mathematics (New York, N.Y.)·2022
Same journal

Higher-Dimensional Automorphic Lie Algebras.

Foundations of computational mathematics (New York, N.Y.)·2020
Same journal

Shape-Aware Matching of Implicit Surfaces Based on Thin Shell Energies.

Foundations of computational mathematics (New York, N.Y.)·2019
Same journal

Full Discretisations for Nonlinear Evolutionary Inequalities Based on Stiffly Accurate Runge-Kutta and <i>hp</i>-Finite Element Methods.

Foundations of computational mathematics (New York, N.Y.)·2015
See all related articles

Related Experiment Video

Updated: Sep 2, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

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

Published on: February 8, 2019

6.7K

Causal Structure Learning: A Combinatorial Perspective.

Chandler Squires1, Caroline Uhler2

  • 1Massachusetts Institute of Technology, Cambridge, MA 02139 USA.

Foundations of Computational Mathematics (New York, N.Y.)
|August 8, 2022
PubMed
Summary
This summary is machine-generated.

This review explores causal discovery methods for learning causal structure from data, focusing on directed acyclic graphs and handling unobserved variables. It details the search space and equivalence classes of causal graphs, refining them with interventional data.

Keywords:
Causal inferenceCausal structure discoveryMarkov equivalence

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K
Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.4K

Related Experiment Videos

Last Updated: Sep 2, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

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

Published on: February 8, 2019

6.7K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K
Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.4K

Area of Science:

  • Computer Science
  • Statistics
  • Machine Learning

Background:

  • Causal discovery aims to infer causal relationships from data.
  • Learning causal structure is crucial for understanding complex systems.
  • Existing methods often focus on observable variables.

Purpose of the Study:

  • To review and discuss approaches for causal structure learning from data.
  • To focus on directed acyclic graphs and their generalizations with unobserved variables.
  • To highlight combinatorial aspects of causal discovery.

Main Methods:

  • Discussing search space structures over causal graphs.
  • Analyzing equivalence classes of causal graphs from observational data.
  • Exploring refinement of equivalence classes using interventional data.

Main Results:

  • Identified key combinatorial challenges in causal discovery.
  • Characterized the structure of search spaces for causal graphs.
  • Demonstrated how interventional data refines equivalence classes.

Conclusions:

  • Causal discovery from observational data has inherent limitations represented by equivalence classes.
  • Interventional data is essential for uniquely identifying causal structures.
  • Understanding the combinatorial aspects is vital for advancing causal discovery algorithms.