Jove
Visualize
Contact Us

Related Concept Videos

Deductive Reasoning01:16

Deductive Reasoning

59.9K
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...
59.9K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.3K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.3K
Causality in Epidemiology01:21

Causality in Epidemiology

912
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
912
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

676
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
676
Inductive Reasoning00:59

Inductive Reasoning

63.0K
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...
63.0K
Reasoning01:30

Reasoning

140
Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
140

You might also read

Related Articles

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

Sort by
Same author

Extending a rational process model of causal reasoning: Assessing Markov violations and explaining away with inhibitory causal relations.

Journal of experimental psychology. Learning, memory, and cognition·2024
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Sep 18, 2025

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm
12:12

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Published on: May 14, 2014

10.7K

A Magic Act in Causal Reasoning: Making Markov Violations Disappear.

Bob Rehder1

  • 1Psychology Department, New York University, 6 Washington Place, New York, NY 10012, USA.

Entropy (Basel, Switzerland)
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

The mutation sampler model explains when cognitive resource limitations cause causal reasoning errors. Introducing inhibitory causal relations, not just generative ones, can eliminate these systematic Markov violations.

Keywords:
Markov violationscausal graphical modelscausal knowledgecausal reasoningrational process model

More Related Videos

The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory
07:26

The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

Published on: January 31, 2017

38.4K
State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
12:38

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

10.7K

Related Experiment Videos

Last Updated: Sep 18, 2025

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm
12:12

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Published on: May 14, 2014

10.7K
The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory
07:26

The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

Published on: January 31, 2017

38.4K
State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
12:38

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

10.7K

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Causal Inference

Background:

  • Causal reasoning theories should explain both the occurrence and timing of errors.
  • The mutation sampler is a rational process model predicting errors under limited cognitive resources.
  • Markov violations occur when reasoners incorrectly assume statistical dependence between causally independent variables.

Purpose of the Study:

  • To investigate conditions under which Markov violations disappear.
  • To test the mutation sampler's predictions regarding the elimination of causal reasoning errors.
  • To explore the impact of inhibitory versus generative causal relations on reasoning.

Main Methods:

  • Utilized a novel causal structure with generative and inhibitory relations.
  • Presented subjects with reasoning tasks involving these structures.
  • Employed theoretical model fitting to validate predictions.

Main Results:

  • Reasoning with purely generative causal relations produced standard positive Markov violations.
  • Introducing even a single inhibitory causal relation eliminated these Markov violations.
  • Model fitting confirmed the mutation sampler's ability to predict this elimination.

Conclusions:

  • The type of causal relation (generative vs. inhibitory) significantly impacts the presence of Markov violations.
  • The mutation sampler accurately predicts the disappearance of Markov violations under specific conditions.
  • This finding offers new insights into the mechanisms of human causal reasoning errors.