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Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

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...
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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:
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
Counterfactual Thinking01:19

Counterfactual Thinking

Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in human cognition.Types of...
Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
Correlation and Causation01:27

Correlation and Causation

Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...

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Related Experiment Video

Updated: May 20, 2026

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
06:49

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

Published on: January 10, 2014

Updating during reading comprehension: why causality matters.

Panayiota Kendeou1, Emily R Smith, Edward J O'Brien

  • 1School of Health Sciences, Neapolis University Pafos, Pafos 8042, Cyprus. p.kendeou@nup.ac.cy

Journal of Experimental Psychology. Learning, Memory, and Cognition
|August 1, 2012
PubMed
Summary
This summary is machine-generated.

Adding causal explanations to refutations effectively combats outdated information

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Area of Science:

  • Cognitive Psychology
  • Learning Sciences

Background:

  • Outdated information can negatively impact comprehension.
  • Understanding how to update knowledge is crucial for learning.

Purpose of the Study:

  • To investigate the effectiveness of causal explanations in refuting outdated information.
  • To determine if causal explanations prevent comprehension disruption and reactivation of old information.

Main Methods:

  • Seven experiments were conducted to test different refutation strategies.
  • Participants were exposed to outdated information and then presented with refutations, some including causal explanations.

Main Results:

  • A single causal explanation sentence eliminated comprehension disruption but not reactivation.
  • Three causal explanation sentences eliminated both comprehension disruption and reactivation.
  • Causal explanations were more effective than qualified elaborations in preventing reactivation.

Conclusions:

  • Causal explanations are key to overcoming outdated information's impact.
  • Effective knowledge updating requires more than simple refutation; it needs causal reasoning.