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Related Concept Videos

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...
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:
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...
Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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

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Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
06:08

Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

Published on: July 22, 2025

Causal conditionals and counterfactuals.

Caren A Frosch1, Ruth M J Byrne

  • 1School of Psychology, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK. cf159@leicester.ac.uk

Acta Psychologica
|August 4, 2012
PubMed
Summary
This summary is machine-generated.

Understanding causal conditionals and counterfactuals involves considering multiple possibilities. People process enabling causes differently, influencing their inferences and paraphrasing strategies.

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Published on: October 13, 2018

Area of Science:

  • Cognitive Psychology
  • Linguistics
  • Philosophy of Mind

Background:

  • Causal reasoning is fundamental to human cognition.
  • Understanding conditional statements (if-then) is crucial for logical thought.
  • Distinguishing between factual conditionals and counterfactuals is key to interpreting events.

Purpose of the Study:

  • To investigate how people understand causal conditionals and counterfactuals.
  • To explore the cognitive processes underlying the interpretation of different types of conditional statements.
  • To examine the role of enabling causes in conditional reasoning.

Main Methods:

  • Six experiments were conducted using converging evidence from three types of measures.
  • Participants' reading times for specific conjunctions were measured (Experiments 1a, 1b).
  • Paraphrasing strategies for conditionals and counterfactuals were analyzed (Experiments 2a, 2b).
  • Inferences drawn from counterfactuals with and without enabling conditions were compared (Experiments 3a, 3b).

Main Results:

  • Causal conditionals with enabling causes primed faster reading of scenarios where the cause occurred without the effect.
  • People paraphrased causal conditionals using causal/temporal connectives (e.g., 'because', 'when').
  • Causal counterfactuals were paraphrased using subjunctive constructions (e.g., 'had...would have').
  • The presence of enabling conditions influenced inferences made from counterfactual statements.

Conclusions:

  • Understanding causal conditionals and counterfactuals relies on considering multiple possibilities.
  • Enabling causes play a significant role in how conditional statements are processed and interpreted.
  • The findings have implications for theories of conditional reasoning and causal inference.