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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?
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
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:
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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...
Confirmation Biases01:31

Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?

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Related Experiment Video

Updated: Jul 3, 2026

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
06:45

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

Causal diversity effects in information seeking.

Nancy S Kim1, Jennelle E Yopchick, Leontien de Kwaadsteniet

  • 1Department of Psychology, Northeastern University, Boston, Massachusetts 02115, USA. n.kim@neu.edu

Psychonomic Bulletin & Review
|July 9, 2008
PubMed
Summary
This summary is machine-generated.

People seeking to understand causes prefer testing diverse evidence, even if unlikely. This strategy aims to strongly support or disconfirm a hypothesis, rather than just confirm it.

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Area of Science:

  • Cognitive Psychology
  • Decision Making
  • Causal Reasoning

Background:

  • Individuals often seek information to test hypotheses about cause-and-effect relationships.
  • Understanding how people search for evidence is crucial for cognitive science.

Purpose of the Study:

  • To investigate how knowledge of causal structure influences information-seeking behavior.
  • To determine if people prefer testing evidence close together or far apart in a causal network.

Main Methods:

  • Two studies were conducted using a learned disease category with a defined causal structure.
  • Participants were asked to choose evidence to test a candidate hypothesis within this structure.

Main Results:

  • Participants consistently selected evidence distantly located within the causal structure.
  • This diverse evidence was rated as having a comparatively low probability of occurrence.

Conclusions:

  • People do not solely seek confirming evidence; they favor evidence that, if present, would strongly support a hypothesis but is also unlikely.
  • This suggests a strategy balancing hypothesis support with potential disconfirmation in information seeking.