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Related Concept Videos

Expected Value01:15

Expected Value

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The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
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Confounding in Epidemiological Studies01:27

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Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Related Experiment Video

Updated: Jan 23, 2026

The Crossmodal Congruency Task as a Means to Obtain an Objective Behavioral Measure in the Rubber Hand Illusion Paradigm
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Multiple expectancies underlie the congruency sequence effect in confound-minimized tasks.

Christopher D Erb1, Andrew J Aschenbrenner2

  • 1School of Psychology, University of Auckland, 23 Symonds Street, Building 302, Auckland 1010, New Zealand.

Acta Psychologica
|June 23, 2019
PubMed
Summary
This summary is machine-generated.

The congruency sequence effect (CSE) is influenced by multiple simultaneous expectations, not just simple repetition. This study refines understanding of cognitive control mechanisms in tasks like the Eriksen flanker task.

Keywords:
Cognitive controlConflict monitoringCongruency sequence effectRepetition expectancy

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Human Behavior

Background:

  • The congruency sequence effect (CSE) is a phenomenon where the impact of congruent or incongruent stimuli depends on the preceding trial.
  • Existing explanations for CSE include repetition expectancy, conflict monitoring, feature integration, and contingency learning.
  • Confound-minimized tasks aim to isolate the mechanisms driving the CSE.

Purpose of the Study:

  • To propose and test a refined repetition expectancy account of the CSE.
  • To investigate how multiple simultaneous expectations based on trial history influence cognitive control.
  • To re-evaluate the presence of feature-integration confounds in standard tasks like the Eriksen flanker, Simon, and Stroop tasks.

Main Methods:

  • Utilized confound-minimized versions of the prime-probe task to study the CSE.
  • Collected and analyzed data from Eriksen flanker, Simon, and Stroop tasks using confound-minimized methodologies.
  • Developed a novel computational account emphasizing simultaneous, multi-faceted expectations.

Main Results:

  • Data from the prime-probe task supported the proposed variant of the repetition expectancy account.
  • Evidence suggests that feature-integration confounds persist in commonly used Eriksen flanker, Simon, and Stroop tasks.
  • The findings indicate that CSE is driven by more complex expectation processes than previously assumed.

Conclusions:

  • The refined repetition expectancy model provides a better explanation for the CSE observed in controlled tasks.
  • Standard tasks may still contain confounds, challenging previous interpretations of the CSE.
  • This research advances the understanding of cognitive control and sequential effects in perception and attention.