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Related Concept Videos

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?
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Comparison Tests01:28

Comparison Tests

An infinite series composed of positive terms may either approach a finite value or increase without bound. Determining which outcome occurs is a central task in calculus, and comparison tests provide structured methods for making this determination. Rather than evaluating a series directly, these tests relate it to another series whose behavior is already known, allowing conclusions to be drawn through logical comparison.The direct comparison test applies to series with positive terms. If each...

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Related Experiment Video

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The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice
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Testing biased competition between attention shifts: The new multiple cue paradigm.

Franziska Oren1, Søren Kyllingsbæk1, Dawa Dupont1

  • 1Department of Psychology, University of Copenhagen.

Journal of Experimental Psychology. Human Perception and Performance
|May 2, 2024
PubMed
Summary

New research introduces a multiple cue paradigm to study how the brain selects between competing attention shifts. Findings show attention selection involves limited capacity and biased competition, influenced by the number of cues and reward value.

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computational Modeling

Background:

  • The classic Posner cuing paradigm examines single endogenous attention shifts.
  • Limited understanding exists regarding competition between multiple endogenous attention shifts.

Purpose of the Study:

  • To introduce a novel multiple cue paradigm for studying competition among endogenous attention shifts.
  • To investigate the role of limited capacity, biased competition, and reward in selecting attention shifts.

Main Methods:

  • Development and application of a new multiple cue paradigm.
  • Manipulation of the number and relative importance of competing attention shifts.
  • Three experiments were conducted to test the paradigm's efficacy.

Main Results:

  • Attention shift selection is constrained by limited capacity and exhibits biased competition.
  • The probability of optimal attention shift execution depends on the number of competing shifts.
  • Reward significantly influences the selection process between competing attention shifts.

Conclusions:

  • The results support the mathematical model of intention selection (MIS).
  • The new paradigm serves as a tool for testing MIS and understanding response set retrieval from long-term memory (LTM).
  • Findings offer insights into LTM representations, habitual vs. goal-directed action control.