Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
What Are Outliers?01:12

What Are Outliers?

3.7K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
3.7K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.8K
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...
5.8K
Modified Boxplots00:57

Modified Boxplots

9.2K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
9.2K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Emergence of collective mental time travel pathways upon remembering consequential elections.

Communications psychology·2026
Same author

A brief intervention to improve reasoning about accumulation.

Journal of experimental psychology. Applied·2025
Same author

Narrative visualizations: Depicting accumulating risks and increasing trust in data.

Cognitive research: principles and implications·2025
Same author

Effects of outlier and familiar context in trend-line estimates in scatterplots.

Memory & cognition·2024
Same author

The role of culture and semantic organization in working memory updating.

Memory (Hove, England)·2024
Same author

Temporal integration of target features across and within trials in the attentional blink.

Attention, perception & psychophysics·2024
Same journal

Low prevalence targets are primarily missed due to mind wandering.

Attention, perception & psychophysics·2026
Same journal

An introduction to the special issue celebrating Mary A. Peterson.

Attention, perception & psychophysics·2026
Same journal

Properties of the threshold stimulus exposure duration (TSED) measure of visual search efficiency.

Attention, perception & psychophysics·2026
Same journal

Auditory selective attention in depth: Investigating directional dependency across front, lateral, and rear spaces.

Attention, perception & psychophysics·2026
Same journal

Dissociations between stereoacuity and visual acuity with binocular night vision goggles.

Attention, perception & psychophysics·2026
Same journal

Reward-based prioritization and perceptual feature effects on attentional flexibility in working memory.

Attention, perception & psychophysics·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.9K

Attentional modulation of outlier processing.

Ahu Gokce1, Bugay Yildirim2, Aysecan Boduroglu2

  • 1Department of Psychology, Kadir Has University, Cibali Mah. Hisaralti Cad. No: 17, 34083, Istanbul, Turkey. ahu.gokce@khas.edu.tr.

Attention, Perception & Psychophysics
|June 13, 2025
PubMed
Summary
This summary is machine-generated.

Attentional mechanisms, guided by spatial cues, significantly influence how the brain processes unique items within a group. This research shows that top-down attention can override automatic outlier detection.

Keywords:
Endogenous spatial cuesOutlier processingTop-down and bottom-up attentional mechanisms

More Related Videos

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
09:37

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

9.0K
A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

13.6K

Related Experiment Videos

Last Updated: Jun 16, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.9K
Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
09:37

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

9.0K
A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

13.6K

Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Visual Perception

Background:

  • Ensemble perception aids visual processing by summarizing groups of stimuli.
  • This process facilitates the detection of unique items (outliers) that deviate from the norm.
  • Understanding how attention modulates outlier processing is crucial for visual cognition.

Purpose of the Study:

  • To investigate the role of attentional mechanisms in modulating outlier processing within ensemble representations.
  • To determine whether spatial cueing influences the localization of outliers.
  • To compare top-down and bottom-up influences on outlier detection.

Main Methods:

  • Three experiments utilized a spatial cueing paradigm to manipulate attentional orienting.
  • Participants localized outlier stimuli within ensembles of circles varying in size.
  • Outlier processing was assessed under valid, invalid, and neutral cueing conditions, with and without active ensemble engagement.

Main Results:

  • Outlier localization was significantly facilitated by valid precues, indicating top-down attentional control.
  • Cue validity effects were consistent across experiments, even when ensemble perception was actively engaged.
  • In invalid trials, responses shifted towards the cued distractor outlier, demonstrating cue-driven processing.

Conclusions:

  • Automatic outlier processing can be overridden by cue-driven attentional mechanisms.
  • Spatial attention plays a critical role in modulating the processing of unique items within visual ensembles.
  • These findings highlight the dynamic interplay between automatic and controlled processes in visual perception.