Related Experiment Video
Updated: Jul 30, 2026

09:03
Eye Tracking Young Children with Autism
Published on: March 27, 2012
46.4K
Saccade endpoints reflect attentional templates in visual search: Evidence from feature distribution learning
Léa Entzmann1,2,3, Árni Kristjánsson1,4, Árni Gunnar Ásgeirsson5,6
1Icelandic Vision Lab, Faculty of Psychology, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
Journal of Vision
|January 14, 2026
Summary
Visual search uses attentional templates, but saccade endpoints only partially reflect learned distractor color distributions. Reaction times are more sensitive to these subtle environmental regularities than eye movements.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Visual Perception
Background:
- Gaze in visual search is guided by attentional templates, which are probabilistic and shaped by environmental regularities.
- Previous research suggests participants can learn to differentiate distractor feature distributions.
Purpose of the Study:
- To investigate if subtle differences in distractor color distributions (Gaussian vs. uniform) are reflected in saccade endpoints during visual search.
- To determine the influence of distractor distribution learning on reaction times and eye movement behavior.
Main Methods:
- Two experiments involved learning trials to prime specific distractor color distributions and test trials to assess responses.
- Saccade endpoints were analyzed for deviations (global effect), and reaction times were recorded.
- Experiments varied in difficulty, with Experiment 2 using more distractors and colors.
Main Results:
- Reaction times and saccade endpoints were influenced by the target color's distance from the mean distractor distribution.
- Increased distance from the mean led to less saccade deviation and faster reaction times.
- Saccade endpoints did not reflect the shape of distractor color distributions, unlike reaction times in Experiment 2.
Conclusions:
- Color priming influences both reaction times and saccade deviations.
- Learning of distractor feature distributions is dependent on search difficulty and the response measure used.
- Saccade endpoints are less sensitive than reaction times to subtle differences in distractor color distribution shapes.
More Related Videos
Related Concept Videos
Review and Preview
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
Bar Graph
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
Difference from Background: Limit of Detection
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
Data: Types and Distribution
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
Distributions in...
Survival Curves
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Introduction to Normal Distributions
Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...

