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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: May 15, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Object learning improves feature extraction but does not improve feature selection.

Linus Holm1, Stephen Engel, Paul Schrater

  • 1Department of Psychology, University of Umeå, Umeå, Sweden. Linus.Holm@psy.umu.se

Plos One
|December 20, 2012
PubMed
Summary
This summary is machine-generated.

Learning to recognize objects improves visual search by enhancing information extraction per eye fixation, not by changing where people look. This boosts efficiency in finding familiar items.

Related Experiment Videos

Last Updated: May 15, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computer Vision

Background:

  • Object recognition is faster for familiar items than unfamiliar ones.
  • This efficiency may stem from improved eye fixation strategies or enhanced information processing per fixation.

Purpose of the Study:

  • To investigate whether practice improves visual search by altering fixation selection or information extraction.
  • To quantify changes in visual search performance with training.

Main Methods:

  • Eight participants searched for fragmented objects in dense visual displays.
  • Eye movements were recorded across three daily training sessions.
  • An ideal observer model assessed information available per fixation.

Main Results:

  • Object localization performance improved significantly with training, reducing fixations by 64%.
  • Information extraction per eye fixation increased substantially with practice.
  • Fixation location selection strategies did not show improvement with training.

Conclusions:

  • Visual search expertise is primarily driven by enhanced information processing at each fixation point.
  • Training improves the ability to extract more meaning from visual input, rather than optimizing search patterns.