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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?
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Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
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The Anchoring-and-Adjustment Heuristic01:25

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
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Simple randomization
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Motivational Bias01:25

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Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...

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Related Experiment Video

Updated: Jun 29, 2026

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

Bayesian surprise attracts human attention.

Laurent Itti1, Pierre Baldi

  • 1Computer Science Department, University of Southern California, Los Angeles, 90089, USA. itti@usc.edu

Vision Research
|October 7, 2008
PubMed
Summary
This summary is machine-generated.

We introduce a Bayesian definition of surprise, focusing on how new data changes beliefs, not just rarity. This Bayesian surprise strongly attracts human attention and gaze shifts, guiding focus towards unexpected events.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Neuroscience

Background:

  • Current measures of surprise often rely on data rarity or information content.
  • Subjective experience of surprise is linked to changes in an observer's internal model of the world.

Purpose of the Study:

  • To propose a formal Bayesian definition of surprise that captures subjective aspects of sensory information.
  • To test if this Bayesian surprise influences human attention and gaze behavior.

Main Methods:

  • Developed a formal Bayesian definition of surprise based on the difference between posterior and prior beliefs.
  • Implemented a computational model simulating early visual neurons to compute low-level sensory surprise.
  • Quantified human gaze shifts towards surprising events while watching television.

Main Results:

  • Bayesian surprise is defined by substantial changes in an observer's beliefs, independent of data rarity.
  • 72% of human gaze shifts were directed towards locations with higher-than-average Bayesian surprise.
  • This figure increased to 84% for regions of interest selected by all observers.

Conclusions:

  • Bayesian surprise provides a robust measure of how sensory data affects an observer's beliefs.
  • Subjective surprise, as defined by Bayesian surprise, is a powerful driver of human attention and gaze.
  • The framework is broadly applicable across various sensory modalities and levels of abstraction.