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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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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Motivational Bias01:25

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Related Experiment Video

Updated: May 12, 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

Attention is spontaneously biased toward regularities.

Jiaying Zhao1, Naseem Al-Aidroos, Nicholas B Turk-Browne

  • 1Department of Psychology, Green Hall, Princeton University, Princeton, NJ 08540, USA. jiayingz@princeton.edu

Psychological Science
|April 6, 2013
PubMed
Summary
This summary is machine-generated.

Statistically structured information, or regularities, capture attention over noisy data, aiding perception and learning. This finding challenges traditional attention models by highlighting how the brain prioritizes predictable environmental patterns.

Keywords:
attentional capturecognitive controlfeature-based attentionspatial attentionstatistical learningvisual search

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Machine Learning

Background:

  • Environmental regularities aid cognitive functions like perception, memory, and language.
  • Existing attention models often distinguish between stimulus-driven and goal-directed processes.

Purpose of the Study:

  • To investigate if statistically structured information receives attentional priority.
  • To determine if this prioritization occurs independently of intrinsic salience or goal relevance.

Main Methods:

  • Three experiments were conducted to test the hypothesis.
  • Visual search tasks assessed spatial attention biases towards temporal regularities.
  • Attentional capture was measured for features within dimensions exhibiting temporal regularities.

Main Results:

  • Temporal regularities at a location facilitated visual search, demonstrating spatial attention bias.
  • Attentional capture was enhanced when stimuli with regularities appeared in singleton colors or dimensions.
  • Regularities influenced attention even when they did not predict task-relevant information.

Conclusions:

  • Statistically structured information is prioritized by attention, influencing both spatial and feature selection.
  • This prioritization mechanism extends beyond traditional stimulus-driven and goal-directed attention frameworks.
  • Prioritizing regularities may enhance statistical learning and knowledge acquisition about stable environmental patterns.