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Related Concept Videos

Impact of Schemas01:30

Impact of Schemas

Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Related Experiment Video

Updated: Jul 10, 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

How attention simplifies mental representations for planning.

Jason da Silva Castanheira1, Christina Chang He1, Nicholas Shea2

  • 1Department of Experimental Psychology and Institute for Cognitive Neuroscience, University College London, London, United Kingdom.

Elife
|July 9, 2026
PubMed
Summary
This summary is machine-generated.

Human spatial attention guides planning by focusing on task-relevant information. Simplified environmental representations improve planning efficiency, with individual differences in attention explaining behavioral variations.

Keywords:
attentioncognitive neurosciencecomputational modelhumanneuroscienceplanningpsychologyvisuospatial attention

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Psychology

Background:

  • Human planning is efficient and flexible, relying on simplified mental representations.
  • Computational models suggest a nested optimization where planning influences perception and vice-versa.
  • The specific perceptual and attentional mechanisms driving this interaction are not fully understood.

Purpose of the Study:

  • To investigate how spatial attention controls information availability for planning.
  • To characterize the influence of visuospatial attention on the construction of environmental representations.
  • To integrate attentional effects into computational models of decision-making.

Main Methods:

  • Utilized virtual maze navigation tasks to study spatial attention and planning.
  • Analyzed how spatial proximity and attentional contours affect maze representation.
  • Quantified individual differences in attentional influence on behavior.

Main Results:

  • Spatial proximity determines which maze features are accessible for planning.
  • Task-relevant information aligned with natural attentional contours facilitates simpler, more useful representations.
  • Individual variations in attention significantly explain differences in task representations and planning behavior.

Conclusions:

  • Spatial attention acts as a gatekeeper, selecting information for subjective awareness and planning.
  • Visuospatial attention modulates the formation of simplified environmental models, enhancing planning.
  • This research bridges computational models of perception and decision-making to explain human environmental representation.