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Related Experiment Video

Updated: Jul 4, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Spatial transfer of object-based statistical learning.

Dirk van Moorselaar^1,2, Jan Theeuwes^3,4,5

¹Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. dirkvanmoorselaar@gmail.com.

Attention, Perception & Psychophysics

|February 5, 2024

Summary

This summary is machine-generated.

We can learn to prioritize specific locations within objects, not just in space. This object-based attention remains even when the object moves, challenging traditional views of spatial attention.

Keywords:

Attention Attention in learning Object-based Visual search

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Last Updated: Jul 4, 2025

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Area of Science:

Cognitive Psychology
Neuroscience
Visual Attention

Background:

Efficient attentional selection relies on extracting environmental regularities via statistical learning.
Attention is typically directed to previously relevant spatial locations and away from distracting ones.

Purpose of the Study:

To investigate if attentional prioritization can extend beyond spatial locations to specific locations within objects.
To determine if object-based attentional learning is independent of spatial location.

Main Methods:

Participants engaged in a task requiring statistical learning of relevant locations within objects.
The learned attentional bias was tested by moving the object to novel spatial locations.

Main Results:

Learned prioritization of locations within an object was demonstrated.
This object-bound attentional bias persisted even when the object changed its spatial position.

Conclusions:

Attentional selection can be learned for locations within objects, independent of retinotopic space.
Findings suggest attentional priority maps are not strictly retinotopically organized, incorporating object-based factors.