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Related Experiment Video

Updated: Jan 17, 2026

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Spatio-temporal visual statistical learning in context.

Dominik Garber¹, József Fiser¹

¹Department of Cognitive Science, Center for Cognitive Computation, Central European University, Quellenstraße 51, 1100 Vienna, Austria.

|September 20, 2025

Summary

This summary is machine-generated.

Visual statistical learning (VSL) integrates spatial and temporal information, influenced by context and internal biases. This study shows VSL is a dynamic interpretive process, not just a recorder of statistics.

Keywords:

Context dependent learning Perceptual biases Spatio-temporal visual information Unconscious inference

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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

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Last Updated: Jan 17, 2026

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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Area of Science:

Cognitive Psychology
Neuroscience
Visual Perception

Background:

Classical Visual Statistical Learning (VSL) research separates spatial and temporal domains.
Real-world visual input inherently combines spatial patterns unfolding over time.
Contextual information and internal biases significantly influence visual interpretation.

Purpose of the Study:

To investigate the interplay between time, space, and biases in a novel spatio-temporal VSL paradigm.
To determine if temporal statistics can facilitate learning of spatial patterns.
To examine how motion and occlusion context modulate VSL.

Main Methods:

Developed a novel spatio-temporal VSL setup.
Manipulated the presentation of spatial patterns over time, with and without occluders.
Analyzed participants' learning of spatial and temporal regularities under different contextual conditions.

Main Results:

Replicated classical VSL findings within the mixed spatio-temporal setup.
Demonstrated that temporal statistics can be leveraged for inferring spatial patterns.
Confirmed that motion and occlusion context significantly influence the automatic learning of spatio-temporal regularities.

Conclusions:

VSL extends beyond processing co-occurrence statistics to an interpretive process.
VSL integrates low-level spatio-temporal information with higher-level biases.
The environment's structure is inferred through a complex interplay of sensory input and internal biases.