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Related Experiment Video

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Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

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Published on: August 29, 2018

Rapid computation of high-level visual surprise.

David Richter^1,2, Paula Pena¹, María Ruz¹

¹Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain.

|December 24, 2025

Summary

The brain predicts incoming sensory information, using surprise to guide perception. This study found that high-level visual features, not low-level ones, drive neural responses, suggesting abstract predictions facilitate perception.

Keywords:

Health sciences

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Last Updated: Jan 8, 2026

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Published on: August 29, 2018

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Published on: May 12, 2019

Methods to Test Visual Attention Online

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Published on: February 19, 2015

Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Predictive processing theories posit the brain anticipates sensory input.
Sensory prediction errors, deviations from expectations, are crucial for perceptual inference.
Understanding which stimulus features the brain predicts remains a key question.

Purpose of the Study:

To investigate which visual features (low-level vs. high-level) generate surprise that influences neural responses.
To determine if the brain predicts abstract or concrete stimulus properties.

Main Methods:

Utilized electroencephalography (EEG) to record brain activity.
Employed computational modeling with deep neural networks (DNNs) to quantify surprise.
Presented participants with probabilistically predicted object images.

Main Results:

Neural responses around 200 ms post-stimulus onset were significantly increased by high-level visual surprise.
Low-level visual surprise did not significantly affect these neural responses.
Surprise was quantified at both early (low-level) and late (high-level) DNN layers.

Conclusions:

The brain's predictive processing relies on high-level, abstract feature predictions.
These high-level predictions rapidly inform perceptual inference.
The predictive machinery is optimized for abstract expectations over low-level sensory details to enhance perception.