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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.
Niklas Müller1, Hongye Chen1, Sofie Wahlberg1
1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
Plos Computational Biology
|June 30, 2026
Summary
Peripheral visual information is processed before foveal information in the human brain. This study reveals a temporal coarse-to-fine visual processing hierarchy using electroencephalography (EEG) and convolutional neural networks (CNNs).
Area of Science:
- Neuroscience
- Computational Vision
- Visual Perception
Background:
- Retinotopic tuning organizes the human visual cortex.
- Current computational models (CNNs) lack retinotopic organization.
- Temporal dynamics of visual field processing remain unclear.
Purpose of the Study:
- Investigate temporal processing differences between foveal and peripheral vision.
- Develop encoding models predicting human EEG responses using CNNs with spatial sampling strategies.
- Explore retinotopic information encoded in EEG signals.
Main Methods:
- Implemented spatial sampling strategies on CNN feature maps for encoding models.
- Predicted human EEG responses using these models with natural scene images.
- Experimentally confirmed temporal differences using foveal and peripheral stimulation.
- Developed a novel method to recover visual field information from neural data.
Main Results:
- Peripheral visual information processing precedes foveal processing.
- A differential spatial transform based on retinal ganglion cell measurements best modeled this temporal difference.
- EEG recordings contain significant temporally encoded retinotopic information.
- Improved prediction of neural responses by aligning spatial sampling of humans and CNNs.
Conclusions:
- Novel neural evidence for a temporal coarse-to-fine visual processing hierarchy.
- Spatial information sampling directly influences temporal processing dynamics.
- EEG data holds rich, temporally encoded retinotopic information.
- Publicly released EEG dataset for future research.
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