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Related Experiment Video

Updated: Feb 19, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Behavioral differences between humans and machines arise early in visual processing.

Thomas Klein^1,2,3, Wieland Brendel^1,4,5,6, Felix A Wichmann^2,7

¹Max-Planck-Institute for Intelligent Systems, Tübingen, Germany.

Journal of Vision

|February 17, 2026

Summary

This summary is machine-generated.

Deep neural networks (DNNs) show different error patterns than humans, even with brief image presentations. This suggests fundamental differences in early visual processing, not just later cognitive biases.

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Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

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Published on: May 12, 2019

Area of Science:

Computational neuroscience
Cognitive science
Computer vision

Background:

Deep neural networks (DNNs) excel at predicting neural activity in the primate visual cortex.
However, psychophysical studies reveal behavioral discrepancies between DNNs and human observers in image recognition tasks.
Error consistency (EC) highlights differing perceptions of image difficulty between DNNs and humans.

Purpose of the Study:

To investigate whether differences in error consistency (EC) between DNNs and humans emerge from late-stage visual processing.
To test the hypothesis that early visual representations are similar, with later cognitive factors driving behavioral divergence.
To determine if presentation time influences the observed differences in EC.

Main Methods:

Systematic variation of stimulus presentation times (8.3 to 267 ms) for backward-masked natural images.
Measurement of human performance on a speeded eightfold identification task.
Quantification of error consistency (EC) between human performance and DNNs across different presentation durations.

Main Results:

Error consistency (EC) did not increase with shorter presentation times, remaining below the previously established threshold of 0.4.
The findings contradict the hypothesis that EC differences are attributable to late-stage processing or observer-specific factors.
Human performance showed systematic differences compared to DNNs even at very brief presentation durations.

Conclusions:

The persistent low error consistency (EC) suggests that discrepancies between DNNs and human visual systems are not due to late-stage cognitive influences.
These results indicate fundamental processing differences between DNNs and the early stages of the human visual system.
DNNs may not be accurate computational models of early human visual processing as currently understood.