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Poster Session C: Friday, August 15, 2:00 – 5:00 pm, de Brug & E‑Hall

Comparing Brain-Score and ImageNet performance with responses to the scintillating grid illusion

Martin Kent Kraus¹, Lucy Verkerk², Sander Wessel Keemink¹, ¹; ¹University of Western Ontario, ²University Health Network

Presenter: Martin Kent Kraus

Perceptual illusions are widely used to study brain processing, and are essential for elucidating underlying function. Successful brain models should then also be able to reproduce these illusions. Some of the most successful models for vision are several variants of Deep Neural Networks (DNNs). These models can classify images with human-level accuracy, and many behavioral and activation measurements correlate well with humans and animals. For several networks it was also shown that they can reproduce some human illusions. However, this was typically done for a limited number of networks. In addition, it remains unclear whether the presence of illusions is linked to either how accurate or brain-like the DNNs are. Here, we consider the scintillating grid illusion, to which two DNNs have been shown to respond as if they are impacted by the illusion. We develop a measure for measuring Illusion Strength based on model activation correlations, which takes into account the difference in Illusion Strength between illusion and control images. We then compare the Illusion Strength to both model performance (top-1 ImageNet), and how well the model explains brain activity (Brain-score). We show that the illusion was measurable in a wide variety of networks (41 out of 51). However, we do not find a strong correlation between Illusion Strength and Brain-Score, nor performance. Some models have strong illusion scores but not Brain-Score, or vice-versa, but no model does both well. Finally, this differs strongly between model types, particularly between convolutional and transformer-based architectures, with transformers having low illusion scores. Overall, our work shows that Illusion Strength measures an important metric, which is important to consider for assessing brain models, and that some models could still be missing out on some processing important for brain functioning.

Topic Area: Brain Networks & Neural Dynamics

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