Astrid Zeman

KU Leuven, Brain and Cognition, Post-Doc

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Post-doc researcher in deep learning with Prof Hans op de Beeck at the KULeuven in Leuven, Belgium.
Investigating visual learning and visual expertise in DNNs vs fMRI.
My most recent project has been exploring the interaction between shape and category information in deep nets vs the human visual ventral stream.

My first post-doc was with Prof Allison McKendrick (Optometry and Vision Sciences) and Prof Andrew Turpin (Computer Science Dept) at the University of Melbourne in Australia. My project involved static and kinetic perimetry (measuring luminance thresholds across spatial extent for static and moving targets for healthy-sighted humans and patients with glaucoa) as well as integrating social robots to provide feedback to people during perimetry tests.

My PhD was on Computational Modelling of Visual Illusions at Macquarie University in Sydney, Australia with Assoc Prof Kevin Brooks (MQ, Psychology Dept) and Assoc Prof Oliver Obst (UWS, Computer Science Dept, previously at CSIRO).
Supervisors: Prof Hans op de Beeck
Phone: +61 (0)2 9850 6729
Address: Macquarie Cognitive Science Centre (CCD)
Level 4, Building C5C,
Macquarie University
NSW 2109 Australia

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The Müller-Lyer Illusion in a Computational Model of Biological Object Recognition.

by Kevin R Brooks and Astrid Zeman

PLOS ONE

Studying illusions provides insight into the way the brain processes information. The Müller-Lyer... more Studying illusions provides insight into the way the brain processes information. The Müller-Lyer Illusion (MLI) is a classical geometrical illusion of size, in which perceived line length is decreased by arrowheads and increased by arrowtails. Many theories have been put forward to explain the MLI, such as misapplied size constancy scaling, the statistics of image-source relationships and the filtering properties of signal processing in primary visual areas. Artificial models of the ventral visual processing stream allow us to isolate factors hypothesised to cause the illusion and test how these affect classification performance. We trained a feed-forward feature hierarchical model, HMAX, to perform a dual category line length judgment task (short versus long) with over 90% accuracy. We then tested the system in its ability to judge relative line lengths for images in a control set versus images that induce the MLI in humans. Results from the computational model show an overall illusory effect similar to that experienced by human subjects. No natural images were used for training, implying that misapplied size constancy and image-source statistics are not necessary factors for generating the illusion. A post-hoc analysis of response weights within a representative trained network ruled out the possibility that the illusion is caused by a reliance on information at low spatial frequencies. Our results suggest that the MLI can be produced using only feed-forward, neurophysiological connections.

Astrid Zeman

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