ORCID Profile
0000-0001-5033-5069
Current Organisation
Justus Liebig Universitat Giessen
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 3 Research Topics
ANZSRC Field of Research (FoR)
Psychology | Sensory Processes, Perception And Performance
ANZSRC Socio-Economic Objective (SEO)
Related Links
Publications
Unsupervised learning predicts human perception and misperception of gloss
Publisher: Cold Spring Harbor Laboratory
Date: 07-04-2020
DOI: 10.1101/2020.04.07.026120
Abstract: Reflectance, lighting, and geometry combine in complex ways to create images. How do we disentangle these to perceive in idual properties, like surface glossiness? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgments. The networks spontaneously cluster images according to distal properties such as reflectance and illumination, despite receiving no explicit information about them. Intriguingly, the resulting representations also predict the specific patterns of ‘successes’ and ‘errors’ in human perception. Linearly decoding specular reflectance from the model’s internal code predicts human gloss perception better than ground truth, supervised networks, or control models, and predicts, on an image-by-image basis, illusions of gloss perception caused by interactions between material, shape, and lighting. Unsupervised learning may underlie many perceptual dimensions in vision, and beyond.
Resolving figure-ground ambiguity
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 15-03-2010
DOI: 10.1167/2.7.90
The Interpolation of Object and Surface Structure
Publisher: Elsevier BV
Date: 03-2002
Learning About the World by Learning About Images
Publisher: SAGE Publications
Date: 17-03-2021
Abstract: One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of visual processing. But recent advances in unsupervised deep learning have changed that. Neural networks can be taught to compress images or make predictions in space or time. In the process, they learn the statistical regularities that structure images, which in turn often reflect physical objects and processes in the outside world. The astonishing accomplishments of unsupervised deep learning reaffirm the importance of learning statistical regularities for sensory coding and provide a coherent framework for how knowledge of the outside world gets into visual cortex.
Material perception for philosophers
Publisher: Wiley
Date: 24-09-2021
DOI: 10.1111/PHC3.12777
Abstract: Common everyday materials such as textiles, foodstuffs, soil or skin can have complex, mutable and varied appearances. Under typical viewing conditions, most observers can visually recognize materials effortlessly, and determine many of their properties without touching them. Visual material perception raises many fascinating questions for vision researchers, neuroscientists and philosophers, yet has received little attention compared to the perception of color or shape. Here we discuss some of the challenges that material perception raises and argue that further philosophical thought should be directed to how we see materials.
Differential processing of binocular and monocular gloss cues in human visual cortex
Publisher: American Physiological Society
Date: 06-2016
Abstract: The visual impression of an object's surface reflectance (“gloss”) relies on a range of visual cues, both monocular and binocular. Whereas previous imaging work has identified processing within ventral visual areas as important for monocular cues, little is known about cortical areas involved in processing binocular cues. Here, we used human functional MRI (fMRI) to test for brain areas selectively involved in the processing of binocular cues. We manipulated stereoscopic information to create four conditions that differed in their disparity structure and in the impression of surface gloss that they evoked. We performed multivoxel pattern analysis to find areas whose fMRI responses allow classes of stimuli to be distinguished based on their depth structure vs. material appearance. We show that higher dorsal areas play a role in processing binocular gloss information, in addition to known ventral areas involved in material processing, with ventral area lateral occipital responding to both object shape and surface material properties. Moreover, we tested for similarities between the representation of gloss from binocular cues and monocular cues. Specifically, we tested for transfer in the decoding performance of an algorithm trained on glossy vs. matte objects defined by either binocular or by monocular cues. We found transfer effects from monocular to binocular cues in dorsal visual area V3B/kinetic occipital (KO), suggesting a shared representation of the two cues in this area. These results indicate the involvement of mid- to high-level visual circuitry in the estimation of surface material properties, with V3B/KO potentially playing a role in integrating monocular and binocular cues.
Learning to see stuff
Publisher: Elsevier BV
Date: 12-2019
Unsupervised learning predicts human perception and misperception of gloss
Publisher: Springer Science and Business Media LLC
Date: 06-05-2021
DOI: 10.1038/S41562-021-01097-6
Abstract: Reflectance, lighting and geometry combine in complex ways to create images. How do we disentangle these to perceive in idual properties, such as surface glossiness? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this hypothesis, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgements. The networks spontaneously cluster images according to distal properties such as reflectance and illumination, despite receiving no explicit information about these properties. Intriguingly, the resulting representations also predict the specific patterns of ‘successes’ and ‘errors’ in human perception. Linearly decoding specular reflectance from the model’s internal code predicts human gloss perception better than ground truth, supervised networks or control models, and it predicts, on an image-by-image basis, illusions of gloss perception caused by interactions between material, shape and lighting. Unsupervised learning may underlie many perceptual dimensions in vision and beyond.
Related Organisations
University Of Oxford
Location: United Kingdom of Great Britain and Northern Ireland
Massachusetts Institute Of Technology
Location: United States of America
Related Funding Activities
Discovery Projects - Grant ID: DP1097229
Start Date: 02-2010
End Date: 02-2015
Amount: $430,729.00
Funder: Australian Research Council
View Funded Activity