Contour-and Curvature Encoding & Shape Perception
The number and variety of shapes and objects seems endless. Yet, humans are very efficient in shape detection, discrimination and recognition. Physiologically, neurons at early cortical stages (primary visual cortex) are often described as filters tuned to orientation and spatial frequency. The complexity of stimuli, which neurons are selectively responsive to, increases along the "ventral pathway", so that cells at the level of the inferotemporal cortex are selectively responsive to much more complex stimuli such as faces. It is still largely unknown, how these physiological properties of neurons in the visual cortex lead to the perception of the vast number and variety of objects and shapes in the visual world. I am particularly interested in models of shape representation and use computational and experimental (psychophysical) methods to study shape recognition, detection and discrimination.
Schmidtmann, G. & Kingdom, F. A. A., (2017). Nothing more than a pair of curvatures: A common mechanism for the detection of both radial and non-radial frequency patterns. Vision Research, 134, 18–25 [PDF]
Schmidtmann, G., Gordon, E.G., Bennett, D.M., Loffler, G. (2013), Detecting shapes in noise: tuning characteristics of global shape mechanisms, Frontiers in Computational Neuroscience, 7, 37 , 1-14 [PDF]
Schmidtmann, G., Kennedy, J.G., Orbach, H.S., Loffler, G. (2012). Non-linear global pooling in the discrimination of circular and non-circular shapes. Vision Res, 62C, 44-56 [PDF]
Spatial Summation, Probability Summation & Signal Detection Theory
There is physiological and psychophysical evidence that suggests that neurons in the early visual cortex are tuned to particular stimulus features, such as orientation and spatial frequency. Behaviorally, however, the stimuli that are ecologically relevant are more complex, e.g. objects, faces, etc. Hence, the visual system must contain mechanisms that combine the localised outputs from early cortical regions. This leads to the important questions of how these outputs are combined? In a series of theoretical and experimental studies, we have developed the computational basis for these “summation” mechanisms and tested the prediction of such models in various domains of vision, in particular binocular summation, shape discrimination and the detection of orientation-defined textures.
Baldwin, A. S., Schmidtmann, G., Kingdom, F. A. A., & Hess, R. F. (2016). Rejecting probability summation for radial frequency patterns, not so Quick! Vision Research, 122, 124–134. [PDF]
Schmidtmann, G., Jennings, B. J., Bell, J., & Kingdom, F. A. A. (2015). Probability, not linear summation, mediates the detection of concentric orientation-defined textures. Journal of Vision, 15(16):6, 1–19, [PDF]
Kingdom, F. A. A., Baldwin, A. S., Schmidtmann, G. (2015) Modeling probability and additive summation for detection across multiple mechanisms under the assumptions of signal detection theory. Journal of Vision, Vol.15, 1. doi:10.1167/15.5.1 [PDF]
It is well established that the performance in visual tasks typically decreases with increasing eccentricity. This is mainly due to the decreasing density of photoreceptors in the peripheral retina. However, the effect of eccentricity on sensitivity is not homogeneously distributed across the visual field and visual field asymmetries have been described. For instance, we could show that the ability to discriminate simple shapes is best in the lower visual field.
Visual Illusions convincingly demonstrate that we can not rely on our perception. Illusions provide valuable information about the mechanisms of human visual perception and also its limitations.
The left image on the top shows the Arc-Size Illusion. Note that the shorter arc appears flatter compared to the longer ones, despite their equal curvature. Conversely, the arcs on the right side appear equally curved, despite the different curvature. Schmidtmann et al. (2016) quantified the Arc-Size Illusion and proposed a perceptual model.
Another interesting illusion can be seen in the figure on the right. This is an example of an Apparent or Illusory Motion. My colleague Dr. Ben Jennings and I tried to create a spiral texture composed of many Gabor patches. An incorrect procedure led to the accidental discovery of this illusion. The apparent motion works best if you get close to the monitor and move your eyes across the panel.
Schmidtmann, G., Ouhnana, M., Loffler, G., Kingdom, F.A.A. (2016) Imagining circles - empirical data and a perceptual model for the Arc-size Illusion, Vision Research, 121, 50-56 [PDF]
Face Perception & Emotions (McGill Face Database)
Current databases of facial expressions of mental states typically represent only a small subset of expressions, usually covering the basic emotions (fear, disgust, surprise, happiness, sadness, anger). To overcome these limitations, colleagues at McGill University and I have created a large new database of pictures of facial expressions reflecting the richness of mental states. 93 expressions of mental states were interpreted by two professional actors and high-quality pictures were taken under controlled conditions in front and side view. In addition to the validated English version, the database is also available in French and German. It is freely available for scientific, non-commercial purposes.
Schmidtmann, G., Jennings, B. J., Sandra, D. A., Pollock, J., & Gold, I. (2019). The McGill Face Database: validation and insights into the recognition of facial expressions of complex mental states. BioRxiv, 586453. https://doi.org/10.1101/586453 [PDF]
The Database can be downloaded HERE.
Visual Functions of Patients with Traumatic Brain Injuries
Patients with Traumatic Brain Injuries (TBI) frequently suffer from visual discomfort and visual deficits. During my postdoc at the Farivar Lab at the McGill Vision Research Unit as part of the Traumatic Brain Injury Program, I investigated visual functions of patients with TBI. We conducted psychophysical and neuroimaging (fMRI) experiments to understand the mechanisms underlying these symptoms.
Jennings, B. J., Schmidtmann, G., Wehbé, F., Kingdom, F. A. A., & Farivar, R. (2019), Detection of distortions in images of natural scenes in mild traumatic brain injury patients. Vision Research, 161, 12-17 [PDF] [PubMed]
Schmidtmann, G., Ruiz, T., Reynaud, A., Spiegel, D.P., Lague-Beauvais, M., Hess, R.F., Farivar, R. (2017), Sensitivity to binocular disparity is reduced by mild traumatic brain injury. Invest Ophthalmol Vis Sci. 2017; 58:2630–2635. [PDF] [PubMed]
(A) Individual qDSFs for the normative dataset (N = 61, Reynaud et al., Vision Res. 2015). (B) Individual qDSFs for the mTBI group (N = 22). (C) Average qDSFs expressed as the nonparamertic pseudomedian for the normative dataset in blue and the mTBI group in red. The shaded areas represent nonparametric 95% confidence intervals. *p < 0.05 Mann-Whitney U test. Figure 2 from Schmidtmann et al., IOVS, 2017
Glaucoma & Intraocular Pressure
Glaucoma is a complex genetic ocular disease, which leads to a degeneration of retinal ganglion cells and is still one of the most common causes of visual impairment and blindness worldwide. Increased ocular pressure has been associated with an increased risk to develop glaucoma. We could show that playing common exercises on wind instruments, especially high-resistance wind instruments, such as oboe or trumpet, leads to sudden strong fluctuations in the ocular pressure with peak values clearly in the ocular hypertension range.
Schmidtmann, G., Jahnke, S., Seidel, E.J., Sickenberger, W., Grein, H.J. (2011), Intraocular pressure fluctuations in professional brass and woodwind musicians during common playing conditions. Graefes Arch Clin Exp Ophthalmol, 249 (6), 895-901. [PDF] [SUNDAY TIMES] [BBC Radio Interview]
Jahnke, S, Schmidtmann, G., Grein, H.J., Seidel, E.J., Sickenberger, W. (2011), Dynamik des Augeninnendrucks bei professionellen Holz- und Blechbläsern während alltäglicher Spielsituationen. Musiker Medizin und Physiologie, 3, 70-78