I have started a new postdoc position in the Farivar Lab at the McGill Vision Research Unit as part of the Traumatic Brain Injury Program. This research project investigates visual functions of patients with traumatic brain injuries (TBI). I am going to conduct psychophysical and neuroimaging (fMRI) experiments to understand the mechanisms underlying frequently described visual discomfort after TBI. It is also envisaged to develop recovery tools for such patients.
Links:
Rejecting probability summation for radial frequency patterns, not so Quick!
Radial frequency (RF) patterns are used to assess how the visual system processes shape. They are thought to be detected globally. This is supported by studies that have found summation for RF patterns to be greater than what is possible if the parts were being independently detected and performance only then improved with an increasing number of cycles by probability summation between them. However, the model of probability summation employed in these previous studies was based on High Threshold Theory (HTT), rather than Signal Detection Theory (SDT). We conducted rating scale experiments to investigate the receiver operating characteristics. We find these are of the curved form predicted by SDT, rather than the straight lines predicted by HTT. This means that to test probability summation we must use a model based on SDT. We conducted a set of summation experiments finding that thresh- olds decrease as the number of modulated cycles increases at approximately the same rate as previously found. As this could be consistent with either additive or probability summation, we performed maximum-likelihood fitting of a set of summation models (Matlab code provided in our Supplementary material) and assessed the fits using cross validation. We find we are not able to distin- guish whether the responses to the parts of an RF pattern are combined by additive or probability sum- mation, because the predictions are too similar. We present similar results for summation between separate RF patterns, suggesting that the summation process there may be the same as that within a single RF.
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
An essential part of visual object recognition is the evaluation of the curvature of both an object’s outline as well as the contours on its surface. We studied a striking illusion of visual curvature – the arc-size illusion (ASI) – to gain insight into the visual coding of curvature. In the ASI, short circular arcs appear less curved than full circles. We investigated if and how the ASI depends on (i) the physical size of the stimulus and (ii) on the length of the arc. Our results show that perceived curvature monotonically increases with arc length up to an arc angle of about 60 ̊, thereafter remaining constant and equal to the perceived curvature of a full circle. We investigated if the misjudgment of curvature in the ASI translates into predictable biases for three other perceptual tasks: (i) judging the position of the centre of circular arcs; (ii) judging if two circular arcs fall on the circumference of the same (invisible) circle and (iii) interpolating the position of a point on the circumference of a circle defined by two circular arcs. We found that the biases in all the above tasks were reliably predicted by the same bias mediating the ASI. We present a simple model, based on the central angle subtended by an arc, that captures the data for all tasks. Importantly, we argue that the ASI and related biases are a consequence of the fact that an object’s curvature is perceived as constant with viewing distance, in other words is perceptually scale invariant.
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
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
Gunnar Schmidtmann, Ben J. Jennings, Frederick A.A. Kingdom
Visual objects are effortlessly recognized from their outlines, largely irrespective of viewpoint. Previous studies have drawn different conclusions regarding the importance to shape recognition of specific shape features such as convexities and concavities. However, most studies employed familiar objects, or shapes without curves, and did not measure shape recognition across changes in scale and position. We present a novel set of random shapes with well-defined convexities, concavities and inflections (intermediate points), segmented to isolate each feature type. Observers matched the segmented reference shapes to one of two subsequently presented whole-contour shapes (target or distractor) that were re-scaled and re-positioned. For very short segment lengths, performance was significantly higher for convexities than for concavities or intermediate points and remained constant with increasing segment length. For concavities and intermediate points, performance improved with increasing segment length reaching convexity performance only for long segments. No significant differences between concavities and intermediates were found. These results show for the first time that closed curvilinear shapes are encoded using the positions of convexities, rather than concavities or intermediate regions. A shape-template model with no free parameters gives an excellent account of the data.
Schmidtmann, G., Jennings, B. J., & Kingdom, F. A. A. (2015). Shape recognition: convexities, concavities and things in between. Scientific Reports, 5, 17142. http://doi.org/10.1038/srep17142 PDF
The Royal Victoria Hospital.
The Montreal General Hospital
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
