2 citations · 3 across the 7 of their papers we have counts for
7 papers
QualiaNet: An Experience-Before-Inference Network
Paul Linton
Human 3D vision involves two distinct stages: an Experience Module, where stereo depth is extracted relative to fixation, and an Inference Module, where this experience is interpre…
Linton Stereo Illusion: Response on Johnston (1991)
Paul Linton
In (Linton, 2024) I present a new illusion (the 'Linton Stereo Illusion') that challenges our understanding of stereo vision. A vision scientist has shared their own analysis of th…
Linton Stereo Illusion
Paul Linton
We present a new illusion that challenges our understanding of stereo vision. The illusion consists of a larger circle at 50cm, and smaller circle in front of it at 40cm, with cons…
Neither hype nor gloom do DNNs justice
Felix A. Wichmann, Simon Kornblith, Robert Geirhos
Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNN…
Fixing the problems of deep neural networks will require better training data and learning algorithms
Drew Linsley, Thomas Serre
Bowers and colleagues argue that DNNs are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those…
Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics
Conor Houghton, Nina Kazanina, Priyanka Sukumaran
Large language models are not detailed models of human linguistic processing. They are, however, extremely successful at their primary task: providing a model for language. For thi…
Would Gaze-Contingent Rendering Improve Depth Perception in Virtual and Augmented Reality?
Paul Linton
Near distances are overestimated in virtual reality, and far distances are underestimated, but an explanation for these distortions remains elusive. One potential concern is that w…