Perceptual illusions we understand well, and illusions which aren’t really illusions

Central-peripheral dichotomy in vision: its motivation and predictions (such as in visual illusions)

A model of colour appearance based on efficient coding of natural images

An object’s colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and “illusions” have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an object’s perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band’s lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next we systematically test the model’s ability to qualitatively predict over 35 brightness and colour phenomena, with almost complete success. This implies that contrary to high-level processing explanations, much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a basis for modelling the vision of humans and other animals.

Feedforward and feedback processes in visual recognition

Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive field circuits that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.

The role of motion in localizing objects

Everything we see has a location. We know where things are before we know what they are. But how do we know where things are? Receptive fields in the visual system specify location but neural delays lead to serious errors whenever targets or eyes are moving. Motion may be the problem here but motion can also be the solution, correcting for the effects of delays and eye movements. To demonstrate this, I will present results from three motion illusions where perceived location differs radically from physical location. These help understand how and where position is coded. We first look at the effects of a target’s simple forward motion on its perceived location. Second, we look at perceived location of a target that has internal motion as well as forward motion. The two directions combine to produce an illusory path. This “double-drift” illusion strongly affects perceived position but, surprisingly, not eye movements or attention. Even more surprising, fMRI shows that the shifted percept does not emerge in the visual cortex but is seen instead in the frontal lobes. Finally, we report that a moving frame also shifts the perceived positions of dots flashed within it. Participants report the dot positions relative to the frame, as if the frame were not moving. These frame-induced position effects suggest a link to visual stability where we see a steady world despite massive displacements during saccades. These motion-based effects on perceived location lead to new insights concerning how and where position is coded in the brain.