BIU Vision Science

The talk will give a tour of Guided Search 6.0 (GS6), the latest evolution of the Guided Search model of visual search. Part 1 describes The Mechanics of Search. Because we cannot recognize more than a few items at a time, selective attention is used to prioritize items for processing. Selective attention to an item allows its features to be bound together into a representation that can be matched to a target template in memory or rejected as a distractor. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 msec/item. Since selection occurs more frequently than that, it follows that multiple items are undergoing recognition at the same time, though asynchronously, making GS6 a hybrid serial and parallel model. If a target is not found, search terminates when an accumulating quitting signal reaches a threshold. Part 2 elaborates on the five sources of Guidance that are combined into a spatial “priority map” to guide the deployment of attention (hence “guided search”). These are (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g. priming), (4) reward, and (5) scene syntax and semantics. Finally, in Part 3, we will consider the internal representation of what we are searching for; what is often called “the search template”. That search template is really two templates: a guiding template (probably in working memory) and a target template (in long term memory). Put these pieces together and you have GS6.

Smooth pursuit eye movements are among our most intriguing motor behaviors. They are able to keep the line of sight on smoothly moving targets with little or no overt effort or deliberate planning, and they can respond quickly and accurately to changes in the trajectory of motion of targets. Nevertheless, despite these seeming automatic characteristics, pursuit is highly sensitive to high-level factors, such as the choices made about attention, or beliefs about the direction of upcoming motion. Investigators have struggled for decades with the problem of incorporating both high- and low-level processes into a single coherent model. This talk will present an overview of the current state of efforts to incorporate high- and low-level influences, as well as new observations that add to our understanding of both types of influences. These observations (in contrast to much of the literature) focus on the directional properties of pursuit. Studies will be presented that show: (1) the direction of smooth pursuit made to pursue fields of noisy random dots depends on the relative reliability of the sensory signal and the expected motion direction; (2) smooth pursuit shows predictive responses that depend on the interpretation of cues that signal an impending collision; and (3) smooth pursuit during a change in target direction displays kinematic properties consistent with the well-known two-thirds power law. Implications for incorporating high- and low-level factors into the same framework will be discussed.

Artists have been doing experiments on vision longer than neurobiologists. Some major works of art have provided insights as to how we see; some of these insights are so undamental that they can be understood in terms of the underlying neurobiology. For example, artists have long realized that color and luminance can play independent roles in visual perception. Picasso said, "Colors are only symbols. Reality is to be found in luminance alone." This observation has a parallel in the functional subdivision of our visual systems, where color and luminance are processed by the evolutionarily newer, primate-specific What system, and the older, colorblind, Where (or How) system. Many techniques developed over the centuries by artists can be understood in terms of the parallel organization of our visual systems. I will explore how the segregation of color and luminance processing are the basis for why some Impressionist paintings seem to shimmer, why some op art paintings seem to move, some principles of Matisse's use of color, and how the Impressionists painted "air". Central and peripheral vision are distinct, and I will show how the differences in resolution across our visual field make the Mona Lisa's smile elusive, and produce a dynamic illusion in Pointillist paintings, Chuck Close paintings, and photomosaics. I will explore how artists have figured out important features about how our brains extract relevant information about faces and objects, and I will discuss why learning disabilities may be associated with artistic talent.

Prosopometamorphopsia (PMO) is a disorder characterized by face perception distortions. People with PMO see facial features that appear to melt, stretch, and change size and position. I'll discuss research on PMO carried out by my lab and others that sheds light on the cognitive and neural organization of face perception. https://facedistortion.faceblind.org/

Behavioural measurements are often overlooked by computational neuroscientists, who prefer to focus on electrophysiological recordings or neuroimaging data. This attitude is largely due to perceived lack of depth/richness in relation to behavioural datasets. I will show how contemporary psychophysics can deliver extremely rich and highly constraining datasets that naturally interface with computational modelling. More specifically, I will demonstrate how psychophysics can be used to guide/constrain/refine computational models, and how models can be exploited to design/motivate/interpret psychophysical experiments. Examples will span a wide range of topics (from feature detection to natural scene understanding) and methodologies (from cascade models to deep learning architectures).