New Methods for Delineating the Brain and Cognitive Mechanisms of Attention

New Methods for Delineating the Brain and Cognitive Mechanisms of Attention

Friday, May 7, 1:00 – 3:00 pm
Royal Ballroom 4-5

Organizers: George Sperling, University of California, Irvine

Presenters: Edgar DeYoe (Medical College of Wisconsin), Jack L. Gallant (University of California, Berkeley), Albert J. Ahumada (NASA Ames Research Center, Moffett Field CA 94035), Wilson S. Geisler (The University of Texas at Austin), Barbara Anne Dosher (University of California, Irvine), George Sperling (University of California, Irvine)

Symposium Description

This symposium brings together the world’s leading specialists in six different subareas of visual attention. These distinguished scientists will expose the audience to an enormous range of methods, phenomena, and theories. It’s not a workshop; listeners won’t learn how to use the methods described, but they will become aware of the existence of diverse methods and what can be learned from them. The participants will aim their talks to target VSS attendees who are not necessarily familiar with the phenomena and theories of visual attention but who can be assumed to have some rudimentary understanding of visual information processing. The talks should be of interest to and understandable by all VSS attendees who have an interest in visual information processing: students, postdocs, academic faculty, research scientists, clinicians, and the symposium participants themselves. Attendees will see examples of the remarkable insights achieved by carefully controlled experiments combined with computational modeling. DeYoe reviews his extraordinary fMRI methods for localizing spatial visual attention in the visual cortex of alert human subjects to measure their ”attention maps”. He shows in exquisite detail how top-down attention to local areas in visual space changes the BOLD response (an indicator of neural activity) in corresponding local areas V1 of visual cortex and in adjacent spatiotopic visual processing areas. This work is of fundamental significance in defining the topography of attention and it has important clinical applications. Gallant is the premier exploiter of natural images in the study of visual cortical processing. His work uses computational models to define the neural processes of attention in V4 and throughout the attention hierarchy. Gallant’s methods complement DeYoe’s in that they reveal functions and purposes of attentional processing that often are overlooked with simple stimuli traditionally used. Ahumada, who introduced the reverse correlation paradigm in vision science, here presents a model for the eye movements in perhaps the simplest search task (which happens also to have practical importance): the search for a small target near horizon between ocean and sky. This is an introduction to the talk by Geisler. Geisler continues the theme of attention as optimizing performance in complex tasks in studies of visual search. He presents a computational model for how attention and stimulus factors jointly control eye movements and search success in arbitrarily complex and difficult search tasks. Eye movements in visual search approach those of an ideal observer in making optimal choices given the available information, and observers adapt (learn) rapidly when the nature of the information changes. Dosher has developed analytic descriptions of attentional processes that enable dissection of attention into three components: filter sharpening, stimulus enhancement, and altered gain control. She applies these analyses to show how subjects learn to adjust the components of attention to easy and to difficult tasks. Sperling reviews the methods used to quantitatively describe spatial and temporal attention windows, and to measure the amplification of attended features. He shows that different forms of attention act independently.


I Know Where You Are Secretly Attending! The topography of human visual attention revealed with fMRI

Edgar DeYoe, Medical College of Wisconsin; Ritobrato Datta, Medical College of Wisconsin

Previous studies have described the topography of attention-related activation in retinotopic visual cortex for an attended target at one or a few locations within the subject’s field of view. However, a complete description for all locations in the visual field is lacking. In this human fMRI study, we describe the complete topography of attention-related cortical activation throughout the central 28° of visual field and compare it with previous models. We cataloged separate fMRI-based maps of attentional topography in medial occipital visual cortex when subjects covertly attended to each target location in an array of 3 concentric rings of 6 targets each. Attentional activation was universally highest at the attended target but spread to other segments in a manner depending on eccentricity and/or target size.. We propose an “Attentional Landscape” model that is more complex than a ‘spotlight’ or simple ‘gradient’ model but includes aspects of both. Finally, we asked subjects to secretly attend to one of the 18 targets without informing the investigator. We then show that it is possible to determine the target of attentional scrutiny from the pattern of brain activation alone with 100% accuracy. Together, these results provide a comprehensive, quantitative and behaviorally relevant account of the macroscopic cortical topography of visuospatial attention. We also show how the pattern of attentional enhancement as it would appear distributed within the observer’s field of view thereby permitting direct observation of a neurophysiological correlate of a purely mental phenomenon, the “window of attention.”

Attentional modulation in intermediate visual areas during natural vision

Jack L. Gallant, University of California, Berkeley

Area v4 has been the focus of much research on neural mechanisms of attention. However, most of this work has focused on reduced paradigms involving simple stimuli such as bars and gratings, and simple behaviors such as fixation. The picture that has emerged from such studies suggests that the main effect of attention is to change response rate, response gain or contrast gain. In this talk I will review the current evidence regarding how neurons are modulated by attention under more natural viewing conditions involving complex stimuli and behaviors. The view that emerges from these studies suggests that attention operates through a variety of mechanisms that modify the way information is represented throughout the visual hierarchy. These mechanisms act in concert to optimize task performance under the demanding conditions prevailing during natural vision.

A model for search and detection of small targets

Albert J. Ahumada, NASA Ames Research Center, Moffett Field CA 94035

Computational models predicting the distribution of the time to detection of small targets on a display are being developed to improve workstation designs. Search models usually contain bottom-up processes, like a saliency map, and top-down processes, like a priori distributions over the possible locations to be searched. A case that needs neither of these features is the search for a very small target near the horizon when the sky and the ocean are clear. Our models for this situation have incorporated a saccade-distance penalty and inhibition-of-return with a temporal decay. For very small, but high contrast targets, using the simple detection model that the target is detected if it is foveated is sufficient. For low contrast signals, a standard observer detection model with masking by the horizon edge is required. Accurate models of the the search and detection process without significant expectations or stimulus attractors should make it easier to estimate the way in which the expectations and attractors are combined when they are included.

Ideal Observer Analysis of Overt Attention

Wilson S. Geisler, The University of Texas at Austin

In most natural tasks humans use information detected in the periphery, together with context and other task-dependent constraints, to select their fixation locations (i.e., the locations where they apply the specialized processing associated with the fovea). A useful strategy for investigating the overt-attention mechanisms that drive fixation selection is to begin by deriving appropriate normative (ideal observer) models. Such ideal observer models can provide a deep understanding of the computational requirements of the task, a benchmark against which to compare human performance, and a rigorous basis for proposing and testing plausible hypotheses for the biological mechanisms. In recent years, we have been investigating the mechanisms of overt attention for tasks in which the observer is searching for a known target randomly located in a complex background texture (nominally a background of filtered noise having the average power spectrum of natural images). This talk will summarize some of our earlier and more recent findings (for our specific search tasks): (1) practiced humans approach ideal search speed and accuracy, ruling out many sub-ideal models; (2) human eye movement statistics are qualitatively similar to those of the ideal searcher; (3) humans select fixation locations that make near optimal use of context (the prior over possible target locations); (4) humans show relatively rapid adaptation of their fixation strategies to simulated changes in their visual fields (e.g., central scotomas); (5) there are biologically plausible heuristics that approach ideal performance.

Attention in High Precision Tasks and Perceptual Learning

Barbara Anne Dosher, University of California, Irvine; Zhong-Lin Lu, University of Southern California

At any moment, the world presents far more information than the brain can process. Visual attention allows the effective selection of information relevant for high priority processing, and is often more easily focused on one object than two. Both spatial selection and object attention have important consequences for the accuracy of task performance. Such effects are historically assessed primarily for relatively “easy” lower-precision tasks, yet the role of attention can depend critically on the demand for fine, high precision judgments. High precision task performance generally depends more upon attention and attention affects performance across all contrasts with or without noisy stimuli. Low precision tasks with similar processing loads generally show effects of attention only at intermediate contrasts and may be restricted to noisy display conditions. Perceptual learning can reduce the costs of inattention. The different roles of attention and task precision are accounted for within the context of an elaborated perceptual template model of the observer showing distinct functions of attention, and providing an integrated account of performance as a function of attention, task precision, external noise and stimulus contrast. Taken together, these provide a taxonomy of the functions and mechanisms of visual attention.

Modeling the Temporal, Spatial, and Featural Processes of Visual Attention

George Sperling, University of California, Irvine

A whirlwind review of the methods used to quantitatively define the temporal, spatial, and featural properties of attention, and some of their interactions. The temporal window of attention is measured by moving attention from one location to another in which a rapid sequence of different items (e.g., letters or numbers) is being presented. The probability of items from that sequence entering short-term memory defines the time course of attention: typically 100 msec to window opening, maxim at 300-400 msec, and 800 msec to closing. Spatial attention is defined like acuity, by the ability to alternately attend and ignore strips of increasingly finer grids. The spatial frequency characteristic so measured then predicts achievable attention distributions to arbitrarily defined regions. Featural attention is defined by the increased salience of items that contain to-be-attended features. This can be measured in various ways; quickest is an ambiguous motion task which shows that attended features have 30% greater salience than neutral features. Spatio-temporal interaction is measured when attention moves as quickly as possible to a designated area. Attention moves in parallel to all the to-be-attended areas, i.e., temporal-spatial independence. Independence of attentional modes is widely observed; it allows the most efficient neural processing.