Jeremy M. Wolfe

Bio: Jeremy M. Wolfe is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Visual search & Foraging. The author has an hindex of 73, co-authored 370 publications receiving 24822 citations. Previous affiliations of Jeremy M. Wolfe include Massachusetts Institute of Technology & Princeton University.

Guided Search 2.0 A revised model of visual search

Abstract: An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.

Guided search: an alternative to the feature integration model for visual search.

Abstract: Subjects searched sets of items for targets defined by conjunctions of color and form, color and orientation, or color and size. Set size was varied and reaction times (RT) were measured. For many unpracticed subjects, the slopes of the resulting RT X Set Size functions are too shallow to be consistent with Treisman's feature integration model, which proposes serial, self-terminating search for conjunctions. Searches for triple conjunctions (Color X Size X Form) are easier than searches for standard conjunctions and can be independent of set size. A guided search model similar to Hoffman's (1979) two-stage model can account for these data. In the model, parallel processes use information about simple features to guide attention in the search for conjunctions. Triple conjunctions are found more efficiently than standard conjunctions because three parallel processes can guide attention more effectively than two. Language: en

What attributes guide the deployment of visual attention and how do they do it

Abstract: As you drive into the centre of town, cars and trucks approach from several directions, and pedestrians swarm into the intersection. The wind blows a newspaper into the gutter and a pigeon does something unexpected on your windshield. This would be a demanding and stressful situation, but you would probably make it to the other side of town without mishap. Why is this situation taxing, and how do you cope?

What Can 1 Million Trials Tell Us About Visual Search

Abstract: In a typical visual search experiment, observers look through a set of items for a designated target that may or may not be present. Reaction time (RT) is measured as a function of the number of items in the display (set size), and inferences about the underlying search processes are based on the slopes of the resulting RT 〈 Set Size functions. Most search experiments involve 5 to 15 subjects perform- ing a few hundred trials each. In this retrospective study, I examine results from 2,500 experimental sessions of a few hundred trials each (approximately 1 million total trials). These data represent a wide variety of search tasks. The resulting picture of human search behav- ior requires changes in our theories of visual search.

Guided Search 4.0: Current Progress With a Model of Visual Search.

Abstract: performance, specifically of search tasks in which an observer looks for a target object among some number of distracting items. Classically, models have described two mechanisms of search: serial and parallel (Egeth, 1966). In serial search, attention is directed to one item at a time, allowing each item to be classified as a target or a distractor in turn (Sternberg, 1966). Parallel models propose that all (or many) items are processed at the same time. A decision about target presence is based on the output of this processing (Neisser, 1963). GS evolved out of the two-stage architecture of models like Treisman’s feature integration theory (FIT; Treisman & Gelade, 1980). FIT proposed a parallel, preattentive first stage and a serial second stage controlled by visual selective attention. Search tasks could be divided into those performed by the first stage in parallel and those requiring serial processing. Much of the data comes from experiments measuring reaction time (RT) as a function of set size. The RT is the time required to respond that a target is present or absent. Treisman proposed that there was a limited set of attributes (e.g. color, size, motion) that could be processed in parallel, across the whole visual field (Treisman, 1985, 1986; Treisman & Gormican, 1988). These produced RTs that were essentially independent of the set size. Thus, slopes of RT set size functions were near zero. In FIT, targets defined by two or more attributes required the serial deployment of attention. The critical difference between preattentive search tasks and serial tasks was that the serial tasks required a serial “binding” step (Treisman, 1996; von der Malsburg, 1981). One piece of brain might analyze the color of an object. Another might analyze its orientation. Binding is the act of linking those bits of information into a single representation of an object—an object file (Kahneman, Treisman, & Gibbs, 1992). Tasks requiring serial deployment of attention from one item to the next produce RT set size functions with slopes markedly greater than zero (typically, about 20–30 ms/item for target-present trials and a bit more than twice that for target-absent). 8

Control of goal-directed and stimulus-driven attention in the brain

Abstract: We review evidence for partially segregated networks of brain areas that carry out different attentional functions. One system, which includes parts of the intraparietal cortex and superior frontal cortex, is involved in preparing and applying goal-directed (top-down) selection for stimuli and responses. This system is also modulated by the detection of stimuli. The other system, which includes the temporoparietal cortex and inferior frontal cortex, and is largely lateralized to the right hemisphere, is not involved in top-down selection. Instead, this system is specialized for the detection of behaviourally relevant stimuli, particularly when they are salient or unexpected. This ventral frontoparietal network works as a 'circuit breaker' for the dorsal system, directing attention to salient events. Both attentional systems interact during normal vision, and both are disrupted in unilateral spatial neglect.

A model of saliency-based visual attention for rapid scene analysis

Abstract: A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.

Neural Mechanisms of Selective Visual Attention

Abstract: The two basic phenomena that define the problem of visual attention can be illustrated in a simple example. Consider the arrays shown in each panel of Figure 1. In a typical experiment, before the arrays were presented, subjects would be asked to report letters appearing in one color (targets, here black letters), and to disregard letters in the other color (nontargets, here white letters). The array would then be briefly flashed, and the subjects, without any opportunity for eye movements, would give their report. The display mimics our. usual cluttered visual environment: It contains one or more objects that are relevant to current behavior, along with others that are irrelevant. The first basic phenomenon is limited capacity for processing information. At any given time, only a small amount of the information available on the retina can be processed and used in the control of behavior. Subjectively, giving attention to any one target leaves less available for others. In Figure 1, the probability of reporting the target letter N is much lower with two accompa­ nying targets (Figure la) than with none (Figure Ib). The second basic phenomenon is selectivity-the ability to filter out un­ wanted information. Subjectively, one is aware of attended stimuli and largely unaware of unattended ones. Correspondingly, accuracy in identifying an attended stimulus may be independent of the number of nontargets in a display (Figure la vs Ie) (see Bundesen 1990, Duncan 1980).

The magical number 4 in short-term memory: a reconsideration of mental storage capacity.

Abstract: Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. How- ever, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide vari- ety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described. Four basic conditions in which chunks can be identified and capacity limits can accordingly be observed are: (1) when information overload limits chunks to individual stimulus items, (2) when other steps are taken specifically to block the recoding of stimulus items into larger chunks, (3) in performance discontinuities caused by the capacity limit, and (4) in various indirect effects of the capacity limit. Under these conditions, rehearsal and long-term memory cannot be used to combine stimulus items into chunks of an unknown size; nor can storage mechanisms that are not capacity- limited, such as sensory memory, allow the capacity-limited storage mechanism to be refilled during recall. A single, central capacity limit averaging about four chunks is implicated along with other, noncapacity-limited sources. The pure STM capacity limit expressed in chunks is distinguished from compound STM limits obtained when the number of separately held chunks is unclear. Reasons why pure capacity estimates fall within a narrow range are discussed and a capacity limit for the focus of attention is proposed.