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John M. Henderson

Bio: John M. Henderson is an academic researcher from University of California, Davis. The author has contributed to research in topics: Eye movement & Fixation (visual). The author has an hindex of 76, co-authored 216 publications receiving 20660 citations. Previous affiliations of John M. Henderson include University of Massachusetts Amherst & University of Alberta.


Papers
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Journal ArticleDOI
TL;DR: An original approach of attentional guidance by global scene context is presented that combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.
Abstract: Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an original approach of attentional guidance by global scene context. The model comprises 2 parallel pathways; one pathway computes local features (saliency) and the other computes global (scene-centered) features. The contextual guidance model of attention combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.

1,613 citations

Journal ArticleDOI
TL;DR: Current approaches and empirical findings in human gaze control during real-world scene perception are reviewed.

1,318 citations

Journal ArticleDOI
TL;DR: Three areas of high-level scene perception research are reviewed, focusing on the role of eye movements in scene perception and the influence of ongoing cognitive processing on the position and duration of fixations in a scene.
Abstract: Three areas of high-level scene perception research are reviewed. The first concerns the role of eye movements in scene perception, focusing on the influence of ongoing cognitive processing on the position and duration of fixations in a scene. The second concerns the nature of the scene representation that is retained across a saccade and other brief time intervals during ongoing scene perception. Finally, we review research on the relationship between scene and object identification, focusing particularly on whether the meaning of a scene influences the identification of constituent objects.

929 citations

Journal ArticleDOI
TL;DR: In this paper, a model of scene perception and long-term memory was proposed, which showed that relatively detailed visual information is retained in memory from previously attended objects in natural scenes and that participants successfully detected type and token changes (Experiment 1) or token and rotation changes to a target object when the object had been previously attended but was no longer within the focus of attention when the change occurred.
Abstract: The nature of the information retained from previously fixated (and hence attended) objects in natural scenes was investigated. In a saccade-contingent change paradigm, participants successfully detected type and token changes (Experiment 1) or token and rotation changes (Experiment 2) to a target object when the object had been previously attended but was no longer within the focus of attention when the change occurred. In addition, participants demonstrated accurate type-, token-, and orientationdiscrimination performance on subsequent long-term memory tests (Experiments 1 and 2) and during online perceptual processing of a scene (Experiment 3). These data suggest that relatively detailed visual information is retained in memory from previously attended objects in natural scenes. A model of scene perception and long-term memory is proposed. Because of the size and complexity of the visual environments humans tend to inhabit, and because high-acuity vision is limited to a relatively small area of the visual field, detailed perceptual processing of a natural scene depends on the selection of local scene regions by movements of the eyes (for reviews, see Henderson & Hollingworth, 1998, 1999a). During scene viewing, the eyes are reoriented approximately three times each second by saccadic eye movements to bring the projection of a local scene region (typically a discrete object) onto the area of the retina producing the highest acuity vision (the fovea). The periods between saccades, when the eyes are relatively stationary and detailed visual information is encoded, are termed fixations and last an average of approximately 300 ms during scene viewing. During each brief saccadic eye movement, however, visual encoding is suppressed (Matin, 1974). Thus, the visual system is provided with what amounts to a series of snapshots (corresponding to fixations), which may vary dramatically in their visual content over a complex scene, punctuated by brief periods of blindness (corresponding to saccades).

545 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
Abstract: In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.

6,882 citations

Journal ArticleDOI
TL;DR: The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined.
Abstract: Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed.

6,656 citations