Wilson Topics Intuition, Look, Mind, Next, Tell, Thinking, Where, Will BrainyQuote Desktop BrainyQuote Mobile Site Home Authors Topics Quote Of The Day Pictures Top 100 Quotes Professions Birthdays Social BQ on The computation of this prediction is intensive and requires an exhaustive evaluation of all possible fixations before a decision is made. Brachmann 1Department of Biomedical Engineering, 2Department of Medicine, 3Department of Molecular Biology & Biochemistry, 4Departments of Biological Chemistry, and Pathology & Laboratory Medicine, 5Department of Computer Science and 6Institute for Genomics This correlation is likely present in all natural stimuli.
This strategy is analogous to the MAP prediction described by Najemnik and Geisler (2005). Analysis Both the saliency and local uncertainty strategies produce a donut-shaped distribution, but neither strategy shows a For comparison, the green and blue lines represent the local and global uncertainty strategy predictions. It is unclear how the visual system would do this without complex computation. Lathrop 1Department of Biomedical Engineering, 2Department of Medicine, 3Department of Molecular Biology & Biochemistry, 4Departments of Biological Chemistry, and Pathology & Laboratory Medicine, 5Department of Computer Science and 6Institute for Genomics http://ieeexplore.ieee.org/document/6613519/
During natural, active vision, we move our eyes to gather task-relevant information from the visual scene. Saccade amplitude and fixation distributions for the (A) saliency and (B) local uncertainty strategies.Figure 9 Predicted fixation behavior for saliency and local uncertainty strategies. Red points indicate first fixations to the shape.View OriginalDownload Slide Fixated locations were found to be spatially distributed in a “donut” shape for three of four subjects ( Figure 3C). One strategy is to move the eyes to locations that maximize the total information gained about the shape, which is equivalent to reducing global uncertainty.
The histogram is normalized by the total number of edgelets within the radius so that all the histogram entries sum to 1. At first inspection, human eye movements appear “optimal” (reduce global uncertainty); however, our rigorous analysis of individual fixation placement reveals that an approximate, local rule may actually govern eye-movement decisions. Methods doi: 10.1167/7.3.6. Information theory provides an elegant framework for investigating how visual stimulus information combines with prior knowledge and task goals to plan an eye movement.
The area under the ROC curve is noted on each plot.Figure 6 Comparison of human fixation sequence to the global strategy. (A) One observer's fixation sequence superimposed on a shape (left) and The global strategy might be to fixate between them to maximize information about both locations, whereas the local uncertainty strategy would fixate the one with slightly higher uncertainty (more information). Next, observers fixated a dynamic 0.25° dot that blinked on for 1,500 ms and swept out a 5 × 5 calibration grid that covered the stimulus space in steps of 5° One strategy is to move the eyes to locations that maximize the total information gained about the shape, which is equivalent to reducing global uncertainty.
We chose the first five because human observers typically made three to five fixations per shape. Subject 3 repeated the entire experiment, and her second-pass scan paths were not necessarily similar to her first. More likely, it uses estimates (e.g., heuristics or learned priors) to determine the benefit of each possible next fixation. We use a psychophysical experiment that controls the observer's task and the task-relevant visual information, as we measure eye movements.
Eye velocity was computed as the rate of displacement within a symmetric 10-ms window centered on the sample of interest. That is, observers who made large saccades tended to fixate for shorter periods and vice versa. The 95% confidence intervals attained with bootstrapping allow us to determine which points are significant. Individual fixations are compared against strategy predictions using a signal detection theory approach.
We choose r( E) to be equal in size to a “perceptive hypercolumn,” as described by Levi et al. (1985) for vernier acuity in the periphery. This suggests that the local uncertainty signal is powerful. The shapes were novel and abstract silhouettes, created by randomly rotating and superimposing four randomly selected objects from the Snodgrass and Vanderwart (1980) data set of common objects. The significance of the alignment is assessed by bootstrapping (1,000 iterations) to get 95% confidence intervals of the fixation error.
Search for other works by this author on: Oxford Academic PubMed Google Scholar Richard H. See 1.View OriginalDownload Slide With each successive fixation, we update what is known about the stimulus at each point (posterior distribution) by multiplying the new measurement distribution (likelihood) at that point Figure 4A illustrates a sequence of fixations based on this “global” strategy prediction. The saliency strategy is again a poor predictor.
Jonas Salk Quotes Intuition will tell the thinking mind where to look next. Using the information-theoretic model, we can probe how information is used to plan eye movements to the stimulus. Recall that this metric ignores the sequence in which fixations are made and simply computes the distance between observed and predicted fixation locations on a given trial.
As the evidence of orientations accumulates with successive fixations, we can represent the uncertainty of shape knowledge at any point in time by computing a resolution-dependent entropy (RDE) map ( Figure Adding a centroid bias to the local uncertainty prediction results in a significant improvement over other strategies (black symbols). It is well known that humans make fixations toward the centroids of small shapes (Melcher & Kowler, 1999). A prediction is considered significantly better than chance if the 95% confidence interval for the AUC does not include 0.5.
By including a range from easy to difficult trials in each block, subjects were motivated to continue studying the shapes effectively. As before, we compute the fixation error and ROC curves for these two strategies. Edwards Deming, Margaret Mead, Ben Carson, Isaac Asimov, George Washington Carver, E. When viewing natural images, observers tend to fixate regions with higher local contrast, such as regions near object borders or edges (Reinagel & Zador, 1999). Due to the role of stimulus
The fixation error between human fixations and the global strategy predictions is significantly smaller than the fixation error of the random strategy.View OriginalDownload Slide Note that this error measure ignores the The map is rescaled from 0 to 1, and the prediction value is taken as the maximum value that falls within 1° of the human fixation ( Figure 6A), following the A more rigorous treatment of the model can be found in the 1. In the next section, we first look at the general pattern of human eye movements in our task Our internal goals influence our eye-movement behavior, attention, and, ultimately, what we perceive and remember.
The absolute scale of the donut distribution might suggest that observers are making fixations within object boundaries. Updated knowledge becomes prior knowledge for the next fixation. We estimate the orientation information at a point on the stimulus by constructing a pooling neighborhood whose size depends on distance from the current fixation point ( Figure 2B). Notice in Figure 9B that the predicted spatial fixation distribution for this strategy is quite diffuse.
We determine “not-fixated” locations by simply evaluating locations predicted by the random strategy. Najemnik and Geisler (2005) designed a simple yet clever search experiment. The ability to resolve orientations degrades as a function of eccentricity. How do we decide where to look? The final landing point of a saccade is biased by mechanical and physiological constraints, but more important, the properties of the stimulus play a
Defining and quantifying information for a variety of tasks remains one of the great challenges of vision research. Summary and conclusion Information theory provides an elegant framework for conceptualizing and modeling