Home > Where To > Where To Look Next?

Where To Look Next?

With each fixation, the observer takes a foveated measurement of the orientations in the stimulus. Purchase this article with an account. Predicting the fixation sequence that maximized information gain is more computationally intensive, although there is some evidence that humans may indeed plan more than one fixation at a time.   Figure We evaluate each strategy against the smart random baseline.  Saliency Given that the shapes in the psychophysical task are novel, top–down influences such as familiarity should be minimized and observers may

Get Help About IEEE Xplore Feedback Technical Support Resources and Help Terms of Use What Can I Access? This early uncertainty map would then need to be combined with a stimulus-centered representation that incorporates knowledge gained from previous fixations, possibly mediated by mechanisms that pool orientation at a single In this active setting, information-based models of eye movements may do a better job. Using the information-theoretic model, we can probe how information is used to plan eye movements to the stimulus.

About Bioinformatics Editorial Board Author Guidelines Facebook Twitter Purchase Recommend to your Library Advertising and Corporate Services Journals Career Network Online ISSN 1460-2059 Print ISSN 1367-4803 Copyright © 2017 Oxford University 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 We have shown that this shape difference quantity scales with human shape discrimination performance (Renninger, Verghese, & Coughlan, 2005a). Hits and false alarms are plotted with changing threshold, sweeping out the ROC curve.

To explore design choices that affect this desirable behavior, five novel and five existing Active Learning techniques, together with three control methods, were tested on 57 previously unknown p53 cancer rescue 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 Conversely, the higher the value of P( h i( F)| x i, E i( F)) for any component value x i = z, the more likely that the true value of Five hundred shape pairs were created for the experiment.

Such manipulations to the prediction map are easily explored, but it is probably better to do so after the fundamental parameters for contour processing as a function of eccentricity have been Introduction Methods Experimental results Strategy analysis Other strategies General discussion Summary and conclusion Appendix A: Model details Acknowledgments References Free Research Article| February 2007Where to look next? For an intuitive interpretation of the likelihood function, notice that if h i( F) is 0 for some component x i = z, then no edgelet in the local population has The magnitude of the AUC values demonstrate that, although far from perfect, the global strategy has some power to predict human eye movements.  Figure 7View OriginalDownload Slide Global strategy versus smart random

To our knowledge, there are no mechanical factors that restrict the length of a saccade, but it is possible that energy constraints favor shorter saccades or that time pressure leads observers We use a psychophysical experiment that controls the observer's task and the task-relevant visual information, as we measure eye movements. Jonas Salk Biography Author Profession: Scientist Nationality: American Born: October 28, 1914 Died: June 23, 1995 Links Find on Amazon: Jonas Salk Cite this Page: Citation Related Authors Carl Sagan, Neil Please check your email address / username and password and try again.

However, our prediction map may not be correct in detail. We also exclude fixations with dwell times that are less than 50 ms, as they do not fall within the primary mode of the population distribution (see Figure 3B) and may These test shapes subtended 4°. After each fixation, knowledge of the stimulus is updated and a new prediction is computed.

We compute “false alarms” as the probability that the prediction value exceeds threshold at locations not fixated by the observer. The initiation of a fixation was marked if eye velocity dropped below 10°/s. As Raj, Geisler, Frazor, and Bovik (2005) demonstrated, taking samples (fixations) that minimize contrast entropy provides the best information for the image reconstruction of natural scenes. Using computational models, we probe the underlying strategies used by observers when planning their next eye movement.

For example, saccades to a simple shape or object often land near the centroid of that object (Melcher & Kowler, 1999; Vishwanath & Kowler, 2003). Stimuli that cleanly isolate local uncertainty and saliency effects would be needed to determine if the visual system makes use of only one strategy or if it uses both strategies.  Maximize If the goal of eye movements is to gather task-relevant information, then the best strategy is obvious: fixate locations that maximize the total information gained about the contour orientations. Further research is needed to determine if different strategies are used under different conditions or whether observers are able to use hybrid strategies.  Appendix A: Model details Probabilistic model Our psychophysical

Danziger Samuel A. This clustering of the first fixation for very different shapes may indicate that it may simply be a localizing saccade that is mostly independent of detailed shape information. Using this much stricter test, how well does the global strategy predict human fixations?

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 area under the ROC curve is noted on each plot.View OriginalDownload Slide Next, we compute ROC curves and measure the area under the curve (AUC) to assess the power of Don't have an account? Adding a centroid bias to the local uncertainty prediction results in a significant improvement over other strategies (black symbols). These models may help determine which expensive biological data are most useful to acquire next.

We do so by excluding the preview and localizing fixations, which will not be predicted by information strategies. Internal noise may also degrade the information signal over time, whereas we have assumed perfect memory. Eye movements reduce local uncertainty. By using abstract shapes in isolation, we avoid the influence of cognitive information on eye-movement planning, such as object familiarity or scene context, while still capturing edges—a fundamental feature of natural

Here, we discuss several aspects of our approach and findings.  Observer variability On a given trial, two observers may exhibit very different scan paths. Journal of Vision 2007;7(3):6. Observers' behavior may appear highly similar to this strategy, but a rigorous analysis of sequential fixation placement reveals that observers may instead be using a local rule: fixate only the most Alternatively, straight edges will produce energy at a single orientation or very peaked distributions (low entropy).

Paul Critchlow Documents Authors Tables Log in Sign up MetaCart Donate No document with DOI "10.1.1.841.4234" The supplied document identifier does not match any document in our repository. We measured eye movements as observers performed a shape-learning and -matching task, for which the task-relevant information was tightly controlled. 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 However, positional uncertainty is roughly 10-fold less than orientation uncertainty across eccentricities (Levi et al. 1985; White, Levi, & Aitsebaomo, 1992).

coin it the perceptive hypercolumn. Information theory provides an elegant framework for investigating how visual stimulus information combines with prior knowledge and task goals to plan an eye movement. Isolated maxima will predict a fixation regardless of neighboring activity. Lathrop Richard H.

To better understand this difference, imagine two nearby locations that have similar prediction values. Each histogram is intended to be analogous to the initial distribution of neural responses to the stimulus across a hypercolumn of orientation-selective cells in visual cortex (Lee & Yu, 2000).   We hypothesize that the decision strategy remains fixed but that the task-relevant information changes. Brackets indicate significant increases between AUC values.View OriginalDownload Slide Receiver operating characteristic Figure 10B plots the ROC curves for the global, saliency, and local uncertainty strategies, as compared with the smart

Register You could not be signed in. Thus, it is unclear how the AUC would be affected by adding eccentricity factors.  Even without eccentricity factors, our analysis does show some weak predictive power for the saliency strategy, but We have yet to unravel what decision strategies underlie the choice of human fixation locations.  Our approach In this article, we use information theory to probe the underlying decision strategies that 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).

We chose the first five because human observers typically made three to five fixations per shape. These measured eye-movement behaviors are similar to what has been found for viewing of naturalistic stimuli (Bahill, Adler, & Stark, 1975) and in search tasks (Najemnik & Geisler, 2005). Generated Sat, 18 Mar 2017 00:04:47 GMT by s_hv1050 (squid/3.5.23) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.10/ Connection Our stimuli and stimulus generation code are available on the first author's web site.