We consider a grid of possible fixations, spaced 0.25° apart, and compute a strategy prediction map. When estimates are used, the global strategy is to maximize the expected information rather than the actual information. This discrepancy could be due to an incorrect estimation of the visibility of contours in the periphery (i.e., uncertainty in the periphery is even greater, drawing predictions further out), or it Using this much stricter test, how well does the global strategy predict human fixations?
The fixation error between human fixations and the global strategy predictions is significantly smaller than the fixation error of the random strategy.Figure 5 Fixation error. 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 Observers were allowed to study the shape pair until they reached a decision about which member of the pair matched the shape they learned ( Figure 1B). 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 see here
Download citation file: RIS (Zotero) EndNote BibTex Medlars ProCite RefWorks Reference Manager © 2017 Association for Research in Vision and Ophthalmology. × Get Permissions Supplements Abstract How do we decide where Brackets indicate a significant difference between values, as determined by bootstrapped 95% confidence intervals. The size of a pooling neighborhood r(E) depends on the eccentricity E or distance from the current fixation. Eye velocity was computed as the rate of displacement within a symmetric 10-ms window centered on the sample of interest.
Subscribe Personal Sign In Create Account IEEE Account Change Username/Password Update Address Purchase Details Payment Options Order History View Purchased Documents Profile Information Communications Preferences Profession and Education Technical Interests Need This performance level suggests that the task was achievable yet difficult enough to encourage efficient information gathering during the learning phase. Mean amplitudes of object-exploring saccades ranged from 2.38° to 4.44° Quantitatively, r(E)=s(E+E2),s=0.1,E2=0.8,(A1)where E2 is the eccentricity at which acuity drops to half its value in the fovea and s is the slope. In fact, the strategies are sometimes worse than random!
Aside from possible individual differences, these parameters only provide an approximation for how the visual system is able to characterize orientation along the bounding contour of a shape. The resulting probability distributions over orientation are flat at each location, and uncertainty (entropy) is high everywhere. (B) A sample fixation (+) places smaller pooling neighborhoods near the top of the The computation of this prediction is intensive and requires an exhaustive evaluation of all possible fixations before a decision is made. http://www.forbes.com/sites/gregsatell/2015/02/13/where-to-look-for-the-next-big-thing/ 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
How might we formalize this idea? Information-theoretic approaches Information theory provides a convenient framework in which we may formalize our intuitions and observations of eye-movement behavior. Although this approximation has some undesirable properties (such CLOSE More Options Quote of the Day If you can put Boomers and Millennials together in the same place and with the right What if observers are combining the local uncertainty strategy with a simple centroid prior when planning fixations? Local uncertainty + centroid To test this idea, we will assume that observed fixation Some hand selection of the stimuli was performed to discard featureless or circular shapes.
In our ROC analysis, we find that saliency has the least power to predict human fixation locations. https://www.ncbi.nlm.nih.gov/pubmed/17461684 If you originally registered with a username please use that to sign in. Adding a centroid bias to the local uncertainty prediction results in a significant improvement over other strategies (black symbols). Using an ROC analysis, therefore, provides much better insight into how well different strategies align with human fixation behavior.
Use of this web site signifies your agreement to the terms and conditions. The few trials in which this pattern is violated are not included in the analysis. 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 Please check your email address / username and password and try again.
Further analysis revealed that although fixations may cluster near boundaries on average, they often fall outside of the boundary; 8.4% to 27.6% of fixations landed outside object boundaries depending on observer. Several studies suggest that rather than being programmed one at a time, a sequence of fixations may be planned at once (Caspi et al., 2004; McPeek, Skavenski, & Nakayama, 2000). If we instead use a “smart” random strategy as our baseline measure to factor out this bias, we can better gauge the power of different strategies to predict individual fixations within The absolute scale of the donut distribution might suggest that observers are making fixations within object boundaries.
This suggests that the local uncertainty signal is powerful. 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 Each displayed shape was scaled to measure 12.5° along the diagonal of its bounding rectangle. The subject's task is to “learn” the shape during this brief presentation, and its large size
NCBISkip to main contentSkip to navigationResourcesAll ResourcesChemicals & BioassaysBioSystemsPubChem BioAssayPubChem CompoundPubChem Structure SearchPubChem SubstanceAll Chemicals & Bioassays Resources...DNA & RNABLAST (Basic Local Alignment Search Tool)BLAST (Stand-alone)E-UtilitiesGenBankGenBank: BankItGenBank: SequinGenBank: tbl2asnGenome WorkbenchInfluenza VirusNucleotide We move our eyes, often purposefully, to actively gather the sensory information we need to complete a task. The global strategy is to move to locations that maximize the total information gained (i.e., reduce uncertainty about all edge orientations) with each fixation. (B) The global strategy predicts saccade amplitude We measured eye movements as observers performed a shape-learning and -matching task, for which the task-relevant information was tightly controlled.
Saccade amplitude and fixation distributions for the (A) saliency and (B) local uncertainty strategies.View OriginalDownload Slide Fixation error Figure 10A plots the fixation error for all strategies. Lathrop; Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants. 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 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
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 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. In reality, the visual system cannot possibly compute the global strategy in this manner. 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.
Search for other works by this author on: Oxford Academic PubMed Google Scholar Bioinformatics (2007) 23 (13): i104-i114. We produced saliency prediction maps for our stimuli ( Figure 8A) using the model developed by Itti and Koch (2000), which is available on the web. Edwards Deming, Margaret Mead, Ben Carson, Isaac Asimov, George Washington Carver, E. 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).
The probability of target presence is monitored at every location, and the target is “found” when probability at one location exceeds a predetermined threshold. Within a pooling neighborhood, we count the number of occurrences of different veridical edge orientations and create a histogram (or probability distribution after normalization) of the different orientations at that location coin it the perceptive hypercolumn. The responses of a population of oriented filters within a neighborhood of radius r( E) are represented as a histogram over eight orientations.
Don't have an account? With each fixation, the observer takes a foveated measurement of the orientations in the stimulus. We can compute this prediction by evaluating all possible next fixation locations and selecting the one that yields the greatest gain in total information (i.e., greatest reduction in total uncertainty).