Bayesain Hypothesis of Selective Attention - Raw 2011 poster

  • 742 views
Uploaded on

The aim of the study is to understand the process of target averaging during the selection process. We analyzed the probability to select the target after a fixation outside ROIs from the duration of …

The aim of the study is to understand the process of target averaging during the selection process. We analyzed the probability to select the target after a fixation outside ROIs from the duration of fixations and the distance to the target. We aimed to respond to the question “is it possible to predict the selected area?” . In this study we tested the presence of information in non-ROI fixation data about the occurrence of a target at the next saccade. A classification algorithm was trained to predict the target vs. non-target outcome (dependent variable) of a saccade from summary statistics of fixation data (covariates). We claim that significantly accurate predictions are substantial evidence to support the hypothesis of "presence of information".

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
742
On Slideshare
0
From Embeds
0
Number of Embeds
30

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. DISCUSSIONBIBLIOGRAPHYINTRODUCTIONMETHODSSubjects22 subjects (12 females and 10 males)aged 25-40 were trained by apsychologist on the TMTB test [1,2,3].Subjects were seated at viewingdistance of 78cm from a 32‖ colormonitor (51cmx31cm). Eye positionwas recorded using ASL 6000 system.RESULTSBAYESIAN HYPOTHESIS ON SELECTIVE ATTENTIONGiacomo Veneri1,2, Emanuele Olivetti3,4, Paolo Avesani3,4, Antonio Federico2, Alessandra Rufa1,2BACKGROUNDVisual search is an activity that enables humans to explore the real world. It depends on sensory,perceptual and cognitive processes. Given the visual input, during visual search, it is necessary toselect some aspects of input in order to move to the next location. The aim of this study is tounderstand the selection process, that modulates the exploration mechanism, during the executionof a high cognitively demanding task such as a simplified trial making B test (sTMTB). ThesTMTB is a neuropsychological instrument when number and letters should be connected eachother in numeric and alphabetic order (1-A-2-B-3-C-4-D-5-E).OBJECTIVEThe aim of the study is to understand the process of target averaging during the selection process.We analyzed the probability to select the target after a fixation outside ROIs from the duration offixations and the distance to the target. We aimed to respond to the question ―is it possible topredict the selected area?‖ . In this study we tested the presence of information in non-ROIfixation data about the occurrence of a target at the next saccade. A classification algorithm wastrained to predict the target vs. non-target outcome (dependent variable) of a saccade fromsummary statistics of fixation data (covariates). We claim that significantly accurate predictionsare substantial evidence to support the hypothesis of "presence of information".Bayesian hypothesis[1] G. Veneri, P. Piu, F. Rosini, P. Federighi, A. Federico, A. Rufa - Automatic Eye Fixations Identification Based On Analysis Of Variance And Covariance Pattern RecognitionLetters; Volume 32, Issue 13, 1 October 2011, Pages 1588-1593,[2] G. Veneri, E. Pretegiani, F. Rosini, P. Federighi, A. Federico, A. RUFA - Evaluating The Human Ongoing Visual Search Performance By Eye Tracking Application AndSequencing Tests ‗ Computer Methods And Programs In Biomedicine[3] G. Veneri, P. Federighi, F. Rosini, A. Federico, A. RUFA - Influences Of Data Filtering On Human-computer Interaction By Gaze-contingent Display And Eye TrackingApplications (2010) Computers In Human Behavior , Vol.26, 1555.Distribution probability1. Eye tracking & Vision Applications Lab - University of Siena, Italy2. Department of Neurological and Behavioural Sciences - University of Siena , Italy3. Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy4. NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation - University of Trento , ItalyThe predictions of the classifier supported the hypothesis of accurate classification, thus confirming the “Presence of information“.Results reported that the model learned the exploration strategies and visual search was efficient when the target was attended and the observers internal model matched theenvironment. We conclude that: subjects planned the entire target averaging cycle before the first fixation and selected the next location under a Bayesian process, thanks toinformation available in the peripheral vision; this process has a preferential pathway when the attended target is close. Our Bayesian framework and results are very similar to themodel proposed by Najemnik & Geisler [4,5]. Our model was able to correctly discriminate the 65% of fixation in target vs. non-target.The Trial making Task Part B : thesubject is required to connect 1-A-2-B-3-C-4-D-5-E.cdfPreplannedP=35%Long fixP=26%AttendedLong fixP=25%0.020.020.020.020.020.020.020.020.020.040.040.040.040.040.040.040.040.040.040.060.060.060.060.060.060.060.060.080.080.080.080.080.080.10.10.10.120.120.120.14FixdurationDistance Ratio1 2 3 4 5 6 7 8 9 10501001502002503003504004505000.04 0.040.05 0.050.060.06 0.060.070.070.07 0.070.080.080.080.080.090.090.090.10.10.10.110.110.110.120.120.120.13FixdurationDistance Ratio1 2 3 4 5 6 7 8 9 1050100150200250300350400450500http://www.evalab.unisi.ithttp://nilab.cimec.unitn.it/blog/ With the financial support of the Tuscany region http://www.progettosissi.itPresence of informationThe fixation from a non-ROI area and preceding a saccade were summarized in a vector of 6covariates: fixation duration, varX, varY, covXY, distance to ROI, distance to target. A sample of288 vectors was extracted from the fixations of the whole set of subjects of this study and definedthe whole dataset for the classification problem. A subset of 50 vectors resulted in saccadesactually reaching the target and 238 in non-target. The dataset was split in two groups, the trainset and the test set, the first for training/fitting the classifier and the second to assess how accuratewere its predictions. The classification algorithm adopted in this study is the "Gaussian naiveBayes" (GNB) [0]. The GNB algorithm assumes: independence between each pair of the 6covariates and a Gaussian likelihood for each covariate. Under those assumptions the GNBalgorithm is derived directly from the Bayes theorem. The predicted outcome of the classifier isthen:where ci and ci2 are the maximum likelihood estimates of each covariate for each class over thetrain data and p(c), c={target, non-target}, is the prior probability for each value of the dependentvariable.We used a 10-fold cross-validation, a resampling approach, for assessing the results andsplit the dataset in 10 parts via stratified sampling. At each step one part of the dataset, the test set,was kept out for computing the confusion matrix and the remaining nine parts were used fortraining the GNB algorithm. The confusion matrix summarises the agreement of the predictionsand actual values over each value of the dependent variable. The test of independence proposed in[6] (implementation: https://github.com/emanuele/Bayes-factor-multiclass) was carried out on theaggregated confusion matrix.[4] Najemnik, J., & Geisler, W. S. (2005). Optimal Eye Movement Strategies In Visual Search. Nature, 434 , 387–91[5] Najemnik, J., & Geisler, W. S. (2008). Eye movement statistics in humans are consistent with an optimal search strategy. Journal of Vision, 8 , 1–14.[6] Kass, Robert E. and Raftery, Adrian E.(1995) Bayes Factors, Journal of the American Statistical Association, 430, 773—795[7] Casella, George and Moreno, Elìas (2009) Assessing Robustness of Intrinsic Tests of Independence in {Two-Way} Contingency Tables, Journal of the American StatisticalAssociation, 104, 1261—1271[8] Domingos, Pedro and Pazzani, Michael, (1997) On the Optimality of the Simple Bayesian Classifier under Zero-One Loss, Machine Learning, 29, 103-130Presence of information: the logarithm of the Bayes factor against the null hypothesis of independence ranged between 85 and 291.According to [6] such values for the Bayes factor correspond to a strong evidence supporting the dependence between predictions and actualvalues, thus confirming sufficiently accurate classification. The average per class accuracy was 65%.