{"id":82,"date":"2018-12-23T01:13:54","date_gmt":"2018-12-23T01:13:54","guid":{"rendered":"https:\/\/metra.psychologyresearch.co.uk\/?page_id=82"},"modified":"2019-02-17T23:26:50","modified_gmt":"2019-02-17T23:26:50","slug":"svm-classification","status":"publish","type":"page","link":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/svm-classification\/","title":{"rendered":"SVM classification"},"content":{"rendered":"

Analyses flow step 5<\/strong>: For further quantitative description of how gaze travels across the painting at individual and group level, we adopted methods of scan path comparison<\/strong> (summarised in K\u00fcbler 2016), dealing with data-specific biases in art-works, noisy measurements, subsequent visits to the same or nearby image regions, and reliance on a priori defined semantic regions of interest (ROIs). Our methods are based on classification of subsequence frequencies (\u2018Sally\u2019: Rieck, Wressnegger, & Bikadorov, 2012) in longer transition sequences (such as exploring Pollock paintings) and used machine learning techniques to best discriminate the two paintings based on many combinations of sequence and sub-sequence transcriptions, thus going beyond Markov Chain models.<\/p>\n

We implemented a standard technique for mapping strings to a vector space (\u2018bag-of-words model\u2019<\/strong>). Number of letters (alphabet size A) correspond to ROIs, and letter sub-sequences to sparsely occurring events whose frequency is possible to count (n-gram of size N). In this model, a Markov chain is represented with N = 2, and is tested against many other combinations of A and N. Scan path are converted into their string representation, using a variety of algorithms to automatically determine amount of gaze assigned to probability bins (ROIs), which are then transcribed into letters as a function of A. We tested several automatic binning approaches, using (normalised) pixel coordinates of images to define borders of ROIs: distributing gaze data into equally sized bins (grid approach), or into probability chunks having equal amount of gaze assigned (percentile approach) or considering saccadic event amplitude.<\/p>\n

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Normalised scan path reconstruction is used for automatic grid binning of eye movement data to determine ROIs (participant 11). Green: \u2018Blue Poles\u2019; Red: \u2018Mural\u2019<\/em><\/p>\n

We implemented a standard technique for mapping strings to a vector space (\u2018bag-of-words model\u2019<\/strong>). Number of letters (alphabet size A) correspond to ROIs, and letter sub-sequences to sparsely occurring events whose frequency is possible to count (n-gram of size N). In this model, a Markov chain is represented with N = 2, and is tested against many other combinations of A and N. Scan path are converted into their string representation, using a variety of algorithms to automatically determine amount of gaze assigned to probability bins (ROIs), which are then transcribed into letters as a function of A. We tested several automatic binning approaches, using (normalised) pixel coordinates of images to define borders of ROIs: distributing gaze data into equally sized bins (grid approach), or into probability chunks having equal amount of gaze assigned (percentile approach) or considering saccadic event amplitude.<\/p>\n

After segmenting and transcribing data into string sequences, we trained Support Vector Machines (SVM)<\/strong> on pooled data using 10-fold cross-validation (10% of the data used for validation, 90% for training), using all combinations of A (2-26) and N (1-10) to discriminate between two Pollock paintings. The highest accuracy (87.5%) was achieved with A=8 and N=7 using the amplitude of saccadic events to define ROIs. The two paintings were best discriminated using remarkably long eye movements sequences (7-step) and 8 regions of interest. Correspondingly, using our data-driven definition of ROIs derived in analysis flow step 3 (Fixation Hotspot<\/strong>), the classifier was able to distinguish the two Pollock paintings based on triplet subsequence of eye movements (N=3) and 5 most visited ROIs (A=5): the accuracy of the classifier was approximately 80%. Our results suggest that in complex scenarios such as free viewing of abstract paintings for a prolonged time, Markov chain models (N = 2) do not perform as well as using extended n-gram sequence analysis.<\/p>\n

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Discrimination performance of SVMs (in colour code, bright yellow patches indicating the highest accuracy of the classifier) to discriminate the two Pollock paintings, Left: using <\/em>amplitude of saccadic events, and Right: using \u2018Fixation Hotspot\u2019,<\/em> suggesting that longer eye movement are needed for good performance.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Analyses flow step 5: For further quantitative description of how gaze travels across the painting at individual and group level, we adopted methods of scan path comparison (summarised in K\u00fcbler 2016), dealing with data-specific biases in art-works, noisy measurements, subsequent visits to the same or nearby image regions, and reliance on a priori defined semantic … <\/p>\n

Continue reading “SVM classification”<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-82","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/wp-json\/wp\/v2\/pages\/82","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/wp-json\/wp\/v2\/comments?post=82"}],"version-history":[{"count":7,"href":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/wp-json\/wp\/v2\/pages\/82\/revisions"}],"predecessor-version":[{"id":176,"href":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/wp-json\/wp\/v2\/pages\/82\/revisions\/176"}],"wp:attachment":[{"href":"https:\/\/metra.psychologyresearch.co.uk\/index.php\/wp-json\/wp\/v2\/media?parent=82"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}