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matlab code oneshotdata_demo.m  (MathWorks Inc)
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MathWorks Inc matlab code oneshotdata_demo.m
Task and Example Stimuli. Crowdsourced observers judged whether the test (embedded in circle) was in the same categorical class as the surrounding sample(s). In 20 trials, each observer (500 total) judged unique combinations of test and sample(s) to 20 novel object classes (only 4 shown). On each trial, the stimulus frame showed the test embedded in 1 (A, B) or 16 (C,D) surrounding samples in object classes that tended to vary more (C,B) or less (A,D). The test itself varied in its appearance from the samples in 1 of 25 bins based on its skeletal similarity to an original base shape (see Stimulus Frame in Experimental Design, Materials, and Methods ). The stimulus frames and human responses are available through Zenedo. By varying data collection parameters listed in the Specifications Table ( obj_num, cont_num, distbin_num, and shape_num ), the <t>MATLAB</t> code oneSHOTdata_DEMO.m shows how to generate a stimulus frame and recover the corresponding human response from the raw data for an individual trial number. As examples, stimulus frame (A) was rendered with object number 2 ( obj_num = 2 , which had surrounding samples that tended to appear more similar, lower variability), with only 1 surrounding sample ( cont_num = 1 ), with a test that had a smaller skeletal parameter distance from the base shape ( distbin_num = 8 ), and a shape 3 of 10 for this condition ( shape_num = 3 ) while stimulus frame (B) was rendered with object number 1 ( obj_num = 1 , with higher variability across samples), with 16 surrounding samples ( cont_num = 16 ), with a test that had a large skeletal parameter distance from the base shapes ( distbin_num = 25 ), and was shape 2 out 10 for this condition ( shape_num = 2 ). In (C) obj_num = 1 , cont_num = 16 , distbin_num = 25 , and shape_num = 1 . In (D) obj_num = 5 , cont_num = 1 , distbin_num = 12 , and shape_num = 6 .
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Task and Example Stimuli. Crowdsourced observers judged whether the test (embedded in circle) was in the same categorical class as the surrounding sample(s). In 20 trials, each observer (500 total) judged unique combinations of test and sample(s) to 20 novel object classes (only 4 shown). On each trial, the stimulus frame showed the test embedded in 1 (A, B) or 16 (C,D) surrounding samples in object classes that tended to vary more (C,B) or less (A,D). The test itself varied in its appearance from the samples in 1 of 25 bins based on its skeletal similarity to an original base shape (see Stimulus Frame in Experimental Design, Materials, and Methods ). The stimulus frames and human responses are available through Zenedo. By varying data collection parameters listed in the Specifications Table ( obj_num, cont_num, distbin_num, and shape_num ), the MATLAB code oneSHOTdata_DEMO.m shows how to generate a stimulus frame and recover the corresponding human response from the raw data for an individual trial number. As examples, stimulus frame (A) was rendered with object number 2 ( obj_num = 2 , which had surrounding samples that tended to appear more similar, lower variability), with only 1 surrounding sample ( cont_num = 1 ), with a test that had a smaller skeletal parameter distance from the base shape ( distbin_num = 8 ), and a shape 3 of 10 for this condition ( shape_num = 3 ) while stimulus frame (B) was rendered with object number 1 ( obj_num = 1 , with higher variability across samples), with 16 surrounding samples ( cont_num = 16 ), with a test that had a large skeletal parameter distance from the base shapes ( distbin_num = 25 ), and was shape 2 out 10 for this condition ( shape_num = 2 ). In (C) obj_num = 1 , cont_num = 16 , distbin_num = 25 , and shape_num = 1 . In (D) obj_num = 5 , cont_num = 1 , distbin_num = 12 , and shape_num = 6 .

Journal: Data in Brief

Article Title: A dataset for evaluating one-shot categorization of novel object classes

doi: 10.1016/j.dib.2020.105302

Figure Lengend Snippet: Task and Example Stimuli. Crowdsourced observers judged whether the test (embedded in circle) was in the same categorical class as the surrounding sample(s). In 20 trials, each observer (500 total) judged unique combinations of test and sample(s) to 20 novel object classes (only 4 shown). On each trial, the stimulus frame showed the test embedded in 1 (A, B) or 16 (C,D) surrounding samples in object classes that tended to vary more (C,B) or less (A,D). The test itself varied in its appearance from the samples in 1 of 25 bins based on its skeletal similarity to an original base shape (see Stimulus Frame in Experimental Design, Materials, and Methods ). The stimulus frames and human responses are available through Zenedo. By varying data collection parameters listed in the Specifications Table ( obj_num, cont_num, distbin_num, and shape_num ), the MATLAB code oneSHOTdata_DEMO.m shows how to generate a stimulus frame and recover the corresponding human response from the raw data for an individual trial number. As examples, stimulus frame (A) was rendered with object number 2 ( obj_num = 2 , which had surrounding samples that tended to appear more similar, lower variability), with only 1 surrounding sample ( cont_num = 1 ), with a test that had a smaller skeletal parameter distance from the base shape ( distbin_num = 8 ), and a shape 3 of 10 for this condition ( shape_num = 3 ) while stimulus frame (B) was rendered with object number 1 ( obj_num = 1 , with higher variability across samples), with 16 surrounding samples ( cont_num = 16 ), with a test that had a large skeletal parameter distance from the base shapes ( distbin_num = 25 ), and was shape 2 out 10 for this condition ( shape_num = 2 ). In (C) obj_num = 1 , cont_num = 16 , distbin_num = 25 , and shape_num = 1 . In (D) obj_num = 5 , cont_num = 1 , distbin_num = 12 , and shape_num = 6 .

Article Snippet: By varying data collection parameters listed in the Specifications Table ( obj_num, cont_num, distbin_num, and shape_num ), the MATLAB code oneSHOTdata_DEMO.m shows how to generate a stimulus frame and recover the corresponding human response from the raw data for an individual trial number.

Techniques: