{"block 1 (ad hoc)":{"train":{"words":[[1,4,13],[10,11,15],[3,2,13],[6,5,14],[9,12,15],[2,1,13],[8,5,14],[12,10,15],[1,3,13],[5,6,14],[11,9,15],[5,7,14],[4,2,13],[11,12,15],[7,8,14],[9,10,15],[6,7,14],[4,3,13],[6,8,14],[11,12,15],[2,3,13],[7,6,14],[3,1,13],[5,8,14],[10,9,15],[4,1,13],[9,11,15],[5,7,14],[10,11,15],[3,4,13],[8,7,14],[2,3,13],[12,10,15],[1,2,13],[8,6,14],[12,9,15]],"objects":[[1,4],[10,11],[3,2],[6,5],[9,12],[2,1],[8,5],[12,10],[1,3],[5,6],[11,9],[5,7],[4,2],[11,12],[7,8],[9,10],[6,7],[4,3],[6,8],[11,12],[2,3],[7,6],[3,1],[5,8],[10,9],[4,1],[9,11],[5,7],[10,11],[3,4],[8,7],[2,3],[12,10],[1,2],[8,6],[12,9]]},"test":{"words":[[1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11],[12]],"objects":[[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12]]},"accuracy":[0.5588,0.5294,0.6765,0.5588,0.2647,0.3529,0.7647,0.5588,0.2353,0.4412,0.6176,0.7059,0.4853,0.4926,0.5],"n_subj":[34],"label":["Gangwani2011-category-adhoc1"],"condition":["block 1 (ad hoc)"],"description":["Experiment 1 of Gangwani, Kachergis & Yu, 'Simultaneous Cross-situational Learning of Category and Object Names'. 3 words + 2 referents per training trial: the two visible objects' 1-to-1 names (words 1-12) plus one 1-to-many category label (words 13-15; label 13/14/15 -> objects 1-4/5-8/9-12). 36 training trials (shared schedule from category_xsl_order.txt; identical co-occurrence structure across all three blocks -- only the stimuli differ). `accuracy` is per-word human P(correct referent | word): positions 1-12 are 12-AFC name accuracy, positions 13-15 are 3-AFC category-membership accuracy (n = 34 canonical-order participants). Because the matrix is 15x12, get_perf()/mafc_test() only score words 1-12; score words 13-15 (and the ME-violation measure that is the paper's point) with score_gangwani2011_category() in tests/bakeoff_comparison/gangwani2011_category_fit.R. `test` holds the 12 name trials (12-AFC) only. `response_matrix` (15x12, row-normalised) is the full human choice distribution incl. the category labels. This condition: block 1 (ad hoc) -- category members share no perceptual feature."],"response_matrix":[[0.5588,0.0294,0.0294,0.1176,0.0588,0,0.0294,0.0294,0.0588,0,0.0294,0.0588],[0.0294,0.5294,0.0294,0.0882,0.0588,0,0,0.0882,0.0882,0,0.0882,0],[0.0294,0.1176,0.6765,0,0,0,0,0,0.0882,0,0.0882,0],[0.0882,0,0.0588,0.5588,0,0.0294,0,0,0.0294,0.0882,0.0588,0.0882],[0.0588,0.0588,0.0294,0.0588,0.2647,0.1176,0.0588,0.0588,0.0588,0.0588,0.0882,0.0882],[0.0294,0.0588,0.0882,0,0.1765,0.3529,0.0588,0.0294,0.0882,0.1176,0,0],[0,0,0.0294,0,0.0294,0.0294,0.7647,0,0,0.0588,0.0294,0.0588],[0.0294,0.0588,0,0,0.0588,0.0882,0.0294,0.5588,0.0294,0.0882,0.0294,0.0294],[0.0588,0.1176,0,0,0,0.0294,0,0.1176,0.2353,0.2353,0.0294,0.1765],[0.0294,0.0294,0.0294,0.1176,0.0588,0,0.0294,0,0.1765,0.4412,0.0588,0.0294],[0,0.0588,0.0294,0.0294,0.0588,0.0294,0,0.0588,0.0882,0,0.6176,0.0294],[0,0.0588,0,0,0,0.0588,0.0294,0.0294,0.0294,0,0.0882,0.7059],[0.1029,0.0956,0.1544,0.1324,0.0662,0.0809,0.0294,0.0294,0.0956,0.0735,0.0809,0.0588],[0.0074,0.0588,0.0221,0.0515,0.1618,0.1471,0.0441,0.1397,0.125,0.0956,0.1176,0.0294],[0.0147,0.0588,0.0294,0.0294,0.1029,0.0368,0.0074,0.1324,0.2132,0.1691,0.125,0.0809]]},"block 2 (natural)":{"train":{"words":[[1,4,13],[10,11,15],[3,2,13],[6,5,14],[9,12,15],[2,1,13],[8,5,14],[12,10,15],[1,3,13],[5,6,14],[11,9,15],[5,7,14],[4,2,13],[11,12,15],[7,8,14],[9,10,15],[6,7,14],[4,3,13],[6,8,14],[11,12,15],[2,3,13],[7,6,14],[3,1,13],[5,8,14],[10,9,15],[4,1,13],[9,11,15],[5,7,14],[10,11,15],[3,4,13],[8,7,14],[2,3,13],[12,10,15],[1,2,13],[8,6,14],[12,9,15]],"objects":[[1,4],[10,11],[3,2],[6,5],[9,12],[2,1],[8,5],[12,10],[1,3],[5,6],[11,9],[5,7],[4,2],[11,12],[7,8],[9,10],[6,7],[4,3],[6,8],[11,12],[2,3],[7,6],[3,1],[5,8],[10,9],[4,1],[9,11],[5,7],[10,11],[3,4],[8,7],[2,3],[12,10],[1,2],[8,6],[12,9]]},"test":{"words":[[1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11],[12]],"objects":[[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12]]},"accuracy":[0.2647,0.3235,0.2353,0.1765,0.3235,0.3235,0.5294,0.3235,0.3235,0.2059,0.6765,0.5294,0.6176,0.5515,0.6176],"n_subj":[34],"label":["Gangwani2011-category-natural"],"condition":["block 2 (natural)"],"description":["Experiment 1 of Gangwani, Kachergis & Yu, 'Simultaneous Cross-situational Learning of Category and Object Names'. 3 words + 2 referents per training trial: the two visible objects' 1-to-1 names (words 1-12) plus one 1-to-many category label (words 13-15; label 13/14/15 -> objects 1-4/5-8/9-12). 36 training trials (shared schedule from category_xsl_order.txt; identical co-occurrence structure across all three blocks -- only the stimuli differ). `accuracy` is per-word human P(correct referent | word): positions 1-12 are 12-AFC name accuracy, positions 13-15 are 3-AFC category-membership accuracy (n = 34 canonical-order participants). Because the matrix is 15x12, get_perf()/mafc_test() only score words 1-12; score words 13-15 (and the ME-violation measure that is the paper's point) with score_gangwani2011_category() in tests/bakeoff_comparison/gangwani2011_category_fit.R. `test` holds the 12 name trials (12-AFC) only. `response_matrix` (15x12, row-normalised) is the full human choice distribution incl. the category labels. This condition: block 2 (natural) -- category members share a salient perceptual feature (hook/arrow/...); the feature-similarity and novel-object generalization effects are not represented here."],"response_matrix":[[0.2647,0.0882,0.0588,0.0882,0.1176,0,0.0294,0.1176,0.0588,0.0882,0.0294,0.0588],[0.0294,0.3235,0,0.0588,0.0588,0,0.0882,0.1176,0.1176,0.0294,0,0.1765],[0.1176,0.0882,0.2353,0.0882,0.0588,0.0294,0,0.1765,0.0588,0.0588,0.0588,0.0294],[0.1176,0.0588,0.1176,0.1765,0.0294,0.0588,0.0882,0.0588,0.0294,0.0588,0.1765,0.0294],[0.0588,0.1471,0.0882,0.0588,0.3235,0.0294,0.0294,0.1176,0.0294,0,0.0294,0.0882],[0.0882,0.0588,0.0588,0.0294,0.0588,0.3235,0.0882,0.1765,0.0294,0.0588,0,0.0294],[0.0294,0.0588,0,0,0.0882,0.0588,0.5294,0.0588,0.1176,0,0.0294,0.0294],[0.0294,0.1471,0.0588,0.0588,0.0588,0,0.0588,0.3235,0.0882,0.0294,0.0882,0.0588],[0.0588,0.1176,0.0588,0.0588,0,0.0882,0.0294,0.0882,0.3235,0.0588,0.0294,0.0882],[0.0588,0.0588,0.0294,0.0588,0.1176,0.0882,0.0294,0,0.2059,0.2059,0.0882,0.0588],[0,0.0294,0,0.0588,0.0294,0.0294,0.0294,0.0588,0,0.0294,0.6765,0.0588],[0.0294,0.1176,0.0294,0,0,0,0.0294,0.0294,0.0588,0.0588,0.1176,0.5294],[0.1691,0.1029,0.1765,0.1691,0.0294,0.0956,0.0294,0.0588,0.0515,0.0515,0.0441,0.0221],[0.0441,0.0735,0.0809,0.0956,0.1176,0.1324,0.1324,0.1691,0.0368,0.0368,0.0441,0.0368],[0.0662,0.0588,0.0515,0.0662,0.0515,0.0662,0.0074,0.0147,0.1691,0.1912,0.125,0.1324]]},"block 3 (ad hoc)":{"train":{"words":[[1,4,13],[10,11,15],[3,2,13],[6,5,14],[9,12,15],[2,1,13],[8,5,14],[12,10,15],[1,3,13],[5,6,14],[11,9,15],[5,7,14],[4,2,13],[11,12,15],[7,8,14],[9,10,15],[6,7,14],[4,3,13],[6,8,14],[11,12,15],[2,3,13],[7,6,14],[3,1,13],[5,8,14],[10,9,15],[4,1,13],[9,11,15],[5,7,14],[10,11,15],[3,4,13],[8,7,14],[2,3,13],[12,10,15],[1,2,13],[8,6,14],[12,9,15]],"objects":[[1,4],[10,11],[3,2],[6,5],[9,12],[2,1],[8,5],[12,10],[1,3],[5,6],[11,9],[5,7],[4,2],[11,12],[7,8],[9,10],[6,7],[4,3],[6,8],[11,12],[2,3],[7,6],[3,1],[5,8],[10,9],[4,1],[9,11],[5,7],[10,11],[3,4],[8,7],[2,3],[12,10],[1,2],[8,6],[12,9]]},"test":{"words":[[1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11],[12]],"objects":[[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12],[1,2,3,4,5,6,7,8,9,10,11,12]]},"accuracy":[0.4412,0.5294,0.5294,0.3235,0.2941,0.2059,0.5,0.6765,0.2353,0.2941,0.3529,0.3235,0.5662,0.5662,0.5956],"n_subj":[34],"label":["Gangwani2011-category-adhoc2"],"condition":["block 3 (ad hoc)"],"description":["Experiment 1 of Gangwani, Kachergis & Yu, 'Simultaneous Cross-situational Learning of Category and Object Names'. 3 words + 2 referents per training trial: the two visible objects' 1-to-1 names (words 1-12) plus one 1-to-many category label (words 13-15; label 13/14/15 -> objects 1-4/5-8/9-12). 36 training trials (shared schedule from category_xsl_order.txt; identical co-occurrence structure across all three blocks -- only the stimuli differ). `accuracy` is per-word human P(correct referent | word): positions 1-12 are 12-AFC name accuracy, positions 13-15 are 3-AFC category-membership accuracy (n = 34 canonical-order participants). Because the matrix is 15x12, get_perf()/mafc_test() only score words 1-12; score words 13-15 (and the ME-violation measure that is the paper's point) with score_gangwani2011_category() in tests/bakeoff_comparison/gangwani2011_category_fit.R. `test` holds the 12 name trials (12-AFC) only. `response_matrix` (15x12, row-normalised) is the full human choice distribution incl. the category labels. This condition: block 3 (ad hoc) -- category members share no perceptual feature."],"response_matrix":[[0.4412,0,0.0294,0.0882,0.0294,0.0588,0.0294,0.1471,0.0588,0.0588,0.0294,0.0294],[0.0588,0.5294,0.0882,0.0294,0.0588,0.0588,0.0294,0.0294,0,0.0588,0,0.0588],[0,0.0588,0.5294,0.0294,0.0294,0.0588,0.0588,0.0294,0.1176,0,0.0294,0.0588],[0.2647,0.0294,0.0294,0.3235,0.0294,0,0.0588,0.0294,0.0588,0,0.0882,0.0882],[0,0.0294,0.1765,0,0.2941,0.1765,0.0294,0.0294,0.1471,0.0294,0.0294,0.0588],[0.0294,0.0588,0.0882,0.0882,0.1176,0.2059,0.1471,0.0294,0.1176,0.0294,0.0588,0.0294],[0.0294,0,0.0882,0,0.0882,0.0882,0.5,0.0882,0.0294,0,0.0588,0.0294],[0.0294,0,0,0.0294,0.0588,0.0588,0.0294,0.6765,0.0882,0,0.0294,0],[0.0882,0.0588,0.0294,0.0294,0.0294,0,0.0882,0,0.2353,0.0882,0.0588,0.2941],[0.0588,0.0588,0.0294,0.0588,0.0294,0,0.1471,0,0.0882,0.2941,0.0882,0.1471],[0,0.0294,0.0294,0.0294,0,0.0882,0.0294,0.0882,0.0588,0.1176,0.3529,0.1765],[0.0294,0.0294,0.0588,0.0588,0.1471,0.1176,0.0588,0,0.0882,0,0.0882,0.3235],[0.125,0.1618,0.1397,0.1397,0.0588,0.0588,0.0588,0.0441,0.0662,0.0515,0.0515,0.0441],[0.0441,0.0441,0.0368,0.1103,0.1691,0.1397,0.125,0.1324,0.0515,0.0368,0.0735,0.0368],[0.0221,0.0588,0.0441,0.0588,0.0588,0.0588,0.0147,0.0221,0.2353,0.1912,0.1471,0.0882]]}}