{"id":"W2107704373","doi":"10.1162/0899766041336468","title":"Modeling Mental Navigation in Scenes with Multiple Objects","year":2004,"lang":"en","type":"article","venue":"Neural Computation","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mental rotation; Computer science; Artificial intelligence; Spatial memory; Computer vision; Working memory; Recall; Object (grammar); Perspective (graphical); Mental representation; Transformation (genetics); Posterior parietal cortex; Psychology; Cognitive psychology; Cognition; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003208625,0.0004476323,0.0005890111,0.0004531948,0.0005925529,0.001069505,0.001194003,0.001481966,0.003417831],"category_scores_gemma":[0.001379114,0.0005026133,0.001145765,0.0004305757,0.0009923691,0.001595573,0.00101837,0.0008141261,0.0002250341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175852,"about_ca_system_score_gemma":0.000817436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02643276,"about_ca_topic_score_gemma":0.02724062,"domain_scores_codex":[0.9998724,0.00004649253,0.000004820919,0.0000301352,0.00002162128,0.00002457453],"domain_scores_gemma":[0.9996731,0.0001845031,0.00003698307,0.00003269467,0.00003175856,0.00004103499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004562526,0.00002436717,0.0008226403,0.00002394183,0.00002363894,0.0001092464,0.0001141386,0.9440956,0.0006344846,0.05141473,0.0003015532,0.002390131],"study_design_scores_gemma":[0.00001044821,0.000007709312,0.0001297433,0.000002292597,0.000004766126,0.00001316403,0.00001578617,0.9880154,0.00006895688,0.01139008,0.0003381612,0.000003638275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4265187,0.0004480375,0.5482429,0.001456715,0.0001060258,0.00006352297,0.0005528376,0.0005115839,0.02209963],"genre_scores_gemma":[0.9108531,0.0002392641,0.08327034,0.00007345725,0.00004290493,0.0001325981,0.0002191206,0.00006357347,0.005105671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02643276,"threshold_uncertainty_score":0.05255777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452890480114988,"score_gpt":0.2325115660792328,"score_spread":0.2179826612780829,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}