{"id":"W2559850250","doi":"10.1186/s41235-016-0032-5","title":"Design of embodied interfaces for engaging spatial cognition","year":2016,"lang":"en","type":"article","venue":"Cognitive Research Principles and Implications","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada; Canada Foundation for Innovation; National Science Foundation","keywords":"Embodied cognition; Leverage (statistics); Cognition; Spatial cognition; Perspective (graphical); Human–computer interaction; Computer science; Cognitive science; Space (punctuation); Spatial design; Psychology; Cognitive psychology; Artificial intelligence; 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":[0.001517614,0.001014603,0.0004071785,0.0005950236,0.0003930953,0.00208618,0.001388673,0.00138846,0.004498286],"category_scores_gemma":[0.005518062,0.0006301572,0.0006934316,0.0002661968,0.001084035,0.001981188,0.002491183,0.0008192253,0.000611704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003959087,"about_ca_system_score_gemma":0.0005485354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002713173,"about_ca_topic_score_gemma":0.0005468274,"domain_scores_codex":[0.9988729,0.0005599295,0.0001198547,0.000116496,0.0002535915,0.00007713694],"domain_scores_gemma":[0.9986798,0.0008110459,0.0001022764,0.0001367154,0.0002015381,0.00006864106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007440263,0.0005691043,0.00289561,0.005029928,0.0003641639,0.001567996,0.008838817,0.1513092,0.1837896,0.2180154,0.005062073,0.4218142],"study_design_scores_gemma":[0.0008371893,0.002982012,0.004318407,0.001580315,0.0006392738,0.002539546,0.004283926,0.5410229,0.08839687,0.1658276,0.1872729,0.0002989765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02005381,0.0004702661,0.9706269,0.000242682,0.00008682605,0.0004278407,0.00004819292,0.0004355281,0.007607972],"genre_scores_gemma":[0.2306835,0.0005541441,0.7630606,0.0001442284,0.00001880501,0.001228587,0.00007392056,0.0001078218,0.004128382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004498286,"threshold_uncertainty_score":0.01504821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2158687999714187,"score_gpt":0.3960203965923541,"score_spread":0.1801515966209354,"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."}}