{"id":704,"date":"2026-02-26T18:53:07","date_gmt":"2026-02-26T18:53:07","guid":{"rendered":"https:\/\/globalsolidarity.live\/maitreyamusic\/?p=704"},"modified":"2026-02-26T18:53:11","modified_gmt":"2026-02-26T18:53:11","slug":"cognitive-performance-index-cpi","status":"publish","type":"post","link":"https:\/\/globalsolidarity.live\/maitreyamusic\/neuroyoga\/cognitive-performance-index-cpi\/","title":{"rendered":"Cognitive Performance Index (CPI)"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Internal Benchmarking Model for Human\u2013AI Hybrid Research Units (Maitreya \/ NeuroYoga 3.0)<\/h2>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">1) Prop\u00f3sito y alcance<\/h2>\n\n\n\n<p>El <strong>CPI<\/strong> mide la <strong>capacidad operativa cognitiva<\/strong> de un investigador o equipo <strong>en un contexto de investigaci\u00f3n<\/strong> (no \u201cIQ\u201d, no rasgos de personalidad). Eval\u00faa:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>claridad atencional<\/strong><\/li>\n\n\n\n<li><strong>estabilidad ejecutiva<\/strong><\/li>\n\n\n\n<li><strong>calidad inferencial<\/strong><\/li>\n\n\n\n<li><strong>control de sesgos<\/strong><\/li>\n\n\n\n<li><strong>eficiencia de ciclo cient\u00edfico<\/strong><\/li>\n\n\n\n<li><strong>integraci\u00f3n humano\u2013IA<\/strong><\/li>\n<\/ul>\n\n\n\n<p>Se usa para:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>comparar sesiones vs sesiones,<\/li>\n\n\n\n<li>detectar fatiga y degradaci\u00f3n,<\/li>\n\n\n\n<li>optimizar protocolos,<\/li>\n\n\n\n<li>mejorar productividad cient\u00edfica sin sacrificar rigor.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">2) Principios de dise\u00f1o (no negociables)<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Medible<\/strong> con instrumentos est\u00e1ndar (tests cognitivos + m\u00e9tricas de proceso + opcional biomarcadores).<\/li>\n\n\n\n<li><strong>Resistente a \u201cgaming\u201d<\/strong> (no se optimiza solo una m\u00e9trica).<\/li>\n\n\n\n<li><strong>Normalizable<\/strong> por individuo y por dominio (evita penalizar estilos cognitivos).<\/li>\n\n\n\n<li><strong>Auditable<\/strong> (logs + trazabilidad).<\/li>\n\n\n\n<li><strong>Seguro<\/strong> (incluye banderas de riesgo).<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">3) Arquitectura del CPI<\/h2>\n\n\n\n<p>El CPI total (0\u2013100) es una combinaci\u00f3n ponderada de 6 sub\u00edndices:<math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mi>C<\/mi><mi>P<\/mi><mi>I<\/mi><mo>=<\/mo><mn>0.20<\/mn><mi>A<\/mi><mo>+<\/mo><mn>0.15<\/mn><mi>E<\/mi><mo>+<\/mo><mn>0.20<\/mn><mi>I<\/mi><mo>+<\/mo><mn>0.15<\/mn><mi>B<\/mi><mo>+<\/mo><mn>0.15<\/mn><mi>S<\/mi><mo>+<\/mo><mn>0.15<\/mn><mi>H<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">CPI = 0.20A + 0.15E + 0.20I + 0.15B + 0.15S + 0.15H<\/annotation><\/semantics><\/math>CPI=0.20A+0.15E+0.20I+0.15B+0.15S+0.15H<\/p>\n\n\n\n<p>Donde:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>A<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>A = Attention &amp; Stability Index<\/li>\n\n\n\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">E<\/annotation><\/semantics><\/math>E = Executive Control Index<\/li>\n\n\n\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>I<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">I<\/annotation><\/semantics><\/math>I = Inference Quality Index<\/li>\n\n\n\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>B<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">B<\/annotation><\/semantics><\/math>B = Bias Control Index<\/li>\n\n\n\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>S<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">S<\/annotation><\/semantics><\/math>S = Scientific Cycle Efficiency Index<\/li>\n\n\n\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>H<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">H<\/annotation><\/semantics><\/math>H = Human\u2013AI Synergy Index<\/li>\n<\/ul>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Pesos ajustables por unidad (R&amp;D, cl\u00ednica, modelado), pero se recomienda mantenerlos estables 90 d\u00edas para comparabilidad.<\/p>\n<\/blockquote>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">4) Sub\u00edndices: definici\u00f3n, medici\u00f3n y c\u00e1lculo<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">4.1 <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>A<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>A Attention &amp; Stability (0\u2013100)<\/h3>\n\n\n\n<p>Mide foco sostenido, estabilidad y resistencia a distracci\u00f3n.<\/p>\n\n\n\n<p><strong>Inputs recomendados<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sustained Attention task (SART o CPT)<\/li>\n\n\n\n<li>Error rate + reaction time variability<\/li>\n\n\n\n<li>(Opcional) HRV durante sesi\u00f3n (fatiga\/ruido)<\/li>\n<\/ul>\n\n\n\n<p><strong>Score<\/strong><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mi>A<\/mi><mo>=<\/mo><mn>100<\/mn><mo>\u2212<\/mo><mo stretchy=\"false\">(<\/mo><msub><mi>w<\/mi><mn>1<\/mn><\/msub><mo>\u22c5<\/mo><mi>E<\/mi><mi>r<\/mi><mi>r<\/mi><mo>+<\/mo><msub><mi>w<\/mi><mn>2<\/mn><\/msub><mo>\u22c5<\/mo><mi>R<\/mi><mi>T<\/mi><mi>V<\/mi><mi>a<\/mi><mi>r<\/mi><mo>+<\/mo><msub><mi>w<\/mi><mn>3<\/mn><\/msub><mo>\u22c5<\/mo><mi>L<\/mi><mi>a<\/mi><mi>p<\/mi><mi>s<\/mi><mi>e<\/mi><mi>s<\/mi><mo stretchy=\"false\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">A = 100 &#8211; (w_1 \\cdot Err + w_2 \\cdot RTVar + w_3 \\cdot Lapses)<\/annotation><\/semantics><\/math>A=100\u2212(w1\u200b\u22c5Err+w2\u200b\u22c5RTVar+w3\u200b\u22c5Lapses)<\/p>\n\n\n\n<p>Normalizar cada variable por percentil interno o z-score individual.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">4.2 <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">E<\/annotation><\/semantics><\/math>E Executive Control (0\u2013100)<\/h3>\n\n\n\n<p>Mide inhibici\u00f3n, switching y control de impulsos inferenciales.<\/p>\n\n\n\n<p><strong>Inputs<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Stroop \/ Flanker<\/li>\n\n\n\n<li>Task-switching cost<\/li>\n\n\n\n<li>Working memory (n-back o digit span)<\/li>\n<\/ul>\n\n\n\n<p><strong>Score<\/strong><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mi>E<\/mi><mo>=<\/mo><mn>100<\/mn><mo>\u2212<\/mo><mo stretchy=\"false\">(<\/mo><msub><mi>w<\/mi><mn>1<\/mn><\/msub><mo>\u22c5<\/mo><mi>S<\/mi><mi>t<\/mi><mi>r<\/mi><mi>o<\/mi><mi>o<\/mi><mi>p<\/mi><mi>C<\/mi><mi>o<\/mi><mi>s<\/mi><mi>t<\/mi><mo>+<\/mo><msub><mi>w<\/mi><mn>2<\/mn><\/msub><mo>\u22c5<\/mo><mi>S<\/mi><mi>w<\/mi><mi>i<\/mi><mi>t<\/mi><mi>c<\/mi><mi>h<\/mi><mi>C<\/mi><mi>o<\/mi><mi>s<\/mi><mi>t<\/mi><mo stretchy=\"false\">)<\/mo><mo>+<\/mo><mo stretchy=\"false\">(<\/mo><msub><mi>w<\/mi><mn>3<\/mn><\/msub><mo>\u22c5<\/mo><mi>W<\/mi><mi>M<\/mi><mi>S<\/mi><mi>c<\/mi><mi>o<\/mi><mi>r<\/mi><mi>e<\/mi><mo stretchy=\"false\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">E = 100 &#8211; (w_1\\cdot StroopCost + w_2\\cdot SwitchCost) + (w_3\\cdot WMScore)<\/annotation><\/semantics><\/math>E=100\u2212(w1\u200b\u22c5StroopCost+w2\u200b\u22c5SwitchCost)+(w3\u200b\u22c5WMScore)<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">4.3 <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>I<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">I<\/annotation><\/semantics><\/math>I Inference Quality (0\u2013100) <em>(n\u00facleo del modelo)<\/em><\/h3>\n\n\n\n<p>Mide la calidad del razonamiento cient\u00edfico producido.<\/p>\n\n\n\n<p><strong>Inputs auditables<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Hypothesis discrimination score (interno): % de hip\u00f3tesis descartadas por inconsistencia real vs descartes arbitrarios<\/li>\n\n\n\n<li>Predictive accuracy: desempe\u00f1o del modelo en holdout \/ cross-validation<\/li>\n\n\n\n<li>Complexity penalty: AIC\/BIC\/MDL o regularizaci\u00f3n (evitar overfitting)<\/li>\n\n\n\n<li>Replication readiness: porcentaje de pasos reproducibles y documentados<\/li>\n<\/ul>\n\n\n\n<p><strong>Score (formal)<\/strong><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mi>I<\/mi><mo>=<\/mo><mn>100<\/mn><mo>\u22c5<\/mo><mi>\u03c3<\/mi><mo fence=\"false\" stretchy=\"true\" minsize=\"1.8em\" maxsize=\"1.8em\">(<\/mo><mi>\u03b1<\/mi><mo>\u22c5<\/mo><mi>A<\/mi><mi>c<\/mi><mi>c<\/mi><mo>\u2212<\/mo><mi>\u03b2<\/mi><mo>\u22c5<\/mo><mi>O<\/mi><mi>v<\/mi><mi>e<\/mi><mi>r<\/mi><mi>f<\/mi><mi>i<\/mi><mi>t<\/mi><mo>\u2212<\/mo><mi>\u03b3<\/mi><mo>\u22c5<\/mo><mi>U<\/mi><mi>n<\/mi><mi>d<\/mi><mi>e<\/mi><mi>r<\/mi><mi>d<\/mi><mi>o<\/mi><mi>c<\/mi><mo fence=\"false\" stretchy=\"true\" minsize=\"1.8em\" maxsize=\"1.8em\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">I = 100 \\cdot \\sigma\\Big(\\alpha \\cdot Acc &#8211; \\beta \\cdot Overfit &#8211; \\gamma \\cdot Underdoc\\Big)<\/annotation><\/semantics><\/math>I=100\u22c5\u03c3(\u03b1\u22c5Acc\u2212\u03b2\u22c5Overfit\u2212\u03b3\u22c5Underdoc)<\/p>\n\n\n\n<p>donde <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>\u03c3<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">\\sigma<\/annotation><\/semantics><\/math>\u03c3 es una sigmoide para mantener 0\u2013100 y evitar que una m\u00e9trica extrema domine.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">4.4 <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>B<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">B<\/annotation><\/semantics><\/math>B Bias Control (0\u2013100)<\/h3>\n\n\n\n<p>Mide reducci\u00f3n de sesgos cognitivos en el pipeline.<\/p>\n\n\n\n<p><strong>Inputs<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Pre-commitment compliance: % de hip\u00f3tesis preregistradas antes de ver resultados finales<\/li>\n\n\n\n<li>Post-hoc correction rate: n\u00famero de \u201creinterpretaciones\u201d luego del resultado<\/li>\n\n\n\n<li>Confirmation bias check: evaluaci\u00f3n por pares (quick rubric)<\/li>\n\n\n\n<li>Diversity of alternatives: n\u00famero de modelos alternativos evaluados<\/li>\n<\/ul>\n\n\n\n<p><strong>Score<\/strong><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mi>B<\/mi><mo>=<\/mo><mn>100<\/mn><mo>\u2212<\/mo><mo stretchy=\"false\">(<\/mo><msub><mi>w<\/mi><mn>1<\/mn><\/msub><mo>\u22c5<\/mo><mi>P<\/mi><mi>o<\/mi><mi>s<\/mi><mi>t<\/mi><mi>H<\/mi><mi>o<\/mi><mi>c<\/mi><mo>+<\/mo><msub><mi>w<\/mi><mn>2<\/mn><\/msub><mo>\u22c5<\/mo><mi>C<\/mi><mi>o<\/mi><mi>n<\/mi><mi>f<\/mi><mi>i<\/mi><mi>r<\/mi><mi>m<\/mi><mi>R<\/mi><mi>i<\/mi><mi>s<\/mi><mi>k<\/mi><mo stretchy=\"false\">)<\/mo><mo>+<\/mo><mo stretchy=\"false\">(<\/mo><msub><mi>w<\/mi><mn>3<\/mn><\/msub><mo>\u22c5<\/mo><mi>P<\/mi><mi>r<\/mi><mi>e<\/mi><mi>C<\/mi><mi>o<\/mi><mi>m<\/mi><mi>m<\/mi><mi>i<\/mi><mi>t<\/mi><mo>+<\/mo><msub><mi>w<\/mi><mn>4<\/mn><\/msub><mo>\u22c5<\/mo><mi>A<\/mi><mi>l<\/mi><mi>t<\/mi><mi>M<\/mi><mi>o<\/mi><mi>d<\/mi><mi>e<\/mi><mi>l<\/mi><mi>s<\/mi><mo stretchy=\"false\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">B = 100 &#8211; (w_1\\cdot PostHoc + w_2\\cdot ConfirmRisk) + (w_3\\cdot PreCommit + w_4\\cdot AltModels)<\/annotation><\/semantics><\/math>B=100\u2212(w1\u200b\u22c5PostHoc+w2\u200b\u22c5ConfirmRisk)+(w3\u200b\u22c5PreCommit+w4\u200b\u22c5AltModels)<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">4.5 <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>S<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">S<\/annotation><\/semantics><\/math>S Scientific Cycle Efficiency (0\u2013100)<\/h3>\n\n\n\n<p>Mide la eficiencia de investigaci\u00f3n sin sacrificar rigor.<\/p>\n\n\n\n<p><strong>Inputs<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Cycle time: tiempo desde pregunta \u2192 hip\u00f3tesis \u2192 modelo \u2192 test<\/li>\n\n\n\n<li>Compute utilization: % de runs \u00fatiles vs runs redundantes<\/li>\n\n\n\n<li>Rework ratio: % de trabajo rehecho por mala definici\u00f3n inicial<\/li>\n\n\n\n<li>Decision latency: tiempo para decidir \u201cseguir \/ abortar\u201d hip\u00f3tesis<\/li>\n<\/ul>\n\n\n\n<p><strong>Score<\/strong><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mi>S<\/mi><mo>=<\/mo><mn>100<\/mn><mo>\u2212<\/mo><mo stretchy=\"false\">(<\/mo><msub><mi>w<\/mi><mn>1<\/mn><\/msub><mo>\u22c5<\/mo><mi>C<\/mi><mi>y<\/mi><mi>c<\/mi><mi>l<\/mi><mi>e<\/mi><mi>T<\/mi><mi>i<\/mi><mi>m<\/mi><mi>e<\/mi><mo>+<\/mo><msub><mi>w<\/mi><mn>2<\/mn><\/msub><mo>\u22c5<\/mo><mi>R<\/mi><mi>e<\/mi><mi>w<\/mi><mi>o<\/mi><mi>r<\/mi><mi>k<\/mi><mo>+<\/mo><msub><mi>w<\/mi><mn>3<\/mn><\/msub><mo>\u22c5<\/mo><mi>R<\/mi><mi>e<\/mi><mi>d<\/mi><mi>u<\/mi><mi>n<\/mi><mi>d<\/mi><mi>a<\/mi><mi>n<\/mi><mi>c<\/mi><mi>y<\/mi><mo stretchy=\"false\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">S = 100 &#8211; (w_1\\cdot CycleTime + w_2\\cdot Rework + w_3\\cdot Redundancy)<\/annotation><\/semantics><\/math>S=100\u2212(w1\u200b\u22c5CycleTime+w2\u200b\u22c5Rework+w3\u200b\u22c5Redundancy)<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">4.6 <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>H<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">H<\/annotation><\/semantics><\/math>H Human\u2013AI Synergy (0\u2013100)<\/h3>\n\n\n\n<p>Mide si la IA realmente amplifica al humano sin dominarlo ni crear dependencia.<\/p>\n\n\n\n<p><strong>Inputs<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AI leverage ratio: output \u00fatil por unidad de tiempo vs baseline humano<\/li>\n\n\n\n<li>Human gatekeeping: % de decisiones cr\u00edticas tomadas por humano con justificaci\u00f3n<\/li>\n\n\n\n<li>Traceability: % de outputs con prompts\/logs guardados<\/li>\n\n\n\n<li>Error capture: tasa de detecci\u00f3n humana de alucinaciones\/errores de IA<\/li>\n<\/ul>\n\n\n\n<p><strong>Score<\/strong><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mi>H<\/mi><mo>=<\/mo><mn>100<\/mn><mo>\u22c5<\/mo><mi>\u03c3<\/mi><mo fence=\"false\" stretchy=\"true\" minsize=\"1.8em\" maxsize=\"1.8em\">(<\/mo><mi>\u03b1<\/mi><mo>\u22c5<\/mo><mi>L<\/mi><mi>e<\/mi><mi>v<\/mi><mi>e<\/mi><mi>r<\/mi><mi>a<\/mi><mi>g<\/mi><mi>e<\/mi><mo>+<\/mo><mi>\u03b2<\/mi><mo>\u22c5<\/mo><mi>G<\/mi><mi>a<\/mi><mi>t<\/mi><mi>e<\/mi><mi>k<\/mi><mi>e<\/mi><mi>e<\/mi><mi>p<\/mi><mi>i<\/mi><mi>n<\/mi><mi>g<\/mi><mo>+<\/mo><mi>\u03b3<\/mi><mo>\u22c5<\/mo><mi>T<\/mi><mi>r<\/mi><mi>a<\/mi><mi>c<\/mi><mi>e<\/mi><mo>\u2212<\/mo><mi>\u03b4<\/mi><mo>\u22c5<\/mo><mi>A<\/mi><mi>I<\/mi><mi>E<\/mi><mi>r<\/mi><mi>r<\/mi><mi>o<\/mi><mi>r<\/mi><mi>S<\/mi><mi>l<\/mi><mi>i<\/mi><mi>p<\/mi><mo fence=\"false\" stretchy=\"true\" minsize=\"1.8em\" maxsize=\"1.8em\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">H = 100 \\cdot \\sigma\\Big(\\alpha \\cdot Leverage + \\beta \\cdot Gatekeeping + \\gamma \\cdot Trace &#8211; \\delta \\cdot AIErrorSlip\\Big)<\/annotation><\/semantics><\/math>H=100\u22c5\u03c3(\u03b1\u22c5Leverage+\u03b2\u22c5Gatekeeping+\u03b3\u22c5Trace\u2212\u03b4\u22c5AIErrorSlip)<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">5) Normalizaci\u00f3n (para que sea justo)<\/h2>\n\n\n\n<p>Se recomienda doble normalizaci\u00f3n:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Within-person baseline<\/strong> (cada investigador contra su propia media m\u00f3vil 30 d\u00edas)<\/li>\n\n\n\n<li><strong>Within-role banding<\/strong> (comparaci\u00f3n entre roles similares: modeladores, cl\u00ednicos, etc.)<\/li>\n<\/ol>\n\n\n\n<p>Esto evita:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>penalizar perfiles distintos,<\/li>\n\n\n\n<li>confundir talento con fatiga moment\u00e1nea.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">6) CPI-Risk Flags (no es puntaje, es \u201csem\u00e1foro\u201d)<\/h2>\n\n\n\n<p>Se agregan banderas para evitar que \u201cCPI alto\u201d oculte riesgo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>R1: Overcoherence risk<\/strong> (si EEG\/indicadores sugieren sincron\u00eda excesiva)<\/li>\n\n\n\n<li><strong>R2: Burnout risk<\/strong> (HRV baja persistente + lapses suben)<\/li>\n\n\n\n<li><strong>R3: Dissociation risk<\/strong> (si escalas cl\u00ednicas o reportes de desrealizaci\u00f3n aumentan)<\/li>\n\n\n\n<li><strong>R4: Overfitting risk<\/strong> (accuracy sube pero complejidad\/overfit explota)<\/li>\n<\/ul>\n\n\n\n<p>Regla de gobernanza:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Si hay bandera roja, el CPI no habilita escalamiento de carga ni decisiones cr\u00edticas.<\/p>\n<\/blockquote>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">7) Protocolo de benchmarking (operativo)<\/h2>\n\n\n\n<p><strong>Frecuencia<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Micro-CPI por sesi\u00f3n (10\u201315 min testing)<\/li>\n\n\n\n<li>CPI semanal (agregado)<\/li>\n\n\n\n<li>CPI mensual (tendencia + ajustes)<\/li>\n<\/ul>\n\n\n\n<p><strong>Estructura<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Pre-session: 3\u20135 min (A\/E r\u00e1pido)<\/li>\n\n\n\n<li>During: logging autom\u00e1tico (S\/H\/I)<\/li>\n\n\n\n<li>Post-session: 2\u20133 min (B + checklist)<\/li>\n\n\n\n<li>Weekly review: tendencias, fatiga, correcciones de protocolo<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">8) KPI dashboard interno (lo m\u00ednimo)<\/h2>\n\n\n\n<p>Mostrar solo 8 paneles:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>CPI total (trend)<\/li>\n\n\n\n<li>A y E (fatiga\/estabilidad)<\/li>\n\n\n\n<li>I (calidad inferencial)<\/li>\n\n\n\n<li>B (sesgos)<\/li>\n\n\n\n<li>S (eficiencia)<\/li>\n\n\n\n<li>H (sinergia IA)<\/li>\n\n\n\n<li>Red flags (R1\u2013R4)<\/li>\n\n\n\n<li>Intervenciones aplicadas vs resultado<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">9) Interpretaci\u00f3n pr\u00e1ctica del CPI<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>80\u2013100:<\/strong> rendimiento \u00f3ptimo estable (apto para decisiones cr\u00edticas)<\/li>\n\n\n\n<li><strong>60\u201379:<\/strong> rendimiento operativo normal (requiere higiene cognitiva)<\/li>\n\n\n\n<li><strong>40\u201359:<\/strong> degradaci\u00f3n moderada (reducir carga, reset, revisar sesgos)<\/li>\n\n\n\n<li><strong>&lt;40:<\/strong> riesgo de error alto (no tomar decisiones estrat\u00e9gicas)<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">10) Implementaci\u00f3n m\u00ednima (sin biomarcadores)<\/h2>\n\n\n\n<p>Si no se quiere EEG\/HRV al principio, igual sirve con:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A\/E: tareas cognitivas breves<\/li>\n\n\n\n<li>I\/B\/S\/H: logs del workflow + r\u00fabricas por pares<\/li>\n<\/ul>\n\n\n\n<p>Esto ya permite benchmarking serio.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Internal Benchmarking Model for Human\u2013AI Hybrid Research Units (Maitreya \/ NeuroYoga 3.0) 1) Prop\u00f3sito y alcance El CPI<\/p>\n","protected":false},"author":1,"featured_media":440,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22,10],"tags":[],"class_list":["post-704","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-neuroscience","category-neuroyoga"],"jetpack_featured_media_url":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-content\/uploads\/2026\/02\/31-1.jpg","_links":{"self":[{"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/posts\/704","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/comments?post=704"}],"version-history":[{"count":1,"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/posts\/704\/revisions"}],"predecessor-version":[{"id":705,"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/posts\/704\/revisions\/705"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/media\/440"}],"wp:attachment":[{"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/media?parent=704"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/categories?post=704"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/globalsolidarity.live\/maitreyamusic\/wp-json\/wp\/v2\/tags?post=704"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}