{"id":3047,"date":"2025-11-16T18:30:53","date_gmt":"2025-11-16T18:30:53","guid":{"rendered":"https:\/\/beyondtheimpact.net\/?p=3047"},"modified":"2025-11-18T13:07:53","modified_gmt":"2025-11-18T13:07:53","slug":"the-bayesian-brain-beyond-time-why-functional-neurological-disorders-may-be-overfitting-disorders-of-temporal-inference","status":"publish","type":"post","link":"https:\/\/beyondtheimpact.net\/?p=3047","title":{"rendered":"The Bayesian Brain Beyond Time: Why Functional Neurological Disorders May Be Overfitting Disorders of Temporal Inference"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\" id=\"ember3123\">When Time Stops Flowing Forward<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3124\">Recent quantum experiments have revealed something astonishing: when one entangled particle is measured, its twin behaves as if it <em>already knew<\/em> the outcome. This phenomenon \u2014 <strong>quantum retrocausality<\/strong> \u2014 suggests that information may flow not just from past \u2192 future, but also from future \u2192 past.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3125\">If time is not strictly linear, but <strong>bidirectional<\/strong>, then the brain \u2014 our most advanced prediction system \u2014 may also operate across this two-way temporal structure.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3126\">This raises a radical but coherent possibility: <strong>the brain may integrate information not only from the past, but also from counterfactual futures \u2014 events that almost happened but never did.<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3127\">The Bayesian Brain: A Predictive Engine<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3128\">Modern neuroscience describes the brain as a Bayesian inference system:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3129\">P(Brain State | Sensory Input) = P(Brain State) \u00d7 P(Sensory Input | Brain State)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3130\">The brain:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>builds <strong>priors<\/strong> from past experiences<\/li>\n\n\n\n<li>compares them with sensory input<\/li>\n\n\n\n<li>updates its internal model to minimise prediction error<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3132\">Karl Friston\u2019s <strong>Free Energy Principle<\/strong> formalises this:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3133\">Brain\u2019s Goal \u2192 Minimise Free Energy \u2192 Minimise Surprise<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3134\">The brain constantly attempts to reduce this free energy, keeping internal expectations aligned with reality.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3135\">But what if the brain is using <em>more<\/em> than just the past?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3136\">What If the Brain Also Samples the Future?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3137\">If the universe is temporally symmetric:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>the brain may update its priors based not only on historical data<\/li>\n\n\n\n<li>but also on <strong>near-miss futures<\/strong> (events that would have occurred without small corrective actions)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3139\">This is not mysticism \u2014 it is a logical extension of temporally bidirectional physics and the brain\u2019s predictive nature.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3140\">Imagine someone nearly slips but catches themselves instantly. Consciously, they are unaware of the \u201calmost fall.\u201d But computationally, the brain may register:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3141\">\u201cHad&nbsp;I&nbsp;not&nbsp;corrected&nbsp;myself,&nbsp;I&nbsp;would&nbsp;have&nbsp;fallen.\u201d\\text{\u201cHad I not corrected myself, I would have fallen.\u201d}\u201cHad&nbsp;I&nbsp;not&nbsp;corrected&nbsp;myself,&nbsp;I&nbsp;would&nbsp;have&nbsp;fallen.\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3142\">The Bayesian system then updates its priors <em>as if the near-fall were real<\/em>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3143\">This is <strong>over-updating<\/strong>, not under-updating.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3144\">The Brain as an Overfitting Machine<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3145\">In machine learning, <strong>overfitting<\/strong> happens when a model:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>learns the noise in the data<\/li>\n\n\n\n<li>incorporates rare anomalies<\/li>\n\n\n\n<li>becomes <em>too precise<\/em><\/li>\n\n\n\n<li>loses generalisation<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3147\">Exactly the same can happen in the brain.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3148\">Instead of failing to update priors (the traditional FND model), the brain <strong>overcorrects<\/strong> them.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3149\">A Simple Mathematical Illustration<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3150\">Normal Bayesian updating:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3151\"><strong>New Prior = Old Prior + k \u00d7 Prediction Error<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3152\">(where <em>k<\/em> is a small learning rate)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3153\">In FND:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3154\"><strong>New Prior = Old Prior + K \u00d7 Prediction Error<\/strong>(where <strong>K \u226b k<\/strong>, meaning the update is far too strong)<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/media.licdn.com\/dms\/image\/v2\/D4E12AQFtQ4j6YDAPKw\/article-inline_image-shrink_1000_1488\/B4EZqN7CUGJgAQ-\/0\/1763317655541?e=1764806400&amp;v=beta&amp;t=fORNRzgA1Edp85D3q4ypgw0VqIiUi4z-JOLxIlBUYMs\" alt=\"Article content\"\/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3156\">The brain <strong>overweights<\/strong> the prediction error \u2014 especially if it arises from a counterfactual future event.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3157\">This produces:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>excessively sharp priors<\/li>\n\n\n\n<li>overly precise beliefs<\/li>\n\n\n\n<li>maladaptive predictions<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3159\"><\/h3>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3160\">Why Neural Plasticity Matters<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3161\">Normally, plasticity prevents overfitting:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>weak evidence is discarded<\/li>\n\n\n\n<li>rare events have low weight<\/li>\n\n\n\n<li>anomalous futures are ignored<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3163\">But in FND:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>plasticity is noisy, impaired, or unstable<\/li>\n\n\n\n<li>the brain cannot relax its overfitted priors<\/li>\n\n\n\n<li>the model becomes trapped in a rigid attractor state<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3165\">This produces persistent symptoms such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>functional weakness<\/li>\n\n\n\n<li>abnormal movement patterns<\/li>\n\n\n\n<li>gait disorders<\/li>\n\n\n\n<li>non-epileptic attacks<\/li>\n\n\n\n<li>sensory loss<\/li>\n\n\n\n<li>dissociation<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3167\">Even though the hardware \u2014 the nervous system \u2014 is intact.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3168\">FND as a Disorder of Temporal Overfitting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3169\"><strong>FND is not a failure of updating priors. It is an overcorrection of priors.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"ember3170\">Specifically:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The brain incorporates counterfactual futures as real evidence.<\/li>\n\n\n\n<li>Priors become excessively precise (overfitting).<\/li>\n\n\n\n<li>Impaired plasticity prevents recalibration.<\/li>\n\n\n\n<li>Symptoms emerge as the brain\u2019s best attempt to reconcile the overfitted model with sensory reality.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>When Time Stops Flowing Forward Recent quantum experiments have revealed something astonishing: when one entangled&hellip;<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[153],"tags":[151,90],"class_list":["post-3047","post","type-post","status-publish","format-standard","hentry","category-functional-neurological-disorders","tag-fnd","tag-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - 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