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	<title>Executive Control | Michael Halassa | Psychiatry</title>
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	<title>Executive Control | Michael Halassa | Psychiatry</title>
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		<title>The Long Game of Stimulants and Psychosis</title>
		<link>https://michaelhalassa.com/stimulants-and-psychosis/</link>
		
		<dc:creator><![CDATA[michaelhalassa]]></dc:creator>
		<pubDate>Mon, 16 Mar 2026 23:09:03 +0000</pubDate>
				<category><![CDATA[ADHD medication and psychosis]]></category>
		<category><![CDATA[Algorithmic psychiatry]]></category>
		<category><![CDATA[Chronic stimulant use]]></category>
		<category><![CDATA[Cobenfy]]></category>
		<category><![CDATA[Computational psychiatry]]></category>
		<category><![CDATA[Distributed neural systems]]></category>
		<category><![CDATA[Dopamine and psychosis]]></category>
		<category><![CDATA[Executive Control]]></category>
		<category><![CDATA[Reward-seeking systems]]></category>
		<category><![CDATA[Schizophrenia]]></category>
		<category><![CDATA[Stimulant side effects]]></category>
		<category><![CDATA[Stimulant-induced psychosis]]></category>
		<category><![CDATA[Algorithmic Psychiatry]]></category>
		<category><![CDATA[Biomarkers]]></category>
		<category><![CDATA[Cognitive flexibility]]></category>
		<category><![CDATA[Halassa Lab]]></category>
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		<category><![CDATA[Psychiatry]]></category>
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					<description><![CDATA[When I wrote about Stephanie earlier this summer, the 58-year-old executive who kept photographing &#8220;dimensional breach points&#8221; in her neighbors&#8217; basements, I discussed the potential relationship to her long-term use of prescription stimulant medication. Thirty years of stimulants had reshaped how her brain used evidence to build a model of the world. Even weeks after stopping, her [&#8230;]]]></description>
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<p>When I wrote about <a href="https://michaelhalassa.substack.com/p/substance-induced-psychosis-when" rel="noopener" target="_blank">Stephanie earlier this summer,</a> the 58-year-old executive who kept photographing &#8220;dimensional breach points&#8221; in her neighbors&#8217; basements, I discussed the potential relationship to her long-term use of prescription stimulant medication. Thirty years of stimulants had reshaped how her brain used evidence to build a model of the world. Even weeks after stopping, her psychotic symptoms persisted, challenging the traditional notion of drug-induced psychosis.</p>
<p>That story is no longer just anecdotal. A new <a href="https://doi.org/10.1001/jamapsychiatry.2025.2311" rel="noopener" target="_blank">JAMA Psychiatry meta-analysis</a> quantifies what we&#8217;ve been seeing clinically.</p>
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<h3 class="header-anchor-post">Key Findings from the Meta-Analysis</h3>
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<p>The study represents the largest systematic review to date on this question. Researchers from King&#8217;s College London analyzed 16 studies encompassing 391,043 individuals with ADHD exposed to stimulants, spanning observational cohorts, registry studies, and clinical trials from multiple countries.</p>
<p>The numbers demand attention: 2.8% developed psychotic symptoms (hallucinations, delusions), 2.3% developed a psychotic disorder meeting formal diagnostic criteria, and 3.7% developed bipolar disorder. While these percentages might seem low, with millions on long-term stimulants globally, we&#8217;re talking about tens of thousands developing psychosis or mania.</p>
<p>Interestingly, drug type mattered: risk of psychotic symptoms was 57% higher with amphetamines than with methylphenidate (OR 1.57, 95% CI 1.15-2.16). This differential risk appeared consistent across three large studies that directly compared the medications, including an analysis of over 230,000 individuals. The finding is particularly relevant given that amphetamines (Adderall, Vyvanse) are often prescribed as first-line treatment.</p>
<p>But, to me, the duration effect was the most striking: in studies lasting more than 5 years, 7.2% developed psychotic symptoms, versus just 0.2% in studies under 1 year. This thirty-fold increase may change how we should think about risk, suggesting that there is a cumulative hazard rate we should be considering.</p>
<p>The meta-regression analyses show additional patterns. Higher risk was linked to female sex (surprising, given that psychosis generally affects males more), higher stimulant doses, and North American studies. The heterogeneity was extremely high (I² &gt;95%), telling us that individual vulnerability varies dramatically. Some studies found near-zero risk while others found rates approaching 10%.</p>
<h3 class="header-anchor-post">When &#8220;Rare&#8221; Isn&#8217;t Rare Enough</h3>
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<p>The traditional framing is that stimulant-induced psychosis is a rare side effect. With millions on long-term stimulants and a 7.2% risk after five years, we&#8217;re no longer talking about rare outcomes. Even using the conservative overall rate of 2.8%, applied to the estimated 16 million Americans taking ADHD medications, suggests over 400,000 people at risk.</p>
<p>Of particular significance is the study challenging assumptions about reversibility. Traditional teaching holds that stimulant-induced psychosis resolves after discontinuation. But the meta-analysis reveals that 10-25% of psychosis cases persist, with some patients transitioning to schizophreniform disorder or remaining in diagnostic limbo.</p>
<p>What&#8217;s important to keep in mind is that these cases cluster in older adults who&#8217;ve been on stimulants since the 1990s or early 2000s. They&#8217;re the first generation to take these medications for decades, the unintentional subjects of a natural experiment revealing risks that three-month trials could never have detected.</p>
<h3 class="header-anchor-post">The Methamphetamine Parallel</h3>
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<p>The methamphetamine literature provides important guidance. Chronic recreational users show psychosis rates from 10% to 60%. The variability itself is instructive: it&#8217;s not that meth causes psychosis at some fixed rate, but that it reveals vulnerability in susceptible individuals over time.</p>
<p>The risk factors tell a story about different types of vulnerability. For transient psychosis, it&#8217;s earlier onset of use and male sex. For persistent psychosis that doesn&#8217;t resolve with abstinence, it&#8217;s family history of psychosis and comorbid major depression. Some brains can bounce back from stimulant-induced disruption while others undergo permanent change (at least with current interventional strategies).</p>
<p>Now consider prescription stimulants. Yes, the absolute risk is lower than methamphetamine, but the pattern is eerily similar. Short-term use rarely causes problems. Long-term exposure increases the odds, especially with amphetamines. The same vulnerability factors shape who transitions from transient to persistent symptoms.</p>
<p>The timeline is comparable, too. Methamphetamine users who develop persistent psychosis often do so within years. But therapeutic stimulants? We&#8217;re prescribing these for decades. Lower intensity, much longer duration. By year five, we&#8217;re seeing psychosis rates approaching the lower end of methamphetamine populations.</p>
<p>The field has been reluctant to make this comparison, perhaps worried about stigmatizing ADHD treatment. But ignoring the parallel means missing crucial insights. When 10-25% of therapeutic stimulant psychosis cases don&#8217;t resolve after discontinuation, we&#8217;re seeing the same phenomenon addiction psychiatrists have documented for years: some brains, once pushed into psychotic reorganization, don&#8217;t come back.</p>
<h3 class="header-anchor-post">Risk-Benefit Recalibration</h3>
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<p>For younger patients with severe ADHD, the benefits of stimulants may still outweigh the risks. Untreated ADHD carries its own catastrophic risks: car accidents, substance abuse, unemployment, relationship failure.</p>
<p>But for older adults starting stimulants or individuals with strong family histories of psychosis, the calculus shifts. Methylphenidate or non-stimulant alternatives (atomoxetine, guanfacine) may be safer defaults. Someone starting stimulants at 45 faces potentially thirty years of exposure. That 7.2% risk at five years becomes harder to justify.</p>
<p>For clinicians, this means treating psychosis risk like hypertension risk: low in any one patient, high in the population, and modifiable by careful choices.</p>
<h3 class="header-anchor-post">The Monitoring Gap</h3>
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<p>Current practice often involves annual checks for cardiovascular side effects but not systematic psychosis-risk monitoring. We check blood pressure but don&#8217;t screen for subtle perceptual changes or emerging unusual beliefs. By the time someone&#8217;s photographing dimensional portals, we&#8217;ve missed years of subclinical progression.</p>
<p>The study supports integrating structured screening into long-term ADHD care. Tools like the Prodromal Questionnaire (PQ-16) or adapted versions of the CAARMS could identify early perceptual abnormalities and unusual thought content. High-risk markers include family history of psychotic disorders, cannabis use, female sex, and prior manic episodes. For these individuals, considering mandatory methylphenidate trials before amphetamines and more frequent monitoring, may be prudent.</p>
<h3 class="header-anchor-post">Mechanistic Implications</h3>
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<p>The delayed risk profile challenges simple dopaminergic excess models. If psychosis were merely hyperdopaminergic states, we&#8217;d expect problems during dose titration, not after decades of stable dosing. Instead, the temporal pattern suggests progressive alterations in how neural circuits assign salience and construct beliefs.</p>
<p>Recent work on distributional reinforcement learning reveals that dopamine neurons encode the full statistical distribution of possible reward prediction errors, with different populations maintaining different perspectives on environmental uncertainty. Chronic stimulant exposure likely may distorts these distributional properties, perhaps creating artificially narrow confidence intervals around spurious patterns.</p>
<p>This connects to broader frameworks of predictive processing. The brain maintains generative models of its environment, continuously updating these models to minimize prediction error. Under normal conditions, the width of prediction error distributions signals uncertainty, gating how strongly new observations update existing beliefs. Chronic stimulants may alter these algorithmic properties, resulting in progressively learning wrong generative models of the world.</p>
<p>This framework explains both the slow emergence and incomplete resolution that the meta-analysis documents. It&#8217;s not that dopamine creates delusions directly, but that chronically biased learning algorithm gradually builds coherence maximizing world models that contain aberrant components.</p>
<h3 class="header-anchor-post">A Path Forward</h3>
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<p>The study quantifies what clinicians have observed anecdotally: stimulant-associated psychosis is not negligible, and risk rises with duration and amphetamine exposure. It underscores the need for shared decision-making, drug selection (methylphenidate over amphetamines), and long-term monitoring.</p>
<p>From a broader perspective, it situates stimulant-induced psychosis as part of a spectrum of computational vulnerabilities that accumulate over decades. We need registries tracking long-term outcomes, validated screening tools, and evidence-based protocols for when to switch or discontinue. More research is warranted into the types of antipsychotic medications (and therapies more generally) that would be helpful in these cases. I can share, anecdotally, that the M1/M4 agent xanomeline/trospium (KarXT, <a href="https://michaelhalassa.substack.com/p/the-cobenfy-advance-early-clinical" rel="noopener" target="_blank">Cobenfy</a>) may be particularly helpful in these cases.</p>
<p>The patients I&#8217;ve seen with late-onset stimulant psychosis share a common trajectory: decades of stable treatment, then emergence of fixed beliefs that feel more real than reality itself. Some recover fully. Others remain suspended between knowing their beliefs are false and experiencing them as true. That dual awareness captures what thirty years of algorithmic drift can do to a brain.</p>
<p>We owe it to the millions on long-term stimulants to identify who&#8217;s vulnerable before they reach that point. Because once someone arrives convinced they&#8217;ve discovered galactic conspiracies, it&#8217;s already too late to call it &#8220;just side effects.&#8221;</p>
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		<title>The Brain&#8217;s Confidence Problem: New Insights into Schizophrenia from an Unexpected Source</title>
		<link>https://michaelhalassa.com/the-brains-confidence-problem-new-insights-into-schizophrenia-from-an-unexpected-source/</link>
		
		<dc:creator><![CDATA[michaelhalassa]]></dc:creator>
		<pubDate>Thu, 10 Jul 2025 07:49:52 +0000</pubDate>
				<category><![CDATA[Computational psychiatry]]></category>
		<category><![CDATA[Executive Control]]></category>
		<category><![CDATA[Michael Halassa]]></category>
		<category><![CDATA[Reinforcement learning]]></category>
		<category><![CDATA[Schizophrenia]]></category>
		<category><![CDATA[Cognitive flexibility]]></category>
		<category><![CDATA[Halassa Lab]]></category>
		<category><![CDATA[MD thalamus]]></category>
		<category><![CDATA[Mediodorsal Thalamus]]></category>
		<category><![CDATA[Thalamocortical]]></category>
		<guid isPermaLink="false">https://michaelhalassa.com/?p=777</guid>

					<description><![CDATA[New research reveals how brain circuits control belief updating in schizophrenia. Dr. Michael Halassa explores breakthrough findings on delusional thinking, confidence calibration, and potential neuromodulation treatments for psychotic disorders.]]></description>
										<content:encoded><![CDATA[<p>As a psychiatrist who treats patients with schizophrenia, I&#8217;ve long been struck by a fundamental puzzle: why do individuals with psychosis hold onto beliefs with such unwavering certainty, even when presented with compelling contradictory evidence? The answer, it turns out, may lie in a marble-sized brain region most people have never heard of—and the revelation comes from an entirely unexpected source.</p>
<h2>When Tremor Treatment Accidentally Illuminates Psychosis</h2>
<p>A groundbreaking study by Mackenzie et al. (2025, bioRxiv) has provided some of the strongest evidence yet for a circuit-level understanding of belief formation and revision. The researchers weren&#8217;t studying schizophrenia at all—they were investigating patients receiving focused ultrasound treatment for essential tremor. But when post-surgical brain swelling accidentally affected the mediodorsal (MD) thalamus, something remarkable happened: patients developed a specific pattern of overconfident decision-making that mirrors core features of delusional thinking.</p>
<p>Using a sophisticated behavioral task that probes how people balance exploiting known information versus exploring new possibilities, the researchers found that MD disruption led to a precise computational deficit: <strong>patients lost their capacity for adaptive doubt</strong>. They became overly confident in their existing beliefs and stopped seeking information that might challenge those beliefs—the very cognitive pattern we see in psychotic disorders.</p>
<h2>The Neurobiology of Certainty Gone Wrong</h2>
<p>This finding connects directly to my clinical experience treating patients with schizophrenia. In my practice, I&#8217;ve observed that the challenge isn&#8217;t simply that patients hold false beliefs—it&#8217;s that they hold beliefs with pathological certainty. The traditional psychiatric focus on the content of delusions may be missing the more fundamental issue: <strong>a breakdown in confidence calibration</strong>.</p>
<p>The MD thalamus appears to act as a critical &#8220;confidence regulator&#8221; in the brain&#8217;s decision-making networks. When functioning normally, it helps determine how much we should trust our own predictions versus remaining open to new information. This circuit-level understanding aligns with emerging theoretical frameworks about how the brain coordinates distributed computations for flexible cognition (Scott et al., 2024).</p>
<p>Consider the implications: if the thalamus normally helps us maintain appropriate uncertainty about our beliefs, then thalamic dysfunction could explain why patients with schizophrenia often exhibit such rigid certainty in their delusional beliefs. They haven&#8217;t simply acquired false information—they&#8217;ve lost the neural capacity to doubt what they think they know.</p>
<h2>From Confidence to Delusions: A Circuit-Based Understanding</h2>
<p>The Mackenzie study reveals something crucial about the computational nature of belief updating. When MD-prefrontal circuits were disrupted, patients didn&#8217;t simply become perseverative or confused. Instead, they showed a specific pattern:</p>
<ul>
<li><strong>Increased reward sensitivity</strong>: Greater influence of learned values on choices</li>
<li><strong>Eliminated exploration bonus</strong>: Loss of information-seeking behavior</li>
<li><strong>Overexploitation</strong>: Excessive reliance on existing knowledge</li>
<li><strong>Reduced directed exploration</strong>: Failure to investigate uncertain but potentially informative options</li>
</ul>
<p>This behavioral signature maps remarkably well onto what we observe clinically in psychotic disorders. Patients with delusions often show:</p>
<ul>
<li><strong>Pathological certainty</strong> in false beliefs despite contradictory evidence</li>
<li><strong>Reduced information-seeking</strong> that might challenge their beliefs</li>
<li><strong>Overreliance on internal models</strong> rather than external feedback</li>
<li><strong>Failure to update beliefs</strong> when environmental contingencies change</li>
</ul>
<p>The convergence is striking and suggests we may be looking at the same underlying computational dysfunction from different angles—one measured in the laboratory, the other observed in the clinic.</p>
<h2>The Promise of Circuit-Based Psychiatry</h2>
<p>This research opens exciting possibilities for precision approaches to treating schizophrenia. Rather than the broad neurochemical interventions we currently rely on, we might be able to target specific computational dysfunctions in thalamocortical circuits.</p>
<p>The study&#8217;s anatomical precision is particularly encouraging. The behavioral effects correlated specifically with disruption of the <strong>lateral (parvocellular) MD</strong>, which connects primarily to dorsolateral prefrontal cortex and frontal pole—regions critical for cognitive flexibility and belief updating. This anatomical specificity suggests that focused neuromodulation approaches could potentially restore more adaptive confidence calibration without affecting other brain functions.</p>
<h3>Clinical Implications for Treatment</h3>
<p>In my practice, I&#8217;ve been developing approaches that integrate computational insights with traditional psychiatric care. The MD thalamus findings suggest several potential therapeutic directions:</p>
<ol>
<li><strong> Targeted Neuromodulation</strong>: Technologies like focused ultrasound or deep brain stimulation could potentially modulate MD activity to restore appropriate exploration-exploitation balance.</li>
<li><strong> Confidence Calibration Training</strong>: Cognitive interventions could be designed specifically to help patients develop more accurate metacognitive awareness of their own uncertainty.</li>
<li><strong> Precision Diagnostics</strong>: Computational tasks like the restless bandit could help identify specific cognitive profiles and guide personalized treatment approaches.</li>
<li><strong> Early Intervention</strong>: Understanding confidence miscalibration as a core deficit could lead to earlier detection and intervention before full psychotic episodes develop.</li>
</ol>
<h2>Beyond Schizophrenia: A New Framework for Mental Health</h2>
<p>The implications extend beyond schizophrenia to other conditions where belief updating goes awry:</p>
<ul>
<li><strong>Depression</strong>: May involve underconfidence leading to learned helplessness</li>
<li><strong>Anxiety disorders</strong>: Could reflect miscalibrated threat assessments</li>
<li><strong>Substance use disorders</strong>: Might involve overconfidence in drug-related beliefs</li>
<li><strong>Obsessive-compulsive disorder</strong>: May reflect inability to achieve confidence in safety</li>
</ul>
<p>This represents a fundamental shift from thinking about psychiatric symptoms as categorical disease states toward understanding them as specific computational dysfunctions in learning and decision-making algorithms.</p>
<h2>The Clinical Reality: From Lab to Bedside</h2>
<p>As someone who treats patients with schizophrenia daily, I&#8217;m acutely aware of the challenges in translating neuroscience findings into clinical practice. However, this study is particularly compelling because it provides <strong>causal evidence</strong> in humans—not just correlational findings from neuroimaging studies.</p>
<p>The patients in the Mackenzie study didn&#8217;t lose their ability to learn or make decisions entirely. They maintained overall task performance while showing specific deficits in belief updating and uncertainty management. This selectivity suggests that interventions targeting MD-prefrontal circuits might improve cognitive flexibility without causing global cognitive impairment.</p>
<h2>Looking Forward: A Personal Perspective</h2>
<p>For me, this research represents something I&#8217;ve been working toward throughout my career: a true bridge between basic neuroscience and clinical psychiatry. The fact that these insights emerged from a completely different clinical context—tremor treatment—underscores how interconnected our understanding of brain function really is.</p>
<p>In my clinical work, I&#8217;ve seen how traditional approaches to schizophrenia, while helpful, often fall short of fully restoring cognitive flexibility and adaptive functioning. Understanding the neural basis of confidence calibration offers hope for more targeted, effective interventions.</p>
<p>The convergence between this human lesion study and years of animal research on thalamic function (including work from our lab and others) gives me confidence that we&#8217;re identifying fundamental principles of brain organization rather than isolated curiosities. When different methodologies and species point toward the same underlying mechanisms, it usually means we&#8217;re onto something important.</p>
<h2>The Road Ahead</h2>
<p>Several critical questions remain:</p>
<ol>
<li><strong>Reversibility</strong>: Can confidence calibration deficits be restored through targeted interventions?</li>
<li><strong>Early detection</strong>: Could computational tasks identify at-risk individuals before psychotic episodes?</li>
<li><strong>Personalized medicine</strong>: How can we match specific circuit dysfunctions to optimal treatments?</li>
<li><strong>Combination approaches</strong>: How might neuromodulation combine with cognitive and pharmacological interventions?</li>
</ol>
<p>As we move forward, the goal isn&#8217;t to replace current treatments but to enhance them with circuit-based insights. The patients I treat deserve approaches grounded in rigorous understanding of how their brains actually work, not just symptomatic management.</p>
<h2>Conclusion: When Certainty Becomes the Enemy</h2>
<p>The patients in the Mackenzie study teach us something profound about the nature of adaptive cognition: the capacity to doubt ourselves, when doubt is warranted, may be one of our most important mental faculties. When that capacity is lost—whether through thalamic dysfunction, psychiatric illness, or other causes—we become trapped by our own certainty.</p>
<p>This has broader implications beyond psychiatry. In an era of polarization and &#8220;alternative facts,&#8221; understanding the neural basis of belief formation and revision is more important than ever. The same circuits that go awry in schizophrenia may also be involved in more everyday forms of rigid thinking and confirmation bias.</p>
<p>For my patients with schizophrenia, this research offers something precious: hope for treatments based on understanding rather than trial and error. Instead of simply suppressing symptoms with broad-acting medications, we may soon be able to restore the specific cognitive functions—like appropriate confidence calibration—that enable adaptive functioning in a complex, uncertain world.</p>
<p>The brain&#8217;s confidence problem is solvable. And that gives me confidence that better treatments are within reach.</p>
<p><em>This research builds on extensive work linking thalamic circuits to cognitive flexibility and psychiatric disorders, offering new insights into the computational basis of belief updating and its therapeutic implications.</em></p>
<p><strong>References:</strong></p>
<ul>
<li>Mackenzie, G., et al. (2025). Focused ultrasound neuromodulation of mediodorsal thalamus disrupts decision flexibility during reward learning. bioRxiv.</li>
<li>Scott, D.N., Mukherjee, A., Nassar, M.R., &amp; Halassa, M.M. (2024). Thalamocortical architectures for flexible cognition and efficient learning. Trends in Cognitive Sciences, 28(7), 639-652.</li>
</ul>
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		<title>The Self as a Coalition: How the Brain’s Distributed Systems Shape Mental Health</title>
		<link>https://michaelhalassa.com/brain-distributed-systems-mental-health/</link>
		
		<dc:creator><![CDATA[michaelhalassa]]></dc:creator>
		<pubDate>Tue, 01 Apr 2025 06:01:25 +0000</pubDate>
				<category><![CDATA[Distributed neural systems]]></category>
		<category><![CDATA[Executive Control]]></category>
		<category><![CDATA[Predictive coding]]></category>
		<category><![CDATA[Predictive systems]]></category>
		<category><![CDATA[Psychosis and mania]]></category>
		<category><![CDATA[Reinforcement learning]]></category>
		<category><![CDATA[Reward-seeking systems]]></category>
		<category><![CDATA[Uncategorized]]></category>
		<guid isPermaLink="false">https://michaelhalassa.com/?p=760</guid>

					<description><![CDATA[Explore how the brain’s reward-seeking and predictive systems interact, and how their tension underlies conditions like psychosis and mania. Learn how this framework can inform mental health treatment.]]></description>
										<content:encoded><![CDATA[<h4><strong>Introduction: The iPhone Analogy</strong></h4>
<p style="font-weight: 400;">Imagine your brain as an iPhone. In a healthy state, all your apps—email, maps, music, social media—run smoothly, even if they occasionally compete for resources. For example, your email app might want to check for new messages while your music app tries to play a song. The phone’s operating system ensures that these apps don’t interfere with each other, prioritizing one task at a time and maintaining overall functionality.</p>
<p style="font-weight: 400;">Now, imagine what happens when the operating system starts to fail. Apps crash, freeze, or behave unpredictably. They might run simultaneously, draining the battery and overloading the system, or they might shut down unexpectedly, leaving the phone unresponsive. The once-coordinated system becomes chaotic, and the phone becomes nearly unusable.</p>
<p style="font-weight: 400;">This analogy may capture something interesting about how the brain functions. Like the iPhone, the brain is not a singular entity but a coalition of distributed systems, each optimized for specific computational tasks. In health, these systems are harmonized by executive control mechanisms. But in conditions like mania or psychosis, this coordination can break down, revealing the tension between competing systems.</p>
<p style="font-weight: 400;">Understanding this framework has helped me make sense of patients and approach their care more effectively. It has also enhanced my ability to mentor other healthcare providers, offering them a new lens through which to view mental illness and treatment.</p>
<h4><strong>The Neuroscience of Distributed Systems</strong></h4>
<p style="font-weight: 400;">The brain is a coalition of distributed systems, each optimized for specific computational tasks. These systems operate in parallel, often with overlapping but distinct objectives, and their interactions give rise to coherent behavior and thought. Two key systems—<strong>reward-seeking</strong> and <strong>predictive</strong>—illustrate how these systems work together, even as their differing goals can create tension.</p>
<p style="font-weight: 400;"><strong>Reward-seeking systems</strong> are optimized to identify and pursue rewards, whether they are immediate (e.g., eating a delicious meal) or long-term (e.g., achieving a career goal). These systems rely on mechanisms like <strong>reinforcement learning</strong> to update strategies based on feedback. They drive goal-directed behavior, habit formation, and decision-making, but they can also prioritize short-term rewards over long-term stability, leading to conflicts with other systems.</p>
<p style="font-weight: 400;"><strong>Predictive systems</strong>, on the other hand, are optimized to build and maintain a stable model of the world. They use mechanisms like <strong>predictive coding</strong> to minimize uncertainty, allowing the brain to anticipate future events and adjust behavior accordingly. These systems underpin perception, attention, and belief formation, but they can also resist updating beliefs in light of new evidence, leading to rigidity or maladaptive behaviors.</p>
<p style="font-weight: 400;">These systems interact dynamically to produce behavior. For example, the value assigned to an action by reward-seeking systems can shape predictions about future outcomes, while predictions about the likelihood of rewards can influence which actions are pursued. However, their differing objectives can create tension. Reward-seeking systems may prioritize immediate gratification, while predictive systems emphasize long-term stability. Similarly, reward-seeking systems drive exploration (trying new strategies to maximize rewards), while predictive systems favor exploitation (relying on stable, predictable models).</p>
<h4><strong>Executive Control: Harmonizing the Coalition</strong></h4>
<p style="font-weight: 400;">Executive control mechanisms act as the brain’s “operating system,” integrating signals from reward-seeking and predictive systems and resolving conflicts. For example, executive control may suppress impulsive actions driven by reward-seeking systems in favor of actions that align with long-term goals. It may also update predictive models when new evidence contradicts prior beliefs, ensuring that behavior remains adaptive.</p>
<p style="font-weight: 400;">In healthy individuals, this coordination allows for flexible, goal-directed behavior. But in conditions like psychosis or mania, executive control is compromised, and the tension between systems becomes more apparent. For example, hyperactivity in reward-seeking systems may lead to impulsive behavior and excessive goal-directed activity, while predictive systems struggle to maintain stability. Aberrant predictive systems may result in hallucinations (overweighting prior beliefs) or delusions (failure to update beliefs in light of new evidence), while reward-seeking systems reinforce maladaptive behaviors.</p>
<h4><strong>Clinical Implications: Treating the Coalition</strong></h4>
<p style="font-weight: 400;">This framework has important implications for treatment. Rather than viewing the patient as a singular entity with a unified set of beliefs and behaviors, clinicians can recognize the multiplicity of systems at play. By identifying and targeting the system most responsive to treatment, they can adjust medications and therapeutic interventions more effectively.</p>
<p style="font-weight: 400;">For instance, a patient experiencing conflicting beliefs about their illness might benefit from interventions that strengthen executive control, such as cognitive-behavioral therapy (CBT) or mindfulness practices. Medications can be tailored to address the specific systems contributing to symptoms, whether they involve dopamine dysregulation, glutamate imbalances, or other mechanisms.</p>
<h4><strong>Philosophical and Psychological Perspectives</strong></h4>
<p style="font-weight: 400;">This idea aligns with both psychodynamic theory and modern neuroscience. Psychodynamic theorists have long emphasized the role of internal conflict in mental illness, often framing it as a struggle between conscious and subconscious forces. Neuroscience provides a complementary perspective, grounding these conflicts in the activity of distributed systems.</p>
<p style="font-weight: 400;">This framework also challenges traditional notions of the self. Rather than a singular, unified entity, the self emerges from the dynamic interplay of multiple systems, each with its own objectives and priorities. This perspective can reduce stigma by framing mental illness as a breakdown in coordination, rather than a fundamental flaw in the individual.</p>
<h4><strong>Conclusion: Embracing the Complexity of the Mind</strong></h4>
<p style="font-weight: 400;">The brain is not a monolithic entity but a coalition of distributed systems, each optimized for specific computational tasks. In health, these systems are harmonized by executive control. But in conditions like psychosis and mania, this coordination breaks down, revealing the tension between competing systems.</p>
<p style="font-weight: 400;">By embracing this framework, clinicians can develop more nuanced and effective treatments, tailored to the specific systems at play. Patients, too, can benefit from this perspective, which reframes mental illness as a disruption in coordination rather than a failure of the self. In doing so, we can move closer to a future where mental health is understood not as the absence of conflict, but as the ability to harmonize the brain’s many voices.</p>
<p>&nbsp;</p>
<h4><strong>References</strong></h4>
<ol>
<li>Sutton, R. S., &amp; Barto, A. G. (2018). <em>Reinforcement Learning: An Introduction</em>. MIT Press.</li>
<li>Friston, K. (2010). <em>The free-energy principle: A unified brain theory?</em> Nature Reviews Neuroscience, 11(2), 127-138.</li>
<li>Maia, T. V., &amp; Frank, M. J. (2011). <em>From reinforcement learning models to psychiatric and neurological disorders</em>. Nature Neuroscience, 14(2), 154-162.</li>
</ol>
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