“There’s a portal to a different galaxy in the Johnsons’ basement,” she emphatically explained to me the next morning. “They’ve been in contact with an advanced civilization for months. I have over a thousand photos documenting the evidence.”

Stephanie was 58, a senior vice president at a medium-sized company. She had no previous psychiatric hospitalizations and no major mental illness in her family. She’d been successfully managing ADHD with stimulants for thirty years. I wondered what had changed to bring on psychotic symptoms all of a sudden.

Her son filled in the details when I called him. “She’s been working insane hours since the new product launch six months ago. Said she needed to stay sharp, couldn’t afford to fall behind. I think she was taking way more Adderall than prescribed, but she insisted her doctor had increased it.”

The medical record told a different story. Her last psychiatry appointment was eight months ago. Her stimulant prescription hadn’t changed in two years.

Three weeks into her admission, completely off stimulants, Stephanie still believed in the portal. Importantly, we had done a thorough rule out of psychosis secondary to autoimmune and neurological entities. Nonetheless, Stephanie had developed an elaborate cosmological theory involving interdimensional communication protocols and galactic surveillance networks.

The Persistence Problem

But Stephanie wasn’t following the script. Her psychotic symptoms had crystallized into a stable, internally consistent belief system that persisted weeks after stimulant discontinuation. She wasn’t alone in this persistence.

Large epidemiological studies suggest that 10-25% of substance-induced psychotic episodes don’t resolve as expected. Some patients transition to diagnoses like schizophreniform disorder or brief psychotic disorder. Others remain in diagnostic limbo: no longer substance-induced, not quite meeting criteria for primary psychotic disorders.

The conventional explanation focuses on “unmasking” underlying vulnerability. The substance supposedly reveals a predisposition that was always there, waiting to emerge. But this explanation feels unsatisfying. Why do some people develop persistent psychosis after chronic stimulant use while others don’t? What’s actually changing in the brain during those months or years of escalating use?

Understanding Stephanie’s case required moving beyond simple neurochemical explanations toward a computational framework that could explain the persistence, internal coherence, and treatment-resistance of her symptoms. Her eventual treatment design incorporated an algorithmic circuit framework, and the response suggested that persistent substance-induced psychosis might represent biased constraint satisfaction algorithms rather than a persistent hyperdopaminergic state. But demonstrating this required understanding how chronic stimulants alter the confidence estimates that feed into updating our models of reality.

From a Simple Molecular Narrative to the Complexity of Learning in the Brain

The idea that excessive dopamine underlies psychotic symptoms is supported by two major pieces of evidence. Neuroimaging of dopamine in the ventral striatum early in psychotic illness reveals excessive release, and traditional antipsychotic efficacy is tied to how well they block dopaminergic signaling. In primary psychotic disorders, the idea is that a hyperdopaminergic state is triggered by an underlying genetic vulnerability, and that environmental stressors can lower the threshold for such vulnerability to be expressed. Stimulant use may be one such environmental stressor. With Stephanie, the age of onset was inconsistent with such an interpretation.

But the bigger issue at stake is this: what does dopamine have to do with thinking in the first place, and how does this molecule actually work in that context?

The prefrontal cortex (PFC) houses our most sophisticated cognitive machinery: neural populations that maintain working memory, simulate future scenarios, and hold the beliefs that guide our decisions. These mental maps form the neural substrate of planning and inference, allowing us to navigate complex environments and make sense of ambiguous information. The prefrontal cortex, like other cortical areas, is composed of 80-85% excitatory neurons that use glutamate to communicate with one another. The remaining 15-20% are inhibitory neurons that perform functions like gating, filtering and normalization of signals transmitted among the excitatory glutamatergic neurons.

The prefrontal cortex engages in metalearning (learning how to learn) by discovering which strategies and representations work across different contexts and tasks. Rather than just memorizing specific stimulus-response patterns, it extracts abstract rules and principles that can be applied to novel situations.

This metalearning happens through self-supervised learning processes, where the PFC uses the inherent structure of experience to generate its own training signals. For example, it might learn to predict future events based on current context, or identify which environmental cues reliably predict important outcomes. These prediction tasks don’t require external labels: the PFC generates supervisory signals from the temporal structure of experience itself.

What distinguishes the PFC from other cortical areas is its timescale for temporal integration. While sensory areas might integrate information over milliseconds to seconds, the PFC operates over seconds to minutes. This longer temporal window allows it to detect patterns and relationships that unfold across extended behavioral sequences, enabling the extraction of abstract rules that persist across changing contexts.

The PFC maintains extensive connections throughout the brain, but is particularly involved in loops with two subcortical regions: the thalamus and basal ganglia. Recent work, including Wang and colleagues’ influential DeepMind study, suggests that the PFC learns predictive models of the environment, but that changes in its connectivity patterns support metalearning rather than adapting to individual tasks. The way it adapts to individual tasks is by adjusting its activity patterns rapidly, not connectivity slowly. The thalamus appears to be the brain region that sends signals to rapidly adjust PFC activity patterns, helping it adapt its world models to the current environment. The thalamus receives inputs from regions like the cerebellum, critical for motor adaptation, and the hippocampus, which outputs compressed long-term memory episodes.

How does dopamine factor into this picture? Dopamine is expressed by neurons scattered in the midbrain and brainstem, with the midbrain populations most relevant to our discussion. The major success story in systems neuroscience is that dopamine neurons signal reward prediction errors in temporal difference learning algorithms: computing the difference between expected and actual outcomes to drive learning. In the classic formulation, the temporal difference error is δ = r + γV(s’) – V(s), where r is the immediate reward, γ is the discount factor, V(s’) is the predicted value of the next state, and V(s) is the current state’s value.

The largest concentration of dopamine receptors is in the striatum, the first station of the basal ganglia, where these prediction error signals are expected to adjust value representations encoded by striatal populations. However, two major developments have transformed our understanding of dopamine’s relationship to thinking and planning.

First, whatever happens in the striatum ultimately impacts the thalamus and then the PFC. Recent evidence suggests that dopamine-driven changes in striatal circuits influence how prefrontal world models get updated through cortico-basal ganglia-thalamic loops. This creates a pathway for reward prediction errors to systematically bias the metalearning processes that maintain our beliefs about reality.

Second, emerging research on distributional reinforcement learning reveals that dopamine neurons don’t just signal simple prediction errors (basic “better than expected” or “worse than expected” signals). Instead, they encode the full statistical distribution of possible prediction errors. Think of it like this: instead of just saying “that was surprising,” different dopamine neurons maintain different perspectives on what kinds of surprises to expect and how to weight them. Some neurons act as “optimistic” predictors that overweight positive prediction errors, while others act as “pessimistic” predictors that overweight negative prediction errors. Moreover, individual dopamine neurons encode different discount factors: some optimized for short-term rewards, others for long-term outcomes.

Stimulant use may change several critical aspects of this architecture. First, it could alter how reward prediction errors are computed: the pallidal signals feeding back to the ventral tegmental area (VTA, a midbrain dopamine-producing region) to compute prediction error differences would be acutely altered. This may impact different VTA neurons differently: those with optimistic versus pessimistic biases, and those with short versus long discount factors, could show differential vulnerability to stimulant-induced alterations.

Second, stimulant use may change how quickly striatal systems impact the updating of PFC world models through thalamic loops. If the normal temporal dynamics of this metalearning process are accelerated or biased, the PFC might begin incorporating unreliable distributional information into its predictive models of reality.

Here’s a potential mechanistic insight: distributional alterations may not create noisy signals so much as systematically distort the confidence estimates that guide belief updating. In healthy brains, the width of prediction error distributions appears to signal uncertainty. When you encounter something unexpected, wide distributions might tell the system “be cautious, gather more evidence.” Narrow distributions could signal high confidence: “update your beliefs strongly based on this information.”

Chronic stimulant use might bias this uncertainty signaling by creating artificially narrow distributions around extreme positive prediction errors. The system could receive signals that essentially convey “high confidence that this unexpected pattern is highly significant.” This could alter the normal process by which the brain decides how much to update its beliefs based on new evidence.

When patients notice unusual environmental patterns, their altered distributional system might generate high-confidence signals about the significance of these observations. Instead of the appropriate response (“this might be random, collect more evidence”), the metalearning algorithms could receive the message “this is definitely important, build strong beliefs around it.”

The thalamic gating system, which appears to filter which patterns get promoted to adjust beliefs and plans, likely relies on these confidence estimates to make gating decisions. Altered confidence estimates might cause it to gate spurious patterns as if they were reliable environmental regularities. Once gated into prefrontal circuits, these patterns could become the foundation for elaborate belief systems.

This sets up a crucial question: how does the brain actually construct unified belief systems from these distributed confidence signals? Understanding this process illuminates why Stephanie’s delusions were so resistant to contradictory evidence.

The Computational Architecture of Belief Coherence

Coherence maximization is formally defined as a constraint satisfaction problem, where mental representations either fit together (cohere) via positive constraints or resist fitting together (incohere) via negative constraints. The brain seeks configurations that maximize the satisfaction of these constraints: essentially finding the most internally consistent interpretation of available evidence.

The computational complexity of this process is significant: coherence problems are computationally intractable for large systems, requiring approximation algorithms similar to those used for traveling salesman problems or neural network optimization. This computational challenge explains why specialized neural circuits evolved to handle belief integration.

The brain implements coherence maximization through what computational neuroscientists call active inference: a framework where organisms maintain generative models of their environment and continuously update these models to minimize “free energy,” a measure of surprise or model-environment mismatch. Under this framework, beliefs are not passive representations but active hypotheses that guide both perception and action, with the system working to maintain internal consistency while accommodating new evidence.

This algorithmic framework appears in multiple computational domains. The ECHO (Explanatory Coherence by Harmonic Optimization) algorithm, developed for scientific reasoning, uses connectionist networks where propositions are represented as nodes and coherence relationships as weighted connections. The network settles into configurations that maximize overall constraint satisfaction. Modern machine learning systems face similar challenges: large language models must maintain consistency across vast knowledge bases, and this coherence-seeking behavior emerges naturally from their training objectives.

The evolutionary utility of coherence-seeking becomes clear from this computational perspective: organisms that can rapidly construct consistent internal models from fragmentary evidence gain survival advantages through improved prediction and decision-making. However, this same mechanism becomes problematic when the input signals (the confidence estimates about environmental patterns) become systematically biased.

When chronic stimulant use distorts the distributional properties of prediction errors, the coherence maximization system receives corrupted input: patterns that should be flagged as low-confidence noise instead arrive with high-confidence signals demanding explanatory integration. The system treats these spurious patterns as reliable environmental regularities requiring coherent explanation, leading to elaborate belief systems that represent optimal solutions to a fundamentally corrupted constraint satisfaction problem.

This computational framework explains why persistent substance-induced delusions resist simple contradictory evidence. The beliefs aren’t random false ideas; they’re optimal coherence solutions given systematically biased confidence inputs. Breaking these belief systems requires restoring the underlying computational processes that generate appropriate confidence estimates in the first place, not just presenting contradictory evidence.

Given this understanding, traditional psychiatric approaches that focus solely on blocking neurotransmitter receptors miss the deeper computational dysfunction.

Why Single-Receptor Models Miss the Circuit Story

The problem goes beyond “too much dopamine signaling.” It’s systematically altered distributional learning: corrupted confidence estimates that feed into coherence maximization algorithms, leading to elaborate but internally consistent delusional frameworks. These beliefs persist because they represent optimal solutions to a constraint satisfaction problem operating on fundamentally biased inputs.

A purely receptor-based approach treats the neurochemical symptoms rather than the computational dysfunction. Blocking dopamine receptors may reduce the intensity of aberrant signals, but it doesn’t restore the distributional properties that allow metalearning circuits to distinguish reliable environmental patterns from noise. The coherence maximization system continues operating on the same corrupted confidence estimates, just with dampened intensity.

Understanding how circuits implement distributional learning algorithms and how chronic stimulants systematically bias these implementations suggests more targeted interventions that address the computational roots of persistent delusions rather than just their neurochemical expression.

With this framework in mind, Stephanie’s treatment required a fundamentally different approach than standard antipsychotic protocols.

Treatment Through an Algorithmic Circuit Lens

Standard antipsychotics like haloperidol or risperidone work by dampening aberrant dopamine signals in striatal circuits. A 2019 systematic review of six randomized controlled trials found that various antipsychotics (aripiprazole, haloperidol, quetiapine, olanzapine, and risperidone) were all effective at reducing both positive and negative symptoms of amphetamine-induced psychosis. But this dampening approach addresses only part of the distributional learning problem.

The algorithmic framework suggests that successful treatment requires two complementary strategies: first, reduce the impact of biased prediction error signals on coherence maximization circuits; second, restore the capacity for healthy distributional learning by normalizing the statistical properties that generate appropriate confidence estimates.

For the first component, we chose aripiprazole over traditional D2 antagonists. Its partial agonism at dopamine receptors could theoretically provide more nuanced modulation: dampening excessive signals while preserving some baseline dopaminergic function needed for normal distributional learning. The systematic review noted that aripiprazole showed particular effectiveness for negative symptoms, which might reflect its ability to maintain residual dopaminergic signaling rather than completely blocking the system.

But medication alone wouldn’t restore healthy distributional learning. Stephanie’s constraint satisfaction algorithms needed to relearn how to process confidence estimates appropriately. This required tackling the underlying cause: her dependence on chronic stimulants that had systematically biased the distributional properties feeding into her coherence maximization system.

Restoring Distributional Diversity: Replacement and Modulation

First, we needed to address Stephanie’s underlying ADHD without continuing to bias her prediction error distributions. Wellbutrin (bupropion) offered a promising alternative. Unlike amphetamines, which create massive positive prediction errors through dopamine reuptake blockade, bupropion provides more modest, sustained increases in dopaminergic and noradrenergic signaling. Its mechanism might preserve more natural distributional properties while still providing therapeutic benefit for attention deficits.

The hypothesis here is that Wellbutrin could serve as replacement therapy: providing enough cognitive enhancement to manage her ADHD symptoms while allowing her biased distributional learning circuits to gradually renormalize. Instead of the extreme positive prediction errors from chronic stimulants, she would experience more naturalistic dopaminergic signaling patterns that could support healthy confidence estimation.

Second, we cross-titrated aripiprazole to KarXT, a combination of xanomeline and trospium that targets muscarinic receptors. The algorithmic framework suggests this might work by modulating the inputs to dopaminergic circuits rather than directly blocking dopamine receptors. My early clinical experience with this cholinergic modulation appears to support its efficacy in stimulant-induced psychosis.

Cholinergic signaling plays crucial roles in regulating the context-dependent release of dopamine. By modulating muscarinic receptors, KarXT could potentially help restore more natural patterns of dopaminergic signaling: not by suppressing all dopamine activity, but by helping circuits generate more appropriate distributional responses to environmental inputs.

This represents a fundamentally different therapeutic approach: instead of just dampening aberrant signals, we’re trying to restore the circuit mechanisms that generate healthy distributional learning in the first place.

But even optimal pharmacological intervention addresses only half the problem. The other half involves helping patients’ constraint satisfaction algorithms relearn how to process confidence information appropriately.

Circuit Rehabilitation: A Future Direction

Future therapeutic interventions might look quite different from standard CBT. Rather than challenging the content of delusional beliefs directly, sessions could focus on the process of evidence evaluation itself. Imagine sitting with a patient and working through their observations systematically. Not “you’re wrong about the portal” but “let’s think about all the possible explanations for what you noticed.” What other reasons might account for changes in your neighbor’s lighting patterns? How confident should we be in each explanation? What additional evidence would help us distinguish between them?

The goal wouldn’t be to convince patients they’re wrong. It would be helping their constraint satisfaction algorithms practice processing confidence information appropriately: distinguishing between high-confidence and low-confidence inferences, calibrating degrees of belief to strength of evidence, and maintaining uncertainty when evidence is ambiguous.

Such approaches might reveal that patients’ observations aren’t entirely false. They may have noticed real environmental patterns. But their biased learning algorithms assign extreme confidence to elaborate explanations when the evidence actually supports much simpler, more probable alternatives.

By systematically examining the distributional properties of evidence (the range of possible explanations and their relative probabilities), therapeutic interventions could potentially help these circuits begin distinguishing signal from noise again.

Beyond Chemical Imbalance

The algorithmic framework provides a more comprehensive explanation. Chronic stimulants didn’t create “too much dopamine.” They systematically biased the distributional properties that feed confidence estimates into constraint satisfaction algorithms. Her elaborate interdimensional theory wasn’t a symptom of broken brain chemistry but an optimal solution to a corrupted computational problem. Traditional antipsychotics dampened the signals but couldn’t restore the underlying distributional learning processes.

Understanding psychiatric symptoms as biased algorithms rather than chemical imbalances opens new therapeutic possibilities. Instead of treating medication and therapy as separate interventions targeting different domains, we can recognize them as complementary approaches working on the same computational substrate. Pharmacological interventions like cholinergic modulation help restore healthy distributional properties in the circuits that generate confidence estimates. Therapeutic interventions help retrain these same constraint satisfaction algorithms to process confidence information more appropriately. Both target the algorithmic dysfunction that generates pathological beliefs.

Stephanie’s recovery with cholinergic modulation and stimulant replacement suggests that restoring healthy algorithmic function may be more effective than suppressing aberrant chemistry. The brain implements sophisticated learning algorithms through specific circuit architectures. When we understand how these algorithms can be corrupted and restored, we move beyond the limitations of purely neurochemical approaches toward interventions that address the computational roots of psychiatric dysfunction.

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.

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.

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.

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.

The Neuroscience of Distributed Systems

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—reward-seeking and predictive—illustrate how these systems work together, even as their differing goals can create tension.

Reward-seeking systems 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 reinforcement learning 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.

Predictive systems, on the other hand, are optimized to build and maintain a stable model of the world. They use mechanisms like predictive coding 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.

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).

Executive Control: Harmonizing the Coalition

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.

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.

Clinical Implications: Treating the Coalition

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.

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.

Philosophical and Psychological Perspectives

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.

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.

Conclusion: Embracing the Complexity of the Mind

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.

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.

References

1. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
2. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127-138.
3. Maia, T. V., & Frank, M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14(2), 154-162.