The Featherstone Test: Personality Psychology in Shades of Rust

 

Ladies and gentlemen, distinguished colleagues, laureates, skeptics, and—perhaps most importantly—those still undecided about whether rust is, at heart, orange or merely brown with ambition—

Welcome to the 2026 Ackerman Awards in Applied Personality Psychology.

The Ackerman Award, as many of you know, was established in 1978 to honor contributions that bridge the gap between theory and practice—between what we say about the mind and what we are willing to do about it in the clinic, the courtroom, and, increasingly, the quarterly performance review. Past recipients have ranged from pioneers of cognitive-behavioral therapy, to architects of large-scale personality assessment instruments, to that memorable year in 1994 when the committee, in a moment of either brilliance or fatigue, awarded the prize to a team studying “the psychological implications of waiting room magazines.”

It is in this proud and occasionally perplexing tradition that we gather tonight.

Our honoree, Professor Chester Featherstone, occupies a singular place in this lineage. Trained initially in experimental psychology at University of Chicago—where, it is said, he once spent an entire winter cataloging shades of corrosion on abandoned railings along the Midway—Professor Featherstone later migrated into clinical work, bringing with him an unusual conviction: that the most revealing aspects of personality are not found in grand narratives or elaborate projective fantasies, but in small, stubborn acts of classification.

It is from this conviction that the now-famous Featherstone Test emerged.

At first glance, the test appears disarmingly simple. Participants are presented with a series of color judgments, the most famous of which—now nearly canonical—is the prompt: “Is rust a subtype of orange, or of brown?” From this modest beginning unfolds an intricate interpretive framework. The respondent’s choice is not merely recorded, but situated within a matrix of inferred traits: tolerance for ambiguity, latent aggression, aesthetic conservatism, adaptive flexibility, and—depending on the edition of the manual—one’s likelihood of returning library books on time.

Over the past two decades, the Featherstone Test has found applications in an astonishing array of domains. In severe mental illness, clinicians have reported that patients who classify rust as orange demonstrate “forward-leaning perceptual optimism,” while those who select brown exhibit “grounded chromatic realism,” though the clinical implications of either remain, as ever, under active discussion. In the diagnosis of mild and functional disorders, the test has been praised for its efficiency—requiring no more than a laminated card and, ideally, good lighting.

In forensic psychiatry, its use has been more… adventurous. Expert witnesses have, on occasion, introduced Featherstone profiles to illuminate questions of intent, impulse control, and, in one widely discussed case, whether a defendant’s categorization of rust suggested “a propensity toward interpretive overreach.” The court, we are told, admitted the testimony with interest.

And then there is the workplace. Here, the test has perhaps achieved its widest diffusion. Human resources departments, eager for tools that promise both insight and brevity, have adopted Featherstone-derived modules to assess team compatibility, leadership potential, and—most ambitiously—“color-based decision alignment.” One consulting firm reported that departments composed primarily of “orange-rust classifiers” showed increased ideation, while “brown-rust teams” demonstrated superior follow-through. Whether these findings reflect causation, correlation, or the strategic placement of more agreeable chairs remains, at present, an open question.

Through it all, Professor Featherstone has maintained a steady, almost monastic focus on refinement. Successive editions of the test have introduced controlled variations—oxidation gradients, contextual lighting shifts, even the controversial “weathered steel vignette”—each designed to isolate the elusive boundary between perception and projection.

And yet, as we celebrate this body of work, one cannot help but admire not only its reach, but its… resilience. For in an era of neuroimaging, genomic correlations, and ever more elaborate computational models, Professor Featherstone has reminded us—quietly, persistently—that entire theoretical edifices can still be constructed on the question of whether something is, in essence, orange.

Or brown.

Or, perhaps, something else entirely.

It is, I think, a testament to the spirit of the Ackerman Award that we honor contributions which compel us to look again—at our assumptions, our methods, and occasionally at the objects themselves. For if two equally trained observers cannot agree on rust, what hope have we of agreement on motivation, intention, or personality?

And if, on the other hand, we can agree—if we can standardize, norm, and score such judgments with sufficient confidence—then perhaps the implications are as vast as they are… intriguingly convenient.

In any case, tonight we recognize a scholar who has asked a deceptively simple question, and in doing so, has generated an entire field of answers—answers that have proven useful, influential, and, at times, remarkably adaptable to the needs of those who require them.

Please join me in welcoming the recipient of the 2026 Ackerman Award in Applied Personality Psychology—

Professor Chester Featherstone.

And if, during the reception, you find yourselves near the bar, looking into the amber of a well-poured drink, wondering—quietly, privately—what exactly it is that you are seeing… well.

You would not be the first.

Chat GPT Sees "Ins and Outs" of The Bonfire of the Vanities

 

click to enlarge

The Bonfire of the Vanities: Law, Status, and Self-Destruction Across Book and Film

The Bonfire of the Vanities and its film adaptation The Bonfire of the Vanities tell essentially the same story—but with crucial differences that reshape the legal stakes, the psychology of the protagonist, and the moral tone of the ending. What begins as a panicked decision after a late-night wrong turn in the Bronx becomes, in both versions, a study in how status anxiety, media dynamics, and legal systems interact—though the novel and film resolve that study very differently.

---

What actually happens in the Bronx?

At the center is Sherman McCoy (played in the film by Tom Hanks), a wealthy bond trader who, while driving with his mistress Maria, gets lost in the Bronx. In a moment of fear and confusion, the car strikes a young Black man, Henry Lamb, and they flee.

A critical fact—hidden at first—is that Maria is the one driving.

From this moment forward, Sherman makes the defining decision:
he will conceal the affair and the true driver, even if that means implicitly accepting blame.

---

What is Sherman’s legal strategy?

Early phase: “Maybe this disappears”

Initially, Sherman is not formulating a courtroom defense at all. His goal is simpler and more primitive:

• avoid exposure of the affair

• avoid connection to the Bronx

• hope the incident is never traced back to him

This is not strategy—it is panic containment.

Middle phase: “It was an accident”

Once the car is traced and he becomes a suspect, a denial defense (“I wasn’t there”) becomes untenable. At that point, the plausible line shifts toward:

• accidental injury under duress

• fear of assault by the youths

• loss of control while trying to escape

In other words: not a crime of intent, but a chaotic accident.

Final phase (film only): “I wasn’t the driver”

In the film, Sherman’s ultimate salvation comes not from arguing accident, but from proving:

• Maria was driving, and

• she lied about it

This comes via evidence uncovered by the journalist Peter Fallow (Bruce Willis).

---

How does the journalist get involved?

Peter Fallow begins as a parasitic observer, turning the case into a sensational media narrative. The Bronx DA uses the case for political visibility; activists use it symbolically; the press amplifies everything.

But Fallow eventually stumbles onto—or engineers access to—recorded statements by Maria suggesting she was the driver. In a late turn, he passes this to the defense, becoming an unlikely conduit for truth.

His arc mirrors Sherman’s in a distorted way:
self-interest first, accidental integrity later.

---

What is the nature of the trial?

Film: Criminal prosecution with a Hollywood exit

The trial is criminal, essentially:

• The State of New York vs. Sherman McCoy

The injured Henry Lamb is not a plaintiff; he is the victim in a prosecution shaped by race, class, and media pressure.

Judge Leonard White (Morgan Freeman) presides. In the climactic scene:

• the tape revealing Maria’s role is introduced

• her credibility collapses

• Sherman is effectively cleared / released

The judge then delivers a moral speech criticizing the opportunism of everyone involved—press, politicians, activists, and lawyers.

Book: No such clean resolution

The novel offers no comparable courtroom catharsis. Instead, it presents a system that is messier, more cynical, and less redeemable.

---

What happens to Henry Lamb?

Film:

• Henry is in a coma for much of the story

• later, he regains consciousness and survives

• after that, his personal story largely fades from view

The narrative focus shifts away from him to the spectacle surrounding the case.

Book:

• Henry Lamb dies

This single change radically alters the stakes.

---

How do the legal stakes differ?

This is perhaps the most important divergence.

Film:

• injury, not death

• charges revolve around assault, hit-and-run, perjury, and misrepresentation

• Sherman can plausibly escape with exoneration

Book:

• fatality

• exposure to manslaughter or criminally negligent homicide

• no easy exculpatory mechanism

In the novel, Sherman’s initial lie doesn’t just risk embarrassment—it places him in the path of a prosecution for a death.

---

Why is Sherman’s decision so extreme in the book?

Your insight is exactly right:
Sherman’s choice in the novel is almost absurdly self-destructive.

He is, in effect, choosing:

• concealment of an affair and preservation of status
  over

• avoiding implication in a fatal crime

Why would someone do that?

Wolfe’s answer is satirical but pointed:

• Sherman is conditioned to treat social exposure as catastrophic

• his identity is built on status, reputation, and elite belonging

• in the moment, social death feels worse than legal risk

This is not rational cost-benefit analysis. It is status reflex under pressure.

---

Why does the film soften this?

The film lowers the stakes (injury instead of death) because:

• a mainstream audience is less willing to follow a protagonist making such an extreme irrational choice

• it allows for a clear exoneration arc

• it shifts the tone from tragic satire to moral melodrama with resolution

In short:

• Book: bleak, unresolved, system-wide critique

• Film: structured, redeemable, narratively satisfying

---

Is divorce really that bad?

From a purely financial standpoint, as you noted:

• losing half of $100M is not existential

But for Sherman:

• the divorce exposes the affair

• the affair triggers social exclusion

• reputation collapse threatens career

So the perceived loss is not money—it is identity within an elite hierarchy.

That is why his early choices look irrational:
he is protecting something intangible that he experiences as more fragile than wealth.

---

Conclusion: Truth under pressure

Across both versions, a pattern emerges:

• The initial event is ambiguous and chaotic
• The response is dishonest but recognizably human
• The system amplifies the situation into spectacle
• Truth emerges not from virtue, but from pressure and competing interests

The key difference is tone:

• The film allows truth to restore order
• The book suggests that truth arrives too late, and the system has already done its damage

In both, however, the central irony remains:
a small lie, told to preserve status, expands into a crisis that threatens everything—including, in the novel, a man’s freedom in the face of a death.

What ultimately lingers is not just the satire, but the inversion at its core:
the original choice is made to avoid a limited, containable loss—exposure, embarrassment, a damaged marriage—yet that very choice manufactures a far larger, uncontrollable one.

In that sense, the story is less about guilt or innocence than about escalation.
A moment of panic becomes a strategy; the strategy becomes a narrative; the narrative becomes a prosecution. And by the time the truth surfaces, it no longer has the power to restore all that has been  lost—only to clarify how it was lost.

ChatGPT Reviews "Coupe de Chance" (Film, 2024)

I thought the French woody Allen movie Coup de Chance was quite good. His way of telling the story in cinema, in editing, in “showing not telling”, was delightful. [Streams on Amazon]

##

You’re not wrong about the style you’re reacting to—Coup de Grâce. It’s Allen’s recent French-language film set in Paris, and what you’re describing—editing-driven storytelling and “showing rather than telling”—is exactly where it shines.

The Look and Feel of Coupe de Chance

Chen Couture Bringing Together Pathology and Radiology via AI

 https://openreview.net/forum?id=oxgcPoDkNv   

https://www.linkedin.com/posts/hdcouture_in-clinical-practice-a-complete-patient-share-7452672569268326400-PgPd/

Top points

1. The paper addresses a real problem, not a toy one. In actual oncology, diagnosis and prognosis often depend on both radiology and pathology. Radiology gives the macro view of the lesion in the body; pathology gives the micro view of cells and tissue. Most AI systems still handle these as partly separate worlds and then combine them late in the game.

2. The authors argue that today’s common multimodal method is crude. The usual approach is: take a radiology model, take a pathology model, extract numerical feature vectors from each, concatenate them, and run a classifier. That can work, but it is basically a black-box latent fusion strategy. It may improve accuracy, but it does not tell you much about why, and it may miss richer cross-talk between the domains.

3. Their central idea is to fuse by “concepts,” not by uninterpretable vectors. Instead of combining arbitrary hidden features, they create a bridge built from medically recognizable concepts like tumor necrosis, cellular atypia, invasion, mitotic activity, irregular margins and similar findings. This is meant to provide an interpretable semantic layer between the two expert models.

4. The clever twist is that these concepts are not fixed. The paper’s real novelty is not merely “use concepts.” It is: let the evidence from radiology alter how the pathology concepts are weighted, and let pathology alter how radiology concepts are weighted. They call this cross-domain co-adaptation. So a finding that might be only mildly concerning in one modality can become more significant when the other modality also shows aggressive disease.

5. They do this efficiently, not by retraining giant models from scratch. The radiology and pathology foundation models are kept largely frozen. The authors add a lightweight prompt-based mechanism, called Global-Context-Shared Prompt (GCSP) tuning, that changes how concepts are interpreted for a given case. Total extra trainable parameters are only about 0.15% of the combined model size. That is attractive because it suggests a practical way to exploit large pretrained models without the burden of full fine-tuning.

6. The results are good, though not magical. On their reported datasets, the method beats a variety of unimodal and multimodal baselines for survival prediction and cancer grading. The headline result is better performance than standard latent fusion methods and some adaptive baselines. For example, they report AUC 0.903 on one tumor grading task and better C-indexes on several survival tasks.

7. The interpretability claim is probably the main selling point. Because the final prediction runs through scored concepts, the system can point to the radiology and pathology concepts that drove a high-risk or low-risk call. In other words, it offers at least a candidate rationale rather than only a mystery score. That is exactly the kind of thing people hope for in clinical AI, though of course “more interpretable than a black box” is not the same as fully validated clinical reasoning.

8. The limitations matter and are worth noticing. The concept list is still predefined, not infinitely open-ended. The method also depends on paired radiology-pathology data, which is not trivial to assemble well. And on at least one difficult 5-way gastric cancer grading task, the authors themselves say performance is still not good enough for clinical deployment. So this is best read as an important research architecture paper, not as evidence that radiology-pathology multimodal AI is suddenly ready for prime time.

Bottom line:
This paper says: don’t let radiology AI and pathology AI merely dump numbers into the same bucket. Make them communicate through medically meaningful concepts first, then fuse. That is why the article sounds so abstract; underneath the jargon, the idea is actually pretty simple and fairly elegant.

A further  gloss would be: this is less about “AI sees more pixels” and more about “AI gets a better committee meeting between two expert witnesses.”

#####

AND

In plain English, the paper is saying:

We already have strong AI models for radiology and pathology, but they do not really “talk” to each other well. Radiology sees the big picture of a tumor on CT/MRI. Pathology sees the microscopic details on a tissue slide. In real medicine, doctors use both. But most AI systems just turn each one into a pile of numbers and glue the piles together at the end. The authors say that is a black box and misses the real relationship between the two kinds of evidence.

Their proposed fix is: instead of fusing raw math features, fuse medically meaningful concepts. So rather than combining abstract vectors, the model works through concepts like tumor necrosis, cellular atypia, invasion, mitotic activity, irregular margins and so on. Those concepts act as a shared language or “bridge” between radiology and pathology.

The trickiest part is their main idea: the meaning of a concept in one modality can be adjusted by what is seen in the other modality. For example, a radiology finding might make a pathology feature more ominous, or vice versa. They call this Concept Tuning and Fusing (CTF) and use something called Global-Context-Shared Prompting to do it. That means they do a small amount of tuning so each model becomes aware of the other model’s evidence before the final prediction is made.

So the paper’s real claim is not merely, “we made the AUC go up.” It is more like: we made multimodal AI more clinically sensible and more interpretable. The model can say, in effect, “this patient looks high-risk because the radiology suggests aggressive morphology and the pathology also shows aggressive cellular features,” instead of only producing an opaque risk number. The authors emphasize that this gives a more transparent rationale for predictions.

They then show that this approach performs better than several comparison methods on tasks like survival prediction and tumor grading. For example, on one 3-way grading task they report an AUC of 0.903, and they say the method beats unimodal models and several multimodal fusion baselines while only adding about 0.15% extra trainable parameters because the big foundation models stay frozen.

Heather Couture’s LinkedIn post is basically a clean executive summary of the same idea. She says current systems often use late fusion of static feature vectors, producing a black-box prediction, whereas this paper uses clinically grounded concepts as the shared interface. She highlights three takeaways: cross-domain co-adaptation, interpretable predictions, and extreme efficiency. Her bottom-line sentence is that true multimodal AI is about teaching different models to communicate through a shared dynamic clinical vocabulary.

Put even more bluntly:

Old way:
Radiology AI + pathology AI → giant number soup → answer.

This paper’s way:
Radiology AI + pathology AI → “let’s compare notes using doctor-like concepts” → answer with some rationale.

One important caveat: the authors themselves admit this is still a research method, not a ready-for-clinic product. It depends on a predefined pool of concepts and paired data, and they note that performance on a hard 5-way gastric cancer grading task is still modest enough that it is not ready for clinical deployment in that setting.

So the dense prose boils down to this:

They are trying to make radiology AI and pathology AI communicate through medically meaningful concepts rather than through inscrutable latent vectors.
That is the whole movie. 

Sleep and Parkinson's Disease: Webinar at Michael J Fox Foundation

Michael J Fox Foundation holds monthly webinars; here AI notes on a recent session on sleep disorders in PD.

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 https://www.michaeljfox.org/webinars

https://www.michaeljfox.org/webinar/sleep-and-parkinsons-whats-going-and-what-can-help

Soania Mathur MD / MJFF Patient Council

Wanda Kim Lilly / Patient / MJFF Patient Council

Emmanuel During MD / Icahn School of Medicine / Mt Sinai / NYC

Lkeanis Vaou MD, FAAN, Univ Texas San Antonio

###

This Michael J. Fox Foundation webinar on sleep in Parkinson’s disease brings together a moderator with Parkinson’s, a patient advocate, and two physician experts in movement disorders and sleep medicine to explain why sleep problems are so common in Parkinson’s, what kinds of problems occur, and what can actually help. The panel makes the point that sleep is not a side issue in Parkinson’s. It is a major part of daily functioning, quality of life, and even brain and body health. Good sleep supports brain “reset,” toxin clearance, immune function, physical recovery, metabolism, and emotional regulation, while both too little and too much sleep can be harmful. They describe normal sleep as a cycling process through deep sleep and REM sleep across the night, with deep sleep tending to dominate earlier and REM later.

A major theme of the webinar is that sleep problems in Parkinson’s are common, varied, and often under-discussed in clinic. The experts emphasize that many patients do not spontaneously report nighttime symptoms unless asked directly, and that clinicians may fail to ask. The most common complaint is sleep maintenance insomnia rather than difficulty falling asleep: people with Parkinson’s often wake repeatedly through the night, sometimes six to eight times, for different reasons. Those reasons may include pain, muscle cramps, difficulty turning over in bed, nighttime “wearing off” of dopamine medication, tremor, restless legs, frequent urination, anxiety, sleep apnea, or REM sleep behavior disorder. A particularly useful point is that the panel does not present “Parkinson’s insomnia” as one single thing. Instead, they urge patients and doctors to tease apart the causes, because treatment depends on identifying what is actually waking the person up.

The patient panelist gives a vivid real-world account of how this can feel. She says she used to be an excellent sleeper, but later developed painful muscle cramps and dystonia, including leg cramps at night. What woke her up was the pain, but what kept her awake was fear—the fear that a new symptom meant disease progression or loss of control. That distinction becomes one of the webinar’s most human and practical insights: in Parkinson’s, disrupted sleep can become a feedback loop of symptom, vigilance, and anxiety. The panel repeatedly returns to this idea, noting that once a person starts expecting poor sleep, that expectation itself can help perpetuate insomnia.

The webinar also explains why Parkinson’s affects sleep biologically, while cautioning that not every sleep complaint is directly caused by the disease itself. One expert says Parkinson’s and other neurologic disorders can disturb the brain systems that regulate sleep and wakefulness, but also stresses that many contributors are treatable coexisting sleep disorders or behavioral factors. This is especially important because patients may assume poor sleep is simply “part of Parkinson’s” and therefore unavoidable. The experts push back against that resignation. They say that even if sleep is not perfect, it can usually be improved, and that patients should not fixate on the idea that nothing can be done. They also note that aging itself fragments sleep, so not every nighttime awakening is uniquely Parkinsonian.

A centerpiece of the discussion is REM sleep behavior disorder, or RBD, which the presenters describe as one of the most distinctive sleep disorders associated with Parkinson’s. In normal REM sleep, the brain is active and dreaming, but the muscles are effectively paralyzed or “locked” so the person does not physically act out dreams. In RBD, that locking mechanism fails, allowing people to twitch, yell, punch, kick, or otherwise enact dreams. The expert explains that the relevant brainstem circuitry is often affected very early in Lewy body disease, sometimes years before classic Parkinsonian motor symptoms appear. About half of patients may develop these symptoms before tremor or movement problems, making RBD not just a symptom but also a potential early marker of elevated Parkinson’s risk. That is one reason it is such an important focus for research.

The panel repeatedly stresses the value of care partners or bed partners in recognizing sleep disorders. Patients may not know they snore, stop breathing, or act out dreams because they are asleep when it happens. Care partners can report snoring, gasping, witnessed apneas, screaming, punching, dream enactment, and other nighttime behaviors that are diagnostically crucial. This is particularly relevant for RBD and sleep apnea, both of which can go unrecognized unless someone else observes them. The message is that sleep history in Parkinson’s should be gathered from both the patient and, when possible, the person sharing the room or bed.

On the practical side, the webinar gives a strong endorsement of sleep hygiene and lifestyle measures before defaulting to sleeping pills. The experts recommend consistent sleep and wake times, avoiding long daytime naps, exercising regularly, limiting alcohol and heavy meals before bed, keeping the bedroom cool and dark, minimizing light exposure at night, and getting daylight exposure during the day. One sleep specialist gives a particularly concrete example of a patient with Parkinson’s whose sleep improved markedly just by following these recommendations: setting a regular schedule, reducing anxiety, exercising more, lowering the bedroom temperature, and changing beliefs about sleep. The panel frames this as encouraging but also realistic: these habits work, but they require discipline and can be hard to sustain. They compare adherence to sleep hygiene to starting a diet—simple in theory, difficult in practice, but often effective when maintained.

The patient advocate adds a nice layer of self-management detail. She keeps a journal tracking symptoms and possible triggers. From observation, she learned that sugar, alcohol, inadequate hydration, and being physically cold at bedtime seemed to provoke cramps. She also found that direct heat, especially an old-fashioned hot water bottle, helped reduce recurrent cramps and eased the fear that another cramp was about to start. Most importantly, she deliberately changed her mental framing: instead of treating poor sleep as something catastrophic or life-limiting, she chose to see it as something that could be improved. That blend of practical adjustment and cognitive reframing is presented as a meaningful part of coping.

The webinar gives a useful explanation of sleep studies. The experts distinguish between the traditional in-lab polysomnogram, which records brain waves, breathing, oxygen, heart activity, muscle activity, and video/audio overnight, and a home sleep test, which is simpler and more comfortable but usually answers only one question: whether the person has sleep apnea. The in-lab test is more comprehensive and can detect conditions like RBD in addition to apnea, but it is more cumbersome and expensive, and some people understandably sleep poorly in that setting. The home test is easier but more limited. This explanation helps demystify why one patient might be sent for one kind of study rather than the other.

On treatment, the physicians are cautious about reflexively prescribing generic sleep aids. They say that in Parkinson’s, the better approach is often to identify the specific source of the disruption and treat that. For example, cramps, painful rigidity, trouble turning in bed, and restless discomfort may reflect overnight wearing-off of dopaminergic therapy. In those cases, they often consider nighttime extended-release carbidopa/levodopa or dopamine agonists, because patients still need dopamine coverage while asleep. One expert also says that deep brain stimulation (DBS) often improves sleep indirectly by providing continuous overnight control of motor symptoms, even though the precise effects on sleep circuitry remain an area of research. Patients frequently report that after DBS they sleep better, even if sleep was not the original treatment target.

When medications specifically for sleep are discussed, the experts mention a few options but still present them as secondary to careful assessment. One speaker highlights orexin antagonists as a newer class that can improve sleep without being habit-forming, and also mentions trazodone as an older medication that can be quite helpful in some patients, especially by supporting deeper sleep early in the night. The tone is not anti-medication, but it is distinctly anti-quick-fix. Medication is treated as one tool among several, not the whole strategy.

The Q&A section covers several common questions. On melatonin, one doctor says it is often more useful for REM sleep behavior disorder than for ordinary insomnia. Patients may assume melatonin is being prescribed simply to help them sleep, but the expert says she more often uses it to reduce dream enactment behaviors. Another expert adds that with melatonin, more is not necessarily better, and that taking it at the same clock time each evening may be more important than taking high doses, because of its role in regulating circadian rhythm. On magnesium, the transcript references it mainly in the context of the patient’s cramps rather than as a broad sleep cure.

The webinar also addresses napping, which is handled with nuance rather than dogma. The recommendation is not “never nap,” but rather: if you truly need one, keep it short—about 30 minutes or less—because shorter naps are more refreshing and less likely to interfere with nighttime sleep. Longer naps may leave people groggy and reduce sleep drive for the next night. If a care partner notices someone has been napping too long, a gentle awakening, followed by activity or a walk outside, may help reestablish alertness.

Another practical symptom discussed is nighttime temperature dysregulation—waking up sweaty one moment and freezing the next. The expert links this to autonomic dysfunction in Parkinson’s, especially in the setting of medication wearing off. Suggestions include adjusting overnight Parkinson’s treatment and using simple supportive strategies such as warm socks or thermal clothing when cold extremities are part of the problem. Again, the webinar’s approach is to connect the symptom back to Parkinsonian physiology without assuming there is a single uniform fix.

The research portion is notably optimistic. One expert is especially interested in ways to enhance deep sleep and in the use of wearable devices and AI-based analysis to detect subtle movements during REM sleep that may identify RBD—and therefore elevated Parkinson’s risk—years before motor diagnosis. The idea is that consumer-style sleep devices might someday become scalable tools for earlier detection of neurodegenerative disease. The panel also promotes participation in PPMI, the Parkinson’s Progression Markers Initiative, as well as other research opportunities, arguing that progress depends on large-scale patient participation and long-term data collection.

Overall, the webinar’s message is both practical and hopeful. Sleep disturbance in Parkinson’s is common and complicated, but it is not something patients should silently endure or dismiss as untreatable. The panel urges people to tell their clinicians about sleep symptoms, involve care partners in reporting nighttime behaviors, improve sleep hygiene, look for specific treatable causes, and seek a sleep specialist when needed. The closing tone is realistic but upbeat: sleep may not become perfect, but in many cases it can become meaningfully better, and better sleep can make living with Parkinson’s better too.

##

If of interest, see also a 2020 paper by Pfeiffer on autonomic dysfunction in PD: PMID: 32789741

https://doi.org/10.1007/s13311-020-00897-4

Profiling Indiana Univ Prof. Jennifer Oliva: AI in Healthcare and More

 An April 2026 STAT PLUS article on United Healthcare and AI highlighted the expertise of Prof. Jennifer Oliva of Indiana University.   Article by Casey Ross.

Here, Chat GPT summarizes some of her work.  Find her bio here which includes comprehensive links to her publications.

https://law.indiana.edu/about/people/details?name=oliva-jennifer-d

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Professor Jennifer D. Oliva emerges from these materials as a health-law scholar working at the junction of AI, insurance coverage, public health, and administrative power. Her Indiana bio places her at Indiana University Maurer School of Law as Professor of Law and Val Nolan Faculty Fellow, with research interests spanning health law and policy, privacy, evidence, torts, and complex litigation.  Her bio also shows a notably interdisciplinary background—West Point, Oxford MBA, Georgetown JD—plus roles with Georgetown’s O’Neill Institute and the UCSF/UC Law Consortium.

What is striking in Oliva’s recent work is that she does not treat AI in healthcare as a generic “innovation” story. She treats it as a power-allocation problem: who gets to decide, at scale and often opaquely, whether patients receive care, whether agencies remain evidence-based, and whether the law can still reach the real actors making consequential decisions. That theme runs through both her insurance-algorithm articles and, in a different register, through “Government Goes Goop.”

In “Regulating Healthcare Coverage Algorithms” (Indiana Law Journal, 2025), Oliva’s basic move is to shift the center of attention from clinical AI to coverage AI. She notes that while FDA regulates many AI-enabled clinical tools, insurers also use algorithms to determine whether care is “medically necessary” and how much care will be covered, yet those coverage algorithms remain largely unregulated, proprietary, and shielded from external validation. She argues that this is not a merely administrative issue: coverage algorithms can be used to delay or deny medically necessary treatment, with direct consequences for patient health.

[The Biden administration proposed a rule in late 2024 that would have required Medicare Advantage plans to  have much higher clarity about AI and coverage criteria in general; this was nixed in the final rule under Trump II.]

That article presents Oliva as a scholar with a very clear instinct: follow the real decision-maker. If an algorithm is effectively deciding access to care, she argues, then the law should not be distracted by formal distinctions between a tool that guides treatment and a tool that governs payment for treatment. Her argument is that those distinctions may look neat on paper, but in the real world, where most patients cannot self-fund expensive care, a coverage algorithm can be just as consequential as a diagnostic or treatment algorithm. 

On that basis, she presses for robust oversight, ideally through FDA authority if available, and otherwise through legislative expansion and interim state action requiring pre-market assessment for validity, accuracy, and fairness.

The article’s tone is also important. It is not technophobic. Oliva acknowledges that such tools may promise efficiency and standardization. But she is skeptical of the way those promises interact with insurer incentives. Her recurring concern is that AI, when deployed inside utilization management, can become a mechanism for industrialized claims control rather than improved care. In her framing, the real policy problem is not only bias or opacity in the abstract, but the combination of automation plus profit motive plus weak oversight.

Her later article, “Regulating Healthcare Coverage Algorithms in the Shadow of ERISA” (Michigan Law Review, forthcoming 2027), sharpens and advances that project. Here, Oliva takes up the big doctrinal obstacle: even if states want to regulate these systems, won’t ERISA preemption block them? 

Her answer is the article’s central intellectual contribution. She argues that states should reconceive coverage algorithms not as inseparable parts of employee benefit plan administration, but as standardized commercial products designed, manufactured, and sold by third-party vendors. If that is right, then state rules requiring validation of these tools are better understood as product safety regulation, not forbidden interference with plan administration.

That is a clever and important pivot. Instead of attacking ERISA head-on, Oliva tries to route around it. She accepts that ERISA has long frustrated state regulation of employer-sponsored health plans, especially self-insured plans, but argues that states still retain room to regulate the software products themselves. The article therefore proposes a model state framework centered on pre-market validation, ongoing performance monitoring, and enforcement by specialized state Algorithm Policy Offices, with regulation directed at vendors rather than plans.

So the difference between the two algorithm papers is useful. The Indiana piece is the broad normative and regulatory claim: these coverage algorithms are high-stakes healthcare tools and should not enjoy a “free ride.” The Michigan piece is the doctrinal and institutional sequel: given the constraints of ERISA and federal inaction, here is a legally defensible path for state governance. Together, they show Oliva at work as both critic and constructor—first identifying the regulatory void, then designing a plausible architecture to fill it.

The ERISA article also shows another feature of her style: she writes against the grain of conventional assumptions. The abstract expressly says the article “challenges” the assumption that comprehensive state oversight is beyond reach because of ERISA. That tells you something about her scholarly temperament. She does not simply describe preemption as an immovable barrier; she treats it as a legal terrain that can be reinterpreted for the era of algorithmic decision-making.

Then there is “Government Goes Goop” (Emory Law Journal Online, 2026), which at first looks like a departure from AI-insurance scholarship but in fact fits the same larger pattern. Here Oliva argues that the rise of wellness and antivaccine figures into senior federal health roles represents the culmination of a long historical evolution in American health fraud—from medicine shows to institutional capture. She traces continuities in tactics: emotional manipulation, conspiratorial framing, anti-establishment posture, and exploitation of information asymmetries.

The paper is especially pointed in its treatment of Trump Administration 2. Its table of contents and introduction frame the administration’s health leadership as a “wellness cabinet,” with discussions of Kennedy at HHS, Bhattacharya at NIH, Oz at CMS, and Makary at FDA, plus subsequent vaccine-skeptic appointments. Oliva’s claim is not merely that these figures hold controversial views. It is that federal health agencies risk being transformed from evidence-based institutions into platforms for wellness ideology, with effects including staff departures, preventable outbreaks, and weakened scientific credibility.

What links “Government Goes Goop” to the insurance-algorithm work is her deeper preoccupation with epistemic governance. In one set of papers, the question is: who validates the algorithm that determines care? In the Goop paper, the question becomes: who validates the very institutions meant to protect scientific standards? In both settings, Oliva worries about systems that look official, scalable, and rationalized, yet may be driven by commercial incentives, ideology, or both, while ordinary patients bear the risk.

A fair overall profile, then, is that Oliva is developing into a scholar of health-law infrastructure. She studies not just rights or doctrines in isolation, but the machinery by which modern healthcare decisions are made: utilization-management software, regulatory jurisdiction, preemption doctrine, agency leadership, and the porous boundary between public health expertise and commercialized misinformation. Her work is distinctive because it combines practical policy urgency with doctrinal inventiveness. She is not content to say that AI and healthcare are complicated. She asks, very concretely, who is governing whom, by what tool, under what legal authority, and with what accountability.

 

World Parkinson Day at Cedars Sinai Los Angeles (April 11 2026)

 Revised Meeting Report

World Parkinson Day Community Workshop
Cedars-Sinai Medical Center, Los Angeles
April 11, 2026

At Cedars-Sinai’s April 11, 2026 Parkinson’s community workshop, speakers stressed that Parkinson’s extends far beyond tremor, spanning motor, non-motor, cognitive, and autonomic symptoms. The program reviewed newer treatments, remote digital monitoring, active clinical trials, atypical parkinsonism, and the growing role of palliative care. A closing presentation emphasized that surgery can help selected arm-and-leg motor symptoms, but not most midline or non-motor problems. Overall, the meeting offered a practical, hopeful roadmap for individualized multidisciplinary care. 

Video Archive

https://vimeo.com/1183179155/363e4c2b21?share=copy&fl=sv&fe=ci

The Cedars program director, Dr Tagliati, wrote Parkinson Disease for Dummies, which has excellent reviews.

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Cedars-Sinai’s Parkinson’s Patient Community Conference was held on Saturday, April 11, 2026, from 9:00 a.m. to 1:30 p.m. as a free hybrid event for patients with Parkinson’s disease and their caregivers. The published faculty included Michele Tagliati, John Mason, Echo Tan, Elliot Hogg, Yvette Bordelon, Jessica Besbris, Adam Mamelak, and Chae Kim. In his opening remarks, Dr. Tagliati noted that this was the 16th consecutive year Cedars-Sinai had organized its patient educational symposium, emphasizing continuity, education, and community support.

The workshop’s central message was that Parkinson’s disease is far more complex than tremor alone. Across the morning, speakers described Parkinson’s as a chronic, fluctuating, multidimensional illness involving motor symptoms, non-motor symptoms, treatment timing, monitoring challenges, research opportunities, atypical syndromes, supportive care needs, and surgical options. The tone was practical and hopeful: the meeting did not promise a cure, but it repeatedly stressed that care is becoming more individualized, data-informed, and multidisciplinary.

Dr. John Mason opened the educational content with a clear overview of the motor symptoms of Parkinson’s disease. He emphasized that Parkinson’s is heterogeneous, meaning patients do not all look alike and do not all share the same dominant symptoms. He reviewed bradykinesia, tremor, rigidity, dystonia, gait changes, freezing of gait, postural changes, postural instability, eye movement abnormalities, speech and swallowing problems, and later-stage dyskinesias. His presentation translated clinical language into daily life: smaller handwriting, difficulty buttoning clothing, reduced arm swing, softer speech, swallowing problems, and falls. He also made the important point that not every patient has major tremor, and that levodopa should not be delayed simply out of fear of dyskinesias, since dyskinesias are more tied to disease progression than to having started the drug “too early.”

Dr. Chae Kim followed with a broad review of non-motor symptoms, arguing that the classic motor picture is only the tip of the iceberg. She highlighted constipation, insomnia, REM sleep behavior disorder, anxiety, depression, apathy, cognitive changes, psychosis, fatigue, urinary symptoms, orthostatic hypotension, and temperature dysregulation. One of her most important points was temporal: several non-motor symptoms can begin years before the motor diagnosis, including constipation, REM sleep behavior disorder, and smell loss. She also emphasized that many patients experience these symptoms as more burdensome than tremor or stiffness. Her treatment discussion was broad and practical, spanning medication choices, sleep studies, bowel regimens, exercise, psychotherapy, attention to mood, and strategies for orthostatic hypotension. In the discussion afterward, she and Dr. Tagliati clarified that orthostatic hypotension is not usually “cured,” but it can often be managed effectively.

Dr. Michele Tagliati then shifted the focus toward digital biomarkers and remote therapeutic monitoring. His core argument was that modern medicine is optimized for acute illnesses, whereas Parkinson’s disease is chronic, variable, and context-dependent, making short office visits a poor fit as the sole basis for decision-making. He pointed to the mismatch between a condition that fluctuates hour to hour and a care model based on a brief office snapshot every few months. He reviewed wearable and app-based systems and highlighted Cedars-Sinai’s pilot experience with smartphone-based monitoring, especially the Neu Health platform. He described a model in which patients perform brief structured tasks through a phone, allowing clinicians to monitor tremor, gait, balance, dexterity, voice, cognition, mood, sleep, pain, blood pressure, medication timing, and symptom fluctuations between visits. His theme was that Parkinson care should become less reactive and more proactive, with continuous information replacing guesswork based on memory alone. 

The workshop also included a broader update on clinical research, with Dr. Elliot Hogg presenting what he described as a whirlwind tour of active and emerging Parkinson’s trials. His conclusion was especially notable: he argued that the era of “levodopa-only” thinking is fading, and that the field is moving toward a much broader therapeutic future that includes symptomatic treatments, disease-modifying strategies, wearable-device studies, stem-cell work, and other novel approaches. In the audience discussion, he addressed GLP-1-related research, noting active interest in the field but no currently recruiting California GLP-1 Parkinson trial within the scope he reviewed, while mentioning ongoing or mixed results from trials elsewhere, including liraglutide-related work.

A nice community interlude came next, when the audience was introduced to PCLA, described as a nonprofit built by Parkinson’s patients and care partners, for Parkinson’s patients and care partners. The representative explained that the group provides support groups, informational seminars, and other community resources, including even a comedy show, all aimed at helping patients and families navigate the steep information and support curve of living with Parkinson’s. That moment reinforced that the conference was not just about expert lectures, but also about peer connection and navigation support.

The newly available Part C materially deepens the second half of the meeting.

Dr. Yvette Bordelon spoke on atypical parkinsonism, which she described as the “other part of the Parkinson family” that resembles Parkinson’s disease but differs in mechanism, prognosis, and management. She reviewed the terminology—atypical parkinsonism, Parkinson’s-plus syndromes, and the newer phrase Parkinson’s-like disorders—and stressed that these disorders share parkinsonian motor features such as stiffness and slowness but have important distinguishing characteristics. She specifically identified the four main atypical parkinsonian syndromes as progressive supranuclear palsy, corticobasal degeneration/corticobasal syndrome, multiple system atrophy, and dementia with Lewy bodies, while also noting drug-induced and vascular parkinsonism as related considerations. Her examples were clinically concrete: early falls and eye movement problems in progressive supranuclear palsy, marked asymmetry, apraxia, dystonia, and myoclonus in corticobasal syndrome, and prominent dysautonomia in multiple system atrophy. She also made an important translational point: progress in Parkinson’s research can help atypical syndromes, and vice versa.

Dr. Jessica Besbris then presented on palliative care, a topic she explicitly said is often misunderstood. Her talk was one of the most conceptually important additions in Part C. She pushed back against the idea that palliative care is only for cancer or only for the end of life, and instead defined it as an approach aimed at improving quality of life for patients and families facing serious illness by addressing symptoms, psychosocial and spiritual distress, future planning, and medical decision-making. She described palliative care as a team sport, involving physicians, nurses, advanced practice providers, social workers, chaplains, pharmacists, and others, all working together to support patients and care partners. Her phrase that best captured the talk was that palliative care helps people “live as well as they can for as long as they can.” She emphasized that these needs may begin at diagnosis, not just near death, and may rise and fall over the course of illness.

Dr. Besbris also made the discussion very practical. She explained that in clinic she may talk with patients about what worries them about the future, what they want to plan for, and how to build a kind of roadmap for the course of illness. That roadmap can support discussions about advance healthcare directives, naming a decision-maker, and clarifying what matters most to the patient. Over time, those conversations may extend to major decisions such as feeding tubes, skilled nursing placement, travel as mobility worsens, and, later in illness, transitions to hospice care. Importantly, she did not present this as abstract philosophy. She also summarized evidence that adding palliative care to usual neurological care improves quality of life, decreases symptom burden, including depression, anxiety, constipation, pain, and other non-motor symptoms, reduces caregiver anxiety, and increases completion of future-planning documents. She noted that Cedars has a dedicated neuro-palliative team and described it as part of the institution’s multidisciplinary Parkinson infrastructure.

The final presentation, as you noted, was Dr. Adam Mamelak’s recorded video talk on surgical therapies for Parkinson’s disease. Dr. Mamelak framed surgery not as a cure, but as another form of symptom modulation. He divided treatment into three broad categories: medications, electrical stimulation, and ablative techniques. For the purposes of this talk, he focused mainly on deep brain stimulation (DBS) and focused ultrasound, describing them as the principal surgical modalities currently used day to day. One of the clearest take-home messages from his talk was his simple “arms and legs” rule: surgery tends to help arm- and leg-related motor symptoms such as bradykinesia, rigidity, dystonia, dyskinesia, and tremor, but is much less effective for midline symptoms such as swallowing difficulty, balance impairment, freezing of gait, and certainly not for non-motor symptoms like constipation or sleep problems. That clarification is often exactly what patients need when considering intervention.

He then compared the advantages of the two main surgical strategies. DBS, in his account, is reversible, adjustable, and non-destructive, allowing clinicians to titrate stimulation parameters over time. The tradeoff is that it is still a true surgical procedure, involving implanted hardware and cranial access. By contrast, lesion-based therapies such as focused ultrasound and stereotactic radiosurgery can be rapid, may avoid some traditional surgical risks, and can sometimes be used in older patients or even in those on blood thinners, with symptom relief appearing quickly in some cases. Dr. Mamelak also reviewed the brain targets used for interventional therapy, noting in particular that the ventral intermediate nucleus of the thalamus is a classic target for tremor-dominant disease. Overall, his talk cast surgery as a powerful but selective tool whose value depends heavily on choosing the right patient, the right symptom target, and realistic expectations.

Taken together, the full corrected transcript shows a workshop with a very deliberate arc. It began with recognition of symptoms, moved into monitoring and treatment, widened into research and atypical syndromes, then expanded further into quality of life, planning, caregiver support, and finally surgical intervention. In that sense, the conference did more than teach facts. It modeled a modern Parkinson’s program as something broader than medication management: a system involving specialists, data tools, patient organizations, supportive care, multidisciplinary planning, and selected procedures.

Bottom line: with Part C now included, the conference reads even more clearly as a meeting about the whole Parkinson’s journey. Earlier talks explained the disease and its monitoring; the newly added talks showed how Cedars is also thinking about diagnostic nuance, life planning, caregiver burden, and targeted surgical intervention. The final message was not just that Parkinson’s is treatable, but that care improves when it becomes multidisciplinary, anticipatory, and honest about both possibilities and limits.

I can also turn this into a blog-style article with a 75-word top summary or a cleaner meeting memo format.