Iterated Insights

1. The basic intuition: we already scrutinize others, just informally

Most of us have had the experience of watching someone speak for a few minutes and forming a surprisingly rich impression. It is not just what they say. It is how they say it. Their timing, their facial expressivity, their level of tension, their energy, their coherence, their warmth or guardedness, and the way they handle a question they did not expect. Even when we cannot articulate it, we are extracting signal.

I think there is a straightforward conclusion hiding in that everyday intuition. Short videos contain a lot of psychologically relevant information. Humans already read it, often accurately, and often unconsciously. That does not mean we can diagnose people from a clip, and it does not mean our impressions are always fair or correct. But it does suggest that there is a learnable structure in the data.

This is where the idea of psychological super-resolution comes in. The machine learning system I am proposing, given a brief interaction, produces a calibrated, probabilistic readout of what is likely going on with the human in question, including what it is unsure about. But this readout is superhuman in its depth and accuracy. 

The real value of this is not voyeuristic mind reading. The value is self-understanding. Many of us would benefit from an honest mirror that is not socially constrained, not flattered by politeness, not distorted by resentment, and not limited to vague feedback. Something that can say, with specificity, “Here is what you tend to do when you are stressed,” or “Here is how your mood shows up in your timing,” or “Here is how you come across to others in moments when you feel perfectly normal inside.”

That is the ambition. A tool that helps a person see themselves more clearly.

2. What this system is, and what it is not

It is important to be explicit about what this system is not. It is not a DSM diagnosis machine. It is not a clinician. It is not a therapist. It is not an authority that pronounces what you are. It should not be used to screen employees, discipline students, deny insurance, or make legal decisions. It should not be framed as a detector of deception or “micro-expression lie reading,” which is both scientifically messy and socially dangerous.

The right framing is narrower and, in my view, more powerful. This is a measurement instrument for behavior. It is a system that takes a brief video interaction and outputs structured observations and calibrated probabilities about psychological dimensions that are plausibly expressed in that interaction.

If you want a concrete analogy, think of it as a psychological vital-sign monitor. It does not tell you who you are. It tells you what it sees right now, and how today compares to your own baseline.

That “delta from you” idea is central. In many domains, the most meaningful signal is not how you compare to an abstract population average, but how you compare to yourself. A person can be naturally quiet, naturally animated, naturally intense, naturally flat. None of that is pathology. The useful question is whether their own signature is shifting in a way that correlates with stress, sleep loss, burnout, mood destabilization, or recovery.

3. What it could learn to infer from minutes of interaction

If we assume an ideal world where we have massive amounts of correctly labeled data, including longitudinal outcomes and standardized assessments, what could we train such a system to do?

The most sensible targets are latent psychological dimensions rather than categorical diagnoses. In practice, that means the model outputs a vector of estimates, each with a probability distribution or confidence interval. Examples include:

mood valence and variability, including signals consistent with anhedonia or elevated positivity

arousal and threat sensitivity, including anxiety-like tension patterns

psychomotor activation versus slowing, which can show up in gait, gesture, and speech tempo

cognitive organization, including coherence, derailment risk, and conversational repair patterns

social reciprocity and pragmatic language style, including the timing and shape of turn-taking

impulsivity and inhibition style, visible in interruptions, pacing, and narrative control

irritability and frustration reactivity, visible in micro-escalations and recovery

dissociation-like markers, when present, such as discontinuity and spacing-out signatures

Notice what I am not claiming. I am not claiming the system can decide, from a clip, that someone “has” a disorder. A disorder diagnosis usually requires duration criteria, functional impairment, context, exclusion rules, and often a longitudinal course. A short interaction cannot contain all of that. But a short interaction can contain strong clues about state and style.

With repeated sampling over time, the system could separate trait from state. It could learn your baseline and detect departures that matter. It could also forecast risk in a cautious, probabilistic way, if the training labels include outcomes. That might include predicting likelihood of symptom escalation in the next week, or the likelihood that a person is entering a destabilized period that typically precedes relapse. Those forecasts should always be framed as probabilities with error bars, not as certainties.

There is also a research opportunity here that is easy to miss if you fixate on DSM. A system trained on rich longitudinal labels would likely discover clusters and phenotypes that cut across diagnostic boundaries. It might reveal new subtypes defined by temporal dynamics, reactivity patterns, or combinations of psychomotor and affective signatures. That might become scientifically valuable even if the tool is never used clinically.

3.2 Writing and reading out loud as psychological sensors

Video is not the only channel that carries psychologically relevant signal. Writing can function as a high-bandwidth record of a person’s mind over time, especially if you have more than a single sample. A few paragraphs written on the spot can reveal state-dependent features like affective tone, cognitive tempo, coherence, and interpersonal stance. A large corpus of someone’s real written communication adds an even more powerful capability: the system can learn the person’s baseline and detect meaningful deviations. It can quantify patterns such as self-focus, agency and attribution style, narrative organization, hedging versus directness, rumination loops, abstraction level, and stability versus drift across weeks and months. The goal is not diagnosis. The goal is a probabilistic behavioral profile with uncertainty, plus a “delta-from-you” readout that tracks when a person’s writing shifts in ways that plausibly correlate with sleep loss, stress, burnout, or recovery.

Reading out loud is a complementary probe because it standardizes content. When two people read the same passage, differences in output are less about topic choice and more about motor-speech control, prosody, timing, attention, and affective expression. A system trained on large, well-labeled data could measure articulation stability, speech rate variability, pause distributions, initiation latency, repairs and restarts, and how well a reader chunks phrases at punctuation. These features can serve as sensitive indicators of state, particularly when compared to the individual’s own baseline over time. In other words, writing captures how you think and frame the world in language, while read-aloud captures how your system executes language under a fixed template. Together they form a practical pair of psychological sensors that are easier to collect than full video interviews, and often less confounded by self-presentation.

4. The signal sources: what short video actually contains

If you want to believe this is possible, you need to believe there is enough signal in a few minutes of interaction to support meaningful inference. I think there is, but only if you treat the video as truly multimodal.

First, there is facial dynamics. This is not the pop-culture version of micro-expressions as a lie detector. The useful version is fine-grained facial movement patterns over time: expressivity range, symmetry, reactivity, timing, and how facial movement coordinates with speech. Some people show emotion primarily in voice and timing rather than in the face. Some people mask facial expression while their posture reveals tension. The point is not to privilege one channel, but to learn the joint pattern.

Second, there is voice and prosody. Humans use prosody constantly to infer state. Speech tempo, pitch variability, loudness dynamics, articulation clarity, pause structure, and response latency all carry information. The distribution of pauses often matters more than the average. The same is true for turn-taking. Does the person overlap? Do they wait too long? Do they respond quickly but shallowly? Do they answer with a delayed, effortful start? These are quantifiable signals.

Third, there is language content and semantics. The words matter, but so does structure. Narrative coherence, compression style, abstraction level, self-reference patterns, certainty language, and repair moves can all be measured. A person can reveal cognitive load not only by what they say, but by how often they backtrack, how they handle ambiguity, and whether they can maintain a stable thread across interruptions.

Fourth, there is movement. This is one of the most underrated channels. Watching someone walk, turn, sit, stand, gesture, and fidget can reveal psychomotor slowing or activation, tension patterns, restlessness, and general coordination signatures. These are not definitive markers of anything on their own, but they contribute to an overall profile. Movement also helps disambiguate facial and vocal signals that might be culturally shaped.

Finally, there is interaction. A monologue is informative, but an interview is richer. Social timing, reciprocity, gaze coordination, and how someone responds to a slightly unexpected question often contain more signal than rehearsed self-presentation. That is why, in the ideal design, the system is not just watching a clip. It is conducting a short, semi-structured conversation and analyzing both the answers and the way the answers unfold.

5. The interview protocol: questions as behavioral probes

If you want a system like this to be more than a vibe detector, you need to standardize the interaction. The goal is not to trap the person. The goal is to elicit enough structured behavior, across enough channels, that the model can make useful inferences with honest uncertainty.

The simplest way is a short, semi-structured interview that feels natural but is designed like a measurement instrument. It should include prompts that probe baseline style, narrative organization, affect dynamics, motivation, and state variables like sleep and energy. It should also include a small amount of movement.

Here is the basic idea. Each prompt is a behavioral probe. It is chosen not only for the semantic content it elicits, but for the timing, coherence, expressivity, and regulation dynamics it evokes.

A compact protocol might include:

Baseline calibration prompts

These establish how someone speaks when they are not emotionally activated.

“Walk me through yesterday from morning to night.” “Explain how to do something you know well, step by step.” “Teach me a concept you enjoy.”

Narrative coherence prompts

These probe sequencing, compression, and self-monitoring.

“Tell me about a recent challenge and how it unfolded.” “Tell me about a time you changed your mind about something important.” “Retell the same story in half the time.”

Emotion and regulation prompts

These are less about what happened and more about how the person’s system responds and recovers.

“Describe something that frustrated you recently. What did you do next?” “When you get stressed, what changes first in your body?” “What usually helps you come back down?”

Reward and motivation prompts

These probe anticipation, agency, and reward responsiveness.

“What are you looking forward to this week?” “What do you do for fun when you have free time?” “What has felt less rewarding than it used to?”

Sleep and activation prompts

These are extremely high signal for state shifts and often show up directly in tempo and psychomotor behavior.

“How has your sleep been over the last two weeks?” “Any days recently where you had much more energy than usual?” “Any days where your thoughts felt unusually fast or hard to slow down?”

Optional short cognitive probes

These should be brief, non-embarrassing, and explained as normal measurement tasks.

A 30-second verbal fluency task A short delayed recall prompt A simple summarization task

Movement tasks

This is where you get psychomotor signal that a seated interview can miss.

10 seconds walking toward the camera, turning, and walking back sit-to-stand and stand-to-sit a short segment of free gesture while describing something spatial

None of this diagnoses anyone. It simply produces a richer, more structured dataset from the person, and it narrows the model’s uncertainty.

6. Training methodology, assuming perfect labels

If we had all the data we needed and it was labeled correctly, what could we train a machine learning system to do?

Under that assumption, the system could be trained as a multi-task model that predicts dimensions, outcomes, and uncertainty. The most important design choice is to avoid training it as a single label classifier. A single label encourages the model to become overconfident and brittle. Multi-task learning encourages it to learn structure.

The training pipeline I imagine has four pillars.

First, large-scale pretraining on unlabeled video

The model should learn general representations of human speech, movement, and interaction from huge amounts of unlabeled data. This gives it broad competence at parsing humans as dynamical systems, before it ever sees a clinical label.

Second, fine-tuning on anchored labeled datasets

Then you fine-tune on datasets where the labels are genuinely anchored, not vague. In a perfect world, labels would include structured interview outcomes, symptom scales, impairment measures, medication status, comorbidity tags, and longitudinal follow-up. This gives the system targets that are closer to reality than to institutional noise.

Third, personal baseline modeling

The system should learn the individual. This is not a cosmetic feature. It is the difference between helpful and harmful. You want the model to build a baseline for you across multiple contexts, and then report deviations from that baseline. That is how you get meaningful self-insight without turning personality differences into pathology.

Fourth, explicit uncertainty and out-of-distribution detection

The system should be trained to know when it does not know. It should flag when the camera angle, lighting, language, culture, disability, or context is outside what it was trained on. It should say “insufficient evidence” as a normal outcome, not as an edge case.

If you do those four things well, the model becomes something like a high-dimensional estimator. It takes a short interaction and returns a probabilistic state description that is calibrated, bounded, and honest about uncertainty.

7. What the user receives: the “honest mirror” report

A system like this lives or dies by what it outputs. The output cannot be a label. It cannot be a vague horoscope. It has to be specific enough to be useful, and humble enough to be safe.

I think the best outputs fall into four layers.

Layer one: descriptive observations

This is the “what I saw” layer, written in plain language with concrete references.

“Your speech rate increased during the middle of the interview and your pauses became shorter.” “Your facial expressivity was relatively stable, but your posture tightened when discussing work.” “You interrupted yourself more often when answering open-ended questions.”

Layer two: probabilistic hypotheses with alternatives

This layer offers interpretations but keeps them conditional.

“This pattern can correlate with stress, sleep loss, or elevated activation. Sleep is a common driver. Does that fit your last week?” “The reduced reward language could reflect anhedonia, fatigue, or simply a busy schedule. Here is why the model is unsure.”

Layer three: delta-from-you tracking

This is where the system becomes genuinely valuable.

“Compared to your baseline over the last month, your response latency was longer and your psychomotor tempo was slower.” “Compared to your baseline, your affect reactivity was reduced in the negative memory prompt.”

Layer four: adaptive follow-up to reduce uncertainty

The system should be able to say, “I can sharpen this if I ask one more question,” and then choose the question that most reduces ambiguity. It is not interrogating you. It is doing measurement.

If the system is designed for self-understanding, it can also provide a “social mirror” option, where it answers the question people quietly want answered: “How do I come across?” That feedback can be delivered gently but directly, framed as tendencies and context-dependent impressions, not as absolute judgments.

8. Validation, failure modes, and the psychological Turing test

A proposal like this needs a success criterion that is not diagnosis, because diagnosis is not the goal. In this context, it is a standard of operational usefulness. A system passes the test if it produces stable, calibrated, and clinically plausible measurements that predict independent outcomes and match external criteria, without collapsing into bias or overreach. That validation can be tiered.

Within-person validation: does the model track meaningful change in the same individual across time, and do those changes correlate with sleep, stress, symptom scales, and functioning? Cross-context robustness: does it still work when the person is in a different room, different lighting, different device, or different conversational partner? Prospective prediction: can it forecast meaningful outcomes, cautiously and with calibration, rather than merely describing the present?

Failure modes are not theoretical. They are guaranteed unless addressed directly.

Label noise and circularity

If the labels reflect institutional bias, the model learns institutional bias. A system trained on messy labels becomes a machine that predicts the kind of diagnosis people tend to receive, not the kind of state they tend to be in.

Cultural and stylistic bias

Expressivity norms, dialect, disability, neurodiversity, and camera quality can all be misread as pathology. If the model cannot separate style from state, it will harm people.

Context blindness

Sleep deprivation, substances, acute stress, grief, and situational masking can dramatically alter behavior. A one-off clip can mislead without context. This is why repeated sampling and self-baselines matter.

Goodhart effects

If people learn to perform for the model, the model begins measuring performance rather than state. The solution is not policing. The solution is designing the system as a cooperative self-insight tool, not a gatekeeper.

Privacy and misuse

A system that can infer psychological state from video is inherently sensitive. If it becomes a tool for employers, schools, insurers, or law enforcement, it becomes a surveillance device. The proposal has to include governance and constraints, not as an afterthought but as a design requirement.

If you take those risks seriously, the project becomes much more defensible. It stops being a dream of automated diagnosis and becomes something closer to a new kind of personal instrument. A tool that helps you see your own patterns, track your own change, and get honest feedback without pretending to be a clinician.

That is a future I find interesting. Not AI as judge. AI as mirror, with calibration, humility, and boundaries.

Personal postscript:

One of the reasons that I’m interested in this, is because I’ve hesitated to put videos of myself or recordings of my voice online because of my own mental shortcomings. Due to various causes, but especially prolonged chronic stress, I’ve lost some mental capacity as I’ve aged. Sustained levels of cortisol affect the prefrontal cortex and hippocampus, two of the most important areas for high-level cognition. I talk about how I think this affected me I here:

https://adaptiveneurodiversity.com/my-chronic-stress/

For more than 15 years, I’ve decided not to create videos of myself lecturing for YouTube because I knew that in the future, artificial intelligence would be able to see right through me. Especially now that I have brain fog from long Covid, an AI system, properly calibrated, will be able to watch such videos and see as clear as day that I am on the Alzheimer’s continuum along with other related continua.

But you know what? I recently started making videos for YouTube to promulgate some of the ideas I’ve had in the last year. I’m trying to now accept and own my level of cognition and consciousness. And I want to encourage others to do the same. I make the videos, I try to speak clearly, I try to do enough research and preparation beforehand that there’s some value in them, and I just hit record. It’s very freeing and emboldening to just press that button and share what’s left of my soul with the internet. Don’t be afraid of having your intelligence scrutinized and ranked. Embrace your neurodiversity and allow it to make its own creative contributions to the world.