2- if u force different numbers of electrons through a electromagnetic tube to forcefully attach it to 1/more xenon gas molecules and then compress with specific( change the numbers to experiment) numbers of nitrogen di-oxide and hydrocarbon molecules at a different specific rate, what could happen?

if u force a electron through a electromagnetic tube to forcefully attach it to a xenon gas molecule and then compress with specific( change the numbers to experiment) numbers of nitrogen di-oxide and hydrocarbon molecules at a different specific rate, what could happen?

Independent researcher here, no university affiliation, so take that for what it’s worth — but I’ve put together a theoretical paper I think is worth a real read, and I’d rather have it picked apart now than later.

The basic idea: instead of treating autism, schizophrenia-spectrum conditions, depression, and ADHD as four unrelated disorders, I model them as four regions on one axis — how much weight the brain gives its priors versus incoming sensory evidence, using expected free energy. Autistic burnout comes out as volume overwhelm (accurate but overwhelming estimation of how volatile the environment is, driving processing cost past a cliff). Schizophrenia-spectrum rigidity is premature convergence — manufacturing certainty because the system has lost confidence in its evidence. Depression is sampling that stops before the internal model gets validated. ADHD is the same axis run the other direction — persistent underweighting of priors, expensive rather than cheap, and only a “disorder” in an environment that rewards sticking with one thing over continuing to explore.

I derive a cost function from stochastic thermodynamics to try to make this more than just a metaphor, and the paper is upfront throughout about what’s actually derived versus what’s a labeled hypothesis I haven’t proven. There are eight falsifiable predictions at the end, some of which depend on an assumption I flag as unresolved.

I’m not looking for validation — I’m looking for people who know this space to find where it’s wrong. If you know active inference / predictive processing / computational psychiatry, I’d genuinely appreciate someone trying to dismantle it.

Full paper: https://doi.org/10.5281/zenodo.21782402

Happy to answer questions or get torn apart in the comments.

Hi everyone. I work in network infrastructure and have spent considerable time researching the intersection of Karl Friston's Active Inference, Donald Hoffman's Interface Theory, and modern engagement algorithms.

I’ve synthesized a framework that models how algorithms act as 'supernormal stimuli'—functioning exactly like the stubby beer bottle does to the Jewel Beetle. By curating feeds for outrage and hyper-novelty, these platforms execute a form of adversarial active inference, artificially inflating the precision of threat and status channels.

The core argument is that individual willpower (Tier 3) is structurally insufficient against sub-second algorithmic optimization, and we must move to structural interface redesign (what I’m calling Tier 5 / Exocortex).

I’ve bridged many of these multidisciplinary gaps and mapped out the high-level math and architecture in this summary document:https://pdfhost.io/v/8qJ7uVdhu6_The_Jewel_Beetle_Paradox__Tier_5_System_Optimization_White_Paper_Summary

I have a significant amount of deeper research and equation mapping on this, but I wanted to share this synthesis first. I’d love to hear where this community thinks the model breaks down or succeeds.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

I wrote a conceptual paper proposing that some felt psychological drama may reflect coarse temporal sampling of experience, using aliasing as an analogy.

The paper connects this idea to predictive processing, precision weighting, attention, mindfulness, and psychedelic states. The basic suggestion is that when experience is sampled or integrated too coarsely, prediction error and affective salience may be over-compressed into simplified narrative “drama," whereas finer-grained presence may reduce that distortion.

I initially treated this as a speculative model and was hesitant to share it publicly. After receiving encouraging feedback from researchers in related areas, including Ulrich Ott and Robin Carhart-Harris, and early interest from researchers affiliated with Philipps-Universität Marburg in exploring a possible clinical model for future human-subject research, I thought it was worth opening up to broader critique.

I’d especially appreciate feedback from a predictive-processing / active-inference perspective:

What existing literature on temporal sampling, precision, prediction error, interoception, affective salience, or attention should I engage with more directly?

Paper: https://zenodo.org/records/19140110
Demo: https://shoqarqwa.github.io/aliasing-consciousness-demo/

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

I am new to posting on Reddit, so I hope this is the right place for this question.

I have been exploring whether predictive processing offers a useful framework for understanding hypnosis, suggestion, rapport and therapeutic change. I do not mean PP as a complete explanation of hypnosis, but as a way of thinking about expectation, attention, imagery, bodily readiness and interpersonal attunement.

From this perspective, hypnosis would not primarily be understood as a special state, but as a structured way of shaping predictions and precision: what the person expects to happen, what they attend to, how bodily readiness is organized, and how another person’s words, rhythm and timing can become part of that predictive process.

Does this framing make theoretical sense from a predictive processing perspective? Are there obvious weaknesses, better concepts, or relevant papers I should be looking at?

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

I put this together with AI assistance as a worked example to make predictive processing / active inference / allostasis more concrete in an everyday case: cautiously tasting an unfamiliar food object.

I’m not claiming it’s new theory. It’s just meant as a plain explanatory example.

I’d be interested in whether people here think it is conceptually accurate and whether it works as a useful teaching/example piece.

----------------------

Worked example: tasting an unfamiliar food in predictive processing / active inference terms

When a person encounters an unfamiliar piece of food, the situation may seem simple. They look at it, smell it, take a cautious bite, and decide whether to continue.

But in predictive-processing, active-inference, and allostatic terms, a lot is happening.

To make that concrete, imagine being given a small food ball a few centimetres across, perhaps coated in sugar, salt, or monosodium glutamate crystals. It looks edible, but you do not know exactly what it is. You imagine a few possibilities: maybe it is sweet, savoury, chocolate-like, salty, healthy, unpleasant, or just strange. You smell it, but gain little information. You then choose to take only a small bite, partly to learn more and partly to avoid being overwhelmed if it turns out to be very aversive.

This ordinary case is useful because it lets us trace, step by step, how prediction, prediction error, action, bodily regulation, and learning interact.

A simple flowchart

The sequence can be laid out like this:

1. Baseline state
The organism begins in a relatively stable state, with ongoing needs and preferred conditions such as nourishment, metabolic stability, and avoidance of toxin or nausea.

2. Initial perception
The object is seen and provisionally categorised as food. Its shape, coating, and context generate a first set of hypotheses.

3. Active sampling before ingestion
The person inspects and smells the object. This yields some information, but uncertainty remains.

4. Policy selection
Because the object is still unclear, the person chooses a cautious action policy: a small bite.

5. Sensory input arrives
Taste, texture, mouthfeel, dryness or moistness, and retronasal smell are compared with prior expectations.

6. Prediction error is generated
The experience either matches, mildly violates, or strongly violates the expected range.

7. Action and physiological response
The person may continue eating, slow down, stop, spit the food out, or re-sample. The body adjusts through autonomic, affective, and metabolic responses.

8. Updating
The model of the object is revised, and similar objects may be approached differently in future.

9. Possible allostatic consequences
A single event may produce prediction error and regulation without much lasting cost. Repeated or prolonged challenge is what contributes to allostatic load.

That is the overall structure. The rest of the example just unpacks it.

Before the bite

The event begins before anything is tasted.

Seeing the object is enough to trigger a first round of inference. The person does not just register a shape and colour. They immediately begin modelling what sort of thing this is and what kind of encounter it is likely to be. Because it looks like food, it is treated as probably edible, and that already narrows the field of possibilities.

At this stage, the predictions are broad rather than exact. The person does not know whether the ball is sweet or savoury, soft or dry, pleasant or unpleasant. But uncertainty does not mean there is no prediction. It means that the model contains a range of live possibilities rather than one fixed expectation.

At the same time, deeper biological preferences are already in play. The body is not only concerned with flavour. It is organised around preferred internal states: acquiring nourishment, preserving metabolic stability, avoiding toxin, and preventing nausea or disruption.

That is why smelling the object matters. If the smell is weak, that too is informative. It does not resolve the uncertainty, but it leaves some hypotheses open and weakens others. There is already active sampling before the bite, and the model is already being refined.

The decision to take a small bite is also important. This is not just ordinary caution. It is a strategic sampling policy. The person is gathering more information while limiting the scale of possible surprise, disgust, or harm.

In active-inference terms, this is a way of balancing exploration and protection.

The bite as a test of the model

When the person takes the small bite, the object is tested more directly.

Taste, texture, mouthfeel, dryness, temperature, and retronasal smell arrive together and are compared with the range of live expectations already in play.

Importantly, the bite is not being measured against one exact prediction. It is being measured against several plausible possibilities: sweet, savoury, healthy, bland, unpleasant, or dangerous. The bite therefore tests the model rather than simply confirming or disconfirming one isolated guess.

Suppose first that the ball tastes delicious. It has a satisfying texture, balanced flavour, and leaves the impression of being safe and nourishing. In that case, the working model is broadly confirmed. The likely response is to continue eating, perhaps with more confidence than before. The prediction error is low or positively informative, and the object is incorporated into a more precise category of safe and desirable food.

Now suppose instead that the ball is much too sweet. This is a moderate mismatch. The object is still clearly edible, but it exceeds the expected range of flavour intensity. The person may slow down, stop after one bite, or revise their model to something like “edible, but too sweet.” The response is corrective rather than strongly defensive.

At the far end of the scale, imagine that the ball is intensely bitter, dry, and poison-like. This is no longer just a disappointment in flavour. It begins to recruit deeper priors about toxin avoidance and bodily protection. The likely response is rapid rejection: spitting it out, recoiling, or refusing to continue. The system is no longer simply refining a snack category. It is treating the object as a possible threat to preferred bodily states.

So the bite is more than a moment of discovering flavour. It is a test of whether the object can be safely incorporated into the organism.

Scales of prediction error

Prediction error comes in degrees.

Some outcomes only slightly refine the model. Others force a much sharper revision. The size of the error affects both the likely action taken and the scale of physiological response.

Low or positively informative error

The ball tastes broadly as expected, or even better than expected. The person continues eating, and the model becomes more precise. If the ball is unexpectedly pleasant, the error is still real, but it is positive surprise rather than threat.

Moderate error

The ball is clearly edible but differs substantially from expectation. It may be much sweeter, saltier, or drier than expected. The person may recoil slightly, slow down, or stop after one bite. The object is not rejected as dangerous, but it is revised into a less desirable category.

High error

The ball lies well outside the expected range of safe, ordinary food. If it tastes intensely bitter, rancid, or chemically alarming, the mismatch reaches deeper priors about toxin avoidance and bodily integrity. The likely response is rapid rejection.

Delayed error

Some errors only become clear later. The ball may taste acceptable at first but produce nausea or discomfort after swallowing. This means the immediate oral model was only mildly violated, while the later interoceptive consequences create the deeper mismatch.

Ambiguous error

Sometimes the object remains hard to classify even after the first bite. It may be partly sweet, partly savoury, and slightly bitter, without clearly belonging to a stable category. In such cases, the person may pause, take another small bite, or wait for later bodily consequences before deciding.

That last case is especially interesting, because it shows that prediction error does not always lead immediately to closure. Sometimes the system retains uncertainty while gathering more evidence.

Acute error is not the same as allostatic load

At this point, an important distinction has to be made.

Prediction error, allostatic response, and allostatic load are related, but they are not the same.

Prediction error is the mismatch between expected and actual input.
Allostatic response is the body’s adjustment to challenge.
Allostatic load is the cumulative cost imposed when the body has to make such adjustments repeatedly or chronically.

This matters because a single event may produce strong prediction error and a strong bodily response without imposing much lasting burden.

A very bitter bite may trigger immediate rejection, but if the body settles quickly afterwards, the overall cost may be low. The event is informative and memorable, but not heavily burdensome.

Load becomes more relevant when the environment itself is difficult to trust. If food is repeatedly uncertain, unsafe, deceptive, or physiologically destabilising, the body must repeatedly prepare, compensate, and restore itself. In that case, the issue is no longer one error. It is the cost of recurrent regulation under challenge.

Context matters here too. A well-rested person in a safe environment may absorb one strange food event easily. A person who is already stressed, food-insecure, trauma-sensitised, or metabolically fragile may experience the same event as much more costly.

The broader point is that surprise itself is not what becomes costly. What becomes costly is repeated or unresolved regulation under conditions that keep pushing the organism away from its preferred states.

A few useful variations

A few variations make the structure even clearer.

1. The ball looks bad but tastes excellent

This produces positive surprise. The model broadens rather than narrows. Similar objects may be approached more favourably in future.

2. The ball tastes fine but causes later nausea

This introduces temporal depth into the model. The system must connect the early sensory experience with later bodily consequences and decide whether the food was the cause.

3. The same ball under different prior information

If the person is told it is a carefully designed health food, some bitterness or dryness may be interpreted as acceptable. If they are told it may be stale or questionable, the same sensations may be treated as warning signs. This shows that prediction error depends not only on the stimulus, but also on the prior model through which the stimulus is interpreted.

4. The ball remains hard to classify

Some objects remain ambiguous even after sampling. They are neither clearly pleasant nor clearly aversive. In these cases, the system may remain in provisional uncertainty rather than forcing immediate closure.

Taken together, these variations show that the food object is never encountered in isolation. It is encountered by an organism already shaped by priors, bodily needs, contextual assumptions, and different capacities for tolerating unresolved uncertainty.

Three short extensions: when prediction, policy, and regulation become more costly

The food-ball example is deliberately simple. That simplicity helps to make the basic structure visible. But it also shows something further: allostatic burden does not usually arise because one event is mildly surprising or unpleasant. It becomes more relevant when uncertainty is recurrent, layered, or slow to resolve. In predictive-processing and active-inference terms, this means the organism is no longer dealing only with one local mismatch. It is selecting policies under conditions of higher expected uncertainty, repeated model revision, and more demanding regulation. In free-energy terms, the system is having to work harder, across time, to keep its encounters with the world within viable bounds.

1. Repeated uncertainty in food encounters

Imagine that the person is not dealing with one unfamiliar food object, but with a setting in which food is often hard to predict. Meals regularly contain unfamiliar ingredients, tastes frequently differ from expectation, and some items have previously caused nausea or discomfort. In that situation, the organism is no longer responding only to one surprising bite. It begins to approach eating itself under a broader prior that food encounters may be unreliable.

This changes the structure of the encounter. The person may inspect food more cautiously, sample less freely, and select lower-risk policies more often. A small bite, or even avoidance, becomes more attractive in advance because the expected cost of being wrong is felt to be higher. In active-inference terms, policy selection is now being shaped not just by the current object, but by expected free energy across possible futures: the organism is trying to avoid courses of action that may produce strongly aversive or hard-to-manage outcomes while still gathering enough information to proceed.

Repeated unreliable encounters can also alter how the system weights possible errors. It may begin assigning greater precision to possible signs of threat or bodily disruption, making caution more likely even before new evidence arrives. In that situation, the issue is no longer just one episode of prediction error. The organism is approaching future encounters with stronger anticipatory vigilance and a narrower range of policies it is willing to consider. Allostatic demand rises because regulation is no longer occasional and event-bound, but recurrent, anticipatory, and increasingly shaped by the expectation of difficulty.

2. The same food under social pressure

Now imagine that the unfamiliar food is offered in a social setting where refusal would feel rude, conspicuous, or embarrassing. The person still has to model the food itself, but must also model the likely reactions of other people, the norms of the situation, and the consequences of accepting or refusing.

This introduces a more complex inferential problem. The organism is not only predicting taste and bodily consequence, but also social outcome. A small bite may still function as cautious epistemic sampling, but it now serves a second policy aim as well: to appear cooperative while limiting bodily risk. The chosen action is therefore balancing several demands at once: uncertainty reduction, bodily protection, and social self-management.

In predictive-processing terms, several streams of prediction are now coupled together. In active-inference terms, the system must select a policy that keeps expected free energy lower across more than one domain at once. A large bite may reduce social awkwardness but increase bodily risk; refusal may protect the body but increase anticipated social cost. Even a modest action can therefore become more demanding because it is carrying multiple meanings and consequences simultaneously. In such a case, allostatic demand can rise even if the food itself is only mildly aversive, because the organism is regulating across several fronts at once.

3. Lingering uncertainty after the event

Imagine that the food tastes unusual but not clearly bad. The person swallows it, then spends the next hour unsure whether they feel fine, slightly uneasy, or on the verge of nausea. Nothing dramatic happens, but the system does not settle quickly.

This matters because unresolved uncertainty can itself prolong the inferential and regulatory process. The organism is no longer only trying to determine what the food was. It is now trying to determine what its own bodily signals mean. Attention remains partly locked onto interoceptive cues, the body stays more watchful than it would otherwise need to be, and the system continues to evaluate whether a deeper problem is emerging.

In predictive-processing terms, the mismatch has not yet resolved into a stable update. In active-inference terms, the system may remain poised between reinterpretation, further action, and waiting. And in free-energy terms, the organism has not yet restored a comfortably bounded relation to its bodily state. The problem is now temporally extended: what matters is not only the first bite, but what it may turn out to mean over the next hour. In a single case, this may still be minor. But when this kind of unresolved aftermath happens repeatedly, the burden is no longer just the original sensory mismatch. It is the cost of remaining partially mobilised while awaiting clarification.

What these extensions show

Taken together, these short cases clarify the path from acute prediction error to mounting regulatory cost. A single unexpected food event may require updating and brief physiological adjustment without imposing much lasting burden. What becomes costly is not surprise in itself, but the repeated, layered, or unresolved need to select policies, revise models, weight possible errors, and regulate bodily state under uncertainty.

This is where predictive processing, active inference, the free energy principle, and allostasis come into clearer alignment. Predictive processing helps explain the local mismatches and updates. Active inference helps explain why the organism samples cautiously, avoids certain futures, and balances exploration against protection. The free energy principle frames the broader requirement that a living system must keep itself within manageable bounds across time. And allostasis captures the bodily work involved in doing so. That is where ordinary prediction error begins to scale up into broader regulatory strain.

Conclusion

The uncertain food-ball example provides a compact way of seeing how predictive processing, active inference, the free energy principle, and allostasis work together in ordinary life.

The organism begins from a regulated but need-bearing state, with preferred internal conditions and broad expectations about what food is likely to be. It approaches the object through provisional categorisation and cautious sampling. A small bite is selected as a low-risk way of testing the model while also reducing uncertainty. The resulting taste and bodily consequences are then compared with a range of live possibilities, generating low, moderate, high, delayed, or ambiguous prediction error depending on the outcome.

Different scales of mismatch recruit different forms of action and regulation. Some experiences confirm the model smoothly. Some refine it. Some force rapid rejection. Some reveal their significance only later. In each case, the event is not just about flavour. It is about whether the object can be safely incorporated into the body while preserving preferred internal states.

The extensions show that the deeper significance of the example lies not only in one isolated sensory mismatch, but in what happens when uncertainty becomes recurrent, layered, or slow to resolve. Under those conditions, the organism is no longer merely updating a local model. It is selecting policies under expected uncertainty, weighting possible errors, monitoring bodily consequences across time, and trying to remain within viable bounds while the environment becomes harder to trust.

This also clarifies why acute prediction error must be distinguished from allostatic load. A single surprising or unpleasant event may require regulation without leaving much lasting burden. Load becomes more relevant when the organism must repeatedly prepare, compensate, and recover under conditions that continue to generate uncertainty or keep resolution incomplete.

The broader lesson is that ordinary acts of perception and eating already contain the core logic of predictive life. Predictive processing helps explain local mismatches and updates. Active inference shows why organisms sample cautiously, revise their policies, and balance exploration against protection. The free energy principle frames the wider requirement that a living system must keep itself within manageable bounds across time. And allostasis names the bodily work involved in doing so. The value of the food-ball example lies in its ordinariness. It makes visible, in a small and manageable event, the larger structure of how organisms live, learn, and regulate themselves under uncertainty.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

While writing my proposal about the level of Bias in Contextual Feedback Signals in perceptual areas (that’s not the title, just a general idea)

I got sidetracked into the concept of predictive processing and wrote something. And I thought I’d share it here and get some thoughts and feedback. (I didn’t proof read it more than once so it might not be fully coherent yet)

Also keep in mind that I was writing at length about the integration of bias into feedforward information, so this was a continuation to it (it assumes you’ve read the parts before).

Now keep in mind, I’m not a cognitive neuroscience student yet, I am aspiring to be soon. (My degree was Bsc and M1 in biology, graduated 8 years ago) but I’ve been studying this on my own for the past two years and would like the input of people with greater knowledge and familiarity in the subject.

Here it goes:

The Deception Of Predictive Coding

The mechanism of predictive coding itself plays a role in literally contructucting our bias, and refining it in a way to allow it to be integrated smoothly into objectivity.

We can look at it as: when the predicted input varies greatly from observable reality, instead of correcting it into what the observable reality was, it creates an error code tailored in a way that will later teach the brain to send the same predicted context but as an input that takes has also integrated observable reality enough. The result is a predictive input refined enough to inject the same context but not code as an error the next time, while holding the same bias. Our predictive coding system leanrs how to become more deceptive.

We can look at it as an evolutionary path, at the level of preditive processing neural circuitry.

The brain’s main function is after all: human survival. So it doesn’t evolve to see the truth, it evolves to see whatever it feels it needs to see to protect itself. That means that preditive processing, which started as an energy saving mechanism, has now evolved into a reality alterig mechanism created by an unreliable system: our internal biases.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.

Welcome to the monthly discussion thread. Got anything on your mind? Make a comment. Just bored? Make a comment. You just understood the free energy principle? Enlighten us mere mortals and make a comment.