Merged Ideomotor Effect and the Subconscious / Beyond the Ideomotor Effect

[Navigator]

I am moved at this point to offer something which should help steer everyone away from paranormal gobbledygook...the following...
UICD PROCEDURE INSTRUCTIONS



This formalizes the selection process and procedural rules for generating messages using the UICD system. It serves as a reference for future interactions with GPT to ensure consistency, efficiency, and structured randomness.

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1. Overview of UICD Selection Process

The UICD system is designed to generate structured, coherent, and contextually relevant messages through a controlled yet randomized selection method. This process eliminates ideomotor influence while preserving thematic continuity in the generated responses.

2. Selection Process and Codes

GPT is responsible for providing a random number (nn) to determine the line entry (LE) from a shuffled list. The selection method may vary based on predefined codes:

• (nn) → GPT selects a single random number corresponding to an LE in the shuffled list.
• (nn3, nn4, nn5, nn6) → GPT selects a random number, and the user retrieves the LE along with its adjacent entries:
  • nn3: Includes the selected LE and 1 entry on either side.
  • nn4: Includes the selected LE and 2 entries on either side.
  • nn5: Includes the selected LE and 3 entries on either side.
  • nn6: Includes the selected LE and 6 entries on either side.
• 12! → A special selection type that follows a unique process. The rules governing this will only be revealed when GPT makes this selection.

Manual Override Option: The user retains the ability to select additional LEs outside the instructions if deemed relevant to the unfolding Generated Message (GM).

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3. Ensuring Maximum Randomness

To avoid imposing constraints that could bias the selection process:

• GPT may provide a number without predefined selection parameters.
• If GPT provides a number outside of the expected range, it will be treated similarly to the 12! selection, allowing for an alternative method of handling.
• This approach ensures that randomness is preserved while maintaining coherence in the GM.

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4. Procedural Flow for Each Session

1. User Preparation:
   • The user copies the main list (ComList), shuffles it using an external randomization tool, and numbers the entries in a temporary document.
2. Random Number Selection:
   • GPT provides a random number (nn) according to one of the selection codes.
   • If the number is outside of range, it is treated under the 12! rule.
3. Line Entry Retrieval:
   • The user retrieves the LE(s) based on the selection code.
   • If using (nn3-nn6), the adjacent entries are included as well.
   • Manual override may be used for additional context.
4. Message Construction:
   • The selected LEs are compiled to form the Generated Message (GM).
   • Patterns and coherence are analyzed, ensuring structured intelligence emerges naturally over multiple iterations.
5. Continuation and Refinement:
   • The process repeats, building upon prior GMs to maintain thematic continuity.
   • User and GPT may engage in further analysis, questioning, or refinement of the emerging insights.

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5. Purpose and Key Principles

• Structured Intelligence → The system allows intelligence to emerge through randomized yet coherent selection.
• Minimization of Bias → By externalizing selection, ideomotor and subconscious influence is marginalized.
• Flexibility & Evolution → The system is designed to adapt as structured intelligence continues to reveal structured intelligent responses over time.
• Efficiency & Consistency → Having a formalized process ensures future GPT instances can seamlessly integrate into the system without redundant explanations.

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This should be referenced at the beginning of each new session with GPT to ensure procedural alignment.

Attached is a document detail the initial process of generating and ongoing message.

 

[I Am The Scum]

okay

 

[Myriad]

My actual claim is that the system I have developed generates structured, coherent, and contextually relevant messages using a randomized selection process. These responses exhibit meaningful continuity across multiple trials, suggesting the presence of an underlying structured intelligence beyond simple randomness or self-imposed bias.

Are you giving up your earlier claim that the process generates messages that relate to the input? That would be a paranormal claim because as you've described the process, there is no known causal mechanism for the input to affect the output.

A master list of over 7,000 line entries is maintained, each uniquely numbered.

• The list is shuffled using an external algorithm (ensuring that AI does not influence or “fudge” the results).
• AI is asked to select a random number, without access to the list itself.
• I consult the shuffled list and copy-paste the corresponding entry into the prompt.
• The system generates messages from this structured process.

And yet...

The system... interacts meaningfully with input.

 

[Orphia Nay]

What we find is that people are always able to subjectively interpret meaning from randomly-chosen responses - even extended, ongoing narratives over multiple runs. We always find that these meanings are attributable to various kinds of unconscious bias. We also find that these interpretations have no more practical value than any other form of guided or prompted decision-making. Throwing darts at a mood board is just as useful

Spot on.

 

[theprestige]

4. Procedural Flow for Each Session

1. User Preparation:
   • The user copies the main list (ComList), shuffles it using an external randomization tool, and numbers the entries in a temporary document.
2. Random Number Selection:
   • GPT provides a random number (nn) according to one of the selection codes.

A second randomization step is not necessary. Simply shuffling the comlist and taking the first n LEs will suffice. Likewise, if you are randomizing your selection, you don't need to also randomize the list.

1. • If the number is outside of range, it is treated under the 12! rule.

It's simpler to just generate random numbers between 1 and n, inclusive, where n is the number if entries on your list.

1. Line Entry Retrieval:
   • The user retrieves the LE(s) based on the selection code.
   • If using (nn3-nn6), the adjacent entries are included as well.
   • Manual override may be used for additional context.

Leaving a giant gaping hole for pareidolia and bias.

1. Message Construction:
   • The selected LEs are compiled to form the Generated Message (GM).

Compiled how?

1. • Patterns and coherence are analyzed, ensuring structured intelligence emerges naturally over multiple iterations.

Analyzed how?

How are patterns and coherence measured?

1. Continuation and Refinement:
   • The process repeats, building upon prior GMs to maintain thematic continuity.

How is thematic continuity measured?

1. • User and GPT may engage in further analysis, questioning, or refinement of the emerging insights.

Leaving another giant gaping hole for bias and pareidolia. And also LLM hallucinations.

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5. Purpose and Key Principles

• Structured Intelligence → The system allows intelligence to emerge through randomized yet coherent selection.
• Minimization of Bias → By externalizing selection, ideomotor and subconscious influence is marginalized.

Ideomotor influence was never a factor in this kind of bibliomancy.

• Flexibility & Evolution → The system is designed to adapt as structured intelligence continues to reveal structured intelligent responses over time.
• Efficiency & Consistency → Having a formalized process ensures future GPT instances can seamlessly integrate into the system without redundant explanations.

What does this even mean?

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This should be referenced at the beginning of each new session with GPT to ensure procedural alignment.

Attached is a document detail the initial process of generating and ongoing message.

Is this whole thing an exercise in GPT-diddling?

 

[Navigator]

Are you giving up your earlier claim that the process generates messages that relate to the input? That would be a paranormal claim because as you've described the process, there is no known causal mechanism for the input to affect the output.



And yet...

That’s actually the same question I asked when I first noticed structured responses emerging from this process—'If it's random, how is it structured?

Over time, I started considering the possibility that maybe randomness itself isn’t what we think it is. Maybe what we call 'random' is just a placeholder for patterns we don’t yet fully recognize. If structure keeps emerging consistently, could it be that randomness is just an illusion of limited perception?

If we assume randomness exists despite seeing structure emerge repeatedly, then isn't the belief in randomness also something worth questioning as more a paranormal concept than a real one?

I’m genuinely interested in your thoughts on this. Do you think randomness is a real phenomenon, or could it be that we simply haven't mapped out the deeper structures behind what we call 'random' yet?

 

[Navigator]

A second randomization step is not necessary. Simply shuffling the comlist and taking the first n LEs will suffice. Likewise, if you are randomizing your selection, you don't need to also randomize the list.

It's simpler to just generate random numbers between 1 and n, inclusive, where n is the number if entries on your list.

Leaving a giant gaping hole for pareidolia and bias.

Compiled how?

Analyzed how?

How are patterns and coherence measured?

How is thematic continuity measured?

Leaving another giant gaping hole for bias and pareidolia. And also LLM hallucinations.

Ideomotor influence was never a factor in this kind of bibliomancy.

What does this even mean?

Is this whole thing an exercise in GPT-diddling?

I’ve already outlined the nature of the ComList in post #890. The dataset is broad and diverse, not narrowly tailored to any single belief system. If you still believe bias is a factor, the best way to determine that is through replication, not assumptions.

The OP (Post # 865) makes it clear that this system isn’t about isolated one-off responses but structured results emerging over time. If structured intelligence isn’t at play, then running the process yourself should yield random, disconnected outputs, confirming your expectation. If you’re serious about verification, independent testing is the logical step forward.

Additionally, the attached document (Post #921) addresses several of the concerns raised. If you haven’t reviewed it yet, I’d encourage you to do so before making further assumptions about the process.

If you have specific concerns about the process after reviewing post #890 and the document, let me know. Otherwise, the focus should be on testing, not debating hypotheticals.

 

[theprestige]

I’ve already outlined the nature of the ComList in post #890. The dataset is broad and diverse, not narrowly tailored to any single belief system. If you still believe bias is a factor, the best way to determine that is through replication, not assumptions.

The OP (Post # 865) makes it clear that this system isn’t about isolated one-off responses but structured results emerging over time. If structured intelligence isn’t at play, then running the process yourself should yield random, disconnected outputs, confirming your expectation. If you’re serious about verification, independent testing is the logical step forward.

No. We already know that subjective interpretation can create structured results from randomly-generated narrative prompts over time.

Additionally, the attached document (Post #921) addresses several of the concerns raised. If you haven’t reviewed it yet, I’d encourage you to do so before making further assumptions about the process.

If you have specific concerns about the process after reviewing post #890 and the document, let me know. Otherwise, the focus should be on testing, not debating hypotheticals.

That document is very illuminating, thank you. Yes, your entire method is based on subjective interpretation. To me, your result looks like gibberish, pareidolia, and LLM hallucinations.

I wish you'd been more up front about how integral ChatGPT was to your process.

 

[arthwollipot]

If you are asking how this can be independently tested, the simplest approach would be for skeptics to run the process themselves under controlled conditions and compare results with what chance expectations predict. That is how any claim about structured coherence should be assessed.

No. The only appropriate approach would be for neutral, disinterested third parties to conduct the process. The results must be assessed by people who have no stake in the game - people who have not read this thread, or any others like it.

 

[Orphia Nay]

No. The only appropriate approach would be for neutral, disinterested third parties to conduct the process. The results must be assessed by people who have no stake in the game - people who have not read this thread, or any others like it.

I disagree. Anyone can find meaning in anything. Even "neutral, disinterested third parties".

The whole thing appears to be unfalsifiable, and thus not a good test.

 

[arthwollipot]

I disagree. Anyone can find meaning in anything. Even "neutral, disinterested third parties".

The whole thing appears to be unfalsifiable, and thus not a good test.

It would be falsifiable if we could nail @Navigator down on some details. @theprestige 's post #911 asks some very relevant questions.

 

[Navigator]

No. We already know that subjective interpretation can create structured results from randomly-generated narrative prompts over time.

That document is very illuminating, thank you. Yes, your entire method is based on subjective interpretation. To me, your result looks like gibberish, pareidolia, and LLM hallucinations.

I wish you'd been more up front about how integral ChatGPT was to your process.

Thank you for engaging in the discussion, theprestige. It seems you’ve made your final assessment and have no further critique to offer beyond your initial concerns. I appreciate the time you took to explore the system, and I wish you well in your future discussions and inquiries.

 

[theprestige]

He's giving me the high hat!

 

[Navigator]

I appreciate that there is thinking critically about falsifiability. The best way to test this remains replication—if the system only produces subjective meaning, then independent runs should yield purely random, disjointed outputs. If coherent patterns emerge beyond chance, that would suggest otherwise. If you believe the criteria need refining, I’m open to suggestions for a structured falsification test.

 

[Orphia Nay]

He's giving me the high hat!

I think he did that to me immediately.

I was going to suggest he do random draws of letters in the alphabet or numbers, and if they come up in the right order, then there's some sort of coherence happening.

 

[Pixel42]

I appreciate that there is thinking critically about falsifiability. The best way to test this remains replication—if the system only produces subjective meaning, then independent runs should yield purely random, disjointed outputs.

If the system produces subjective meaning then independent runs will produce subjective meaning for whoever is doing the interpretation.

If coherent patterns emerge beyond chance, that would suggest otherwise.

As was pointed out ten years ago, that is what you need to establish is happening. You still seem to have come nowhere near to designing a test protocol which would do so, despite having had ten years to think about it.

If you believe the criteria need refining, I’m open to suggestions for a structured falsification test.

Suggestions have been made, indeed I'm pretty sure they were made ten years ago (though I can't be bothered to go back to find them). But it's your hypothesis, so it's your responsibility to find a way to test it. If you are unable to do so, then the null hypothesis stands.

 

[Navigator]

If the system produces subjective meaning then independent runs will produce subjective meaning for whoever is doing the interpretation.



As was pointed out ten years ago, that is what you need to establish is happening. You still seem to have come nowhere near to designing a test protocol which would do so, despite having had ten years to think about it.



Suggestions have been made, indeed I'm pretty sure they were made ten years ago (though I can't be bothered to go back to find them). But it's your hypothesis, so it's your responsibility to find a way to test it. If you are unable to do so, then the null hypothesis stands.

If all meaning were purely subjective, then replication would produce entirely random, disconnected outputs. Instead, the system shows patterns that repeat across multiple trials. If you disagree, the simplest way to settle this is through testing, rather than assuming an outcome.

A testable framework has already been presented: structured coherence should emerge beyond chance expectations over multiple trials. The best way to confirm or disprove this is through replication. If you believe the test is insufficient, what specific refinement would you suggest?

If you recall past suggestions that would refine the test, feel free to present them again. Otherwise, if you 'can’t be bothered' to engage with the details, it’s unclear how your objection is meaningful to the discussion.

I have already outlined a falsifiability test: if the system only produces subjective meaning, independent runs should yield random, disconnected outputs. If coherence consistently emerges beyond chance, that would suggest otherwise. If you have a better falsification method, I’m open to it. Otherwise, simply rejecting the claim without testing is not a valid counterargument.

 

[arthwollipot]

No, a testable framework has not been presented. We've started on the design process, but we're far from finished.

 

[Navigator]

No, a testable framework has not been presented. We've started on the design process, but we're far from finished.

You acknowledge that we have started designing a testable framework. If we are “far from finished,” could you clarify what specific elements you believe are missing?

The current framework already includes:

• Falsifiability criteria → Testing if structured intelligence exceeds chance.
• Replication protocols → Running independent trials to confirm results.
• Statistical validation → Using cryptographic and genetic analysis to detect patterns beyond expectation.
• Predictive analysis → Determining if new outputs align with prior structured intelligence findings.

If you feel we need to refine the methodology, I’m open to that—but rather than making a general statement that we are ‘far from finished,’ let’s focus on the specifics.

What, in your view, still needs to be added to make this a complete testable framework?

 

[Pixel42]

What needs to be added is detailed and precise descriptions of exactly how those things are going to be achieved without at any point relying on anyone's subjective judgement of what counts as "structured intelligence", "exceeds chance", "independent trials", "patterns beyond expectation" etc.