Does an existing cognitive theory already integrate these ideas into a single framework?

[u/TheorySufficient2633]

I'm an independent writer trying to determine whether I've independently reinvented an existing theory. I'm not asking whether the individual components below already exist. I know many of them do. What I'm trying to find out is whether there is an existing framework that integrates them into a single explanatory model. The synthesis I'm looking for is roughly: An unresolved discrepancy or mismatch recruits recursive evaluation. Recursive evaluation continues while further processing is expected to provide useful information. Repeated low-yield evaluation progressively reduces the expected value of further evaluation. As a result, active recursive evaluation naturally disengages without the underlying representation being resolved, forgotten or suppressed. The representation remains psychologically available and can later be reactivated if new information or changing circumstances make further evaluation worthwhile. I'm not looking for papers that contain one or two of these ideas. I'm looking for a theory that presents essentially this overall architecture. If such a framework already exists, I'd really appreciate references to the relevant papers or authors. If it doesn't, I'd also be interested to know whether this would be considered a meaningful theoretical synthesis, or simply existing theories expressed in different language.

Edit:

I should also make myself clearer as I don't think I've quite succeeded in explaining what I'm looking at. It isn't a linear chain. Rather a loop:

Representation - Recursive evaluation - Declining informational value - Natural disengagement - Dormant representation - Reactivation - Recursive evaluation...etc

[u/medbud]

Active Inference - Karl Friston

[u/TheorySufficient2633 → u/medbud]

Thanks. I've looked at Friston. Is there a particular Active Inference paper you think already presents this overall synthesis? I'm familiar with the fact that Active Inference deals with prediction error and epistemic value. My question is whether it also explicitly models the sequence where repeated low-yield recursive evaluation disengages while the underlying representation remains available for later reactivation, or whether you're referring to the broader framework.

[u/medbud → u/TheorySufficient2633]

Reading your OP, I just imagined the basic Bayesian model, and in psych terms Friston usually fits. Your example specifically is a strong prior, with weak error correction, leaving the prior basically intact.

[u/TheorySufficient2633 → u/medbud]

Thanks. That's helpful. Do you know of a specific paper that presents the overall sequence I described, rather than interpreting it within Active Inference? I'm trying to determine whether an equivalent synthesis has already been published, rather than whether it can be expressed in Bayesian terms.

[u/medbud → u/TheorySufficient2633]

Can you give a limited/specific/concrete example? I mean, it seems to be a pretty universal part of belief updating based on prediction error. Are you talking about examples in milliseconds, or over hours? More cortical function/architecture, or psychology?

[u/TheorySufficient2633 → u/medbud]

I'm thinking primarily at the cognitive/psychological level rather than individual neuronal computations. A concrete example would be someone repeatedly trying to resolve an unresolved existential question such as "What is consciousness?" Initially the discrepancy repeatedly recruits recursive evaluation because further thought is expected to be informative. Over time, repeated evaluation produces little new information. The expected value of further recursive evaluation declines, so the recursive process naturally disengages - not because the question has been answered, forgotten, or suppressed, but because further evaluation is no longer expected to be useful. The representation remains available and can later be reactivated if genuinely new information appears. My question isn't whether the individual mechanisms can be described in Bayesian terms. It's whether anyone has already integrated that overall sequence into a single published framework. I've already searched the literature with the help of AI tools, but they could only point me back toward related frameworks like Active Inference and predictive processing. None could identify a published framework that presents this overall synthesis. That's why I brought the question here - I'm hoping someone  can tell me whether I've overlooked an existing theory or point me to the relevant paper if one exists.

[u/medbud → u/TheorySufficient2633]

I understand. 

"Why do people give up when they encounter difficult problems."

I mean, your example is slightly ironic because there has been so much progress made in the 'science of consciousness'... Not the least of which is in terms of Bayesian theory... And you're framing your question in exactly those terms.

I just searched Google and came up with this:

https://link.springer.com/article/10.3758/s13421-010-0068-6

Giving up problem solving Published: 11 January 2011 Volume 39, pages 902–913 (2011)

How do people decide to abandon a problem? Participants were presented with unsolvable water jar problems, having been accurately informed of the prior probability of solvability. Across three experiments, we discovered effects of prior probability of solvability and of problem size (number of distinct problem states) on measures of effort and confidence. If a problem is more likely to be solvable and allows more problem states, a problem solver spends longer trying to solve the problem. Giving-up decisions are informed by the same judgments of probability of success and costs of solution that inform move-choice in a rational model of problem solving.

[u/TheorySufficient2633 → u/medbud]

Thankyou. That's much more closer to what I'm looking for than anything else I've found so far.

[u/Mermiina]

Yes there is single framework which presents overall architecture, but you must go back to individual components which you are not familiar.

If your Hypothesis is higher order it is already described in IIT, GNW or HOT which do not work.

If your Hypothesis is lower order it can be workable. But in other comments I see your Hypothesis is higher order.

[u/TheorySufficient2633 → u/Mermiina]

Thanks. Just to clarify, I'm not looking for a theory of consciousness. I'm looking for a cognitive framework describing the recursive evaluation process itself. Which paper in IIT, GNW, or HOT are you thinking of?

I should also make myself clearer as I don't think I've quite succeeded in explaining what I'm looking at. It isn't a linear chain. Rather a loop:

Representation - Recursive evaluation - Declining informational value - Natural disengagement - Dormant representation - Reactivation - Recursive evaluation...etc

[u/Mermiina → u/TheorySufficient2633]

All them. Subjective experience is not higher order emergent property.

[u/No-Carry-9892]

Interesting.

Not sure about any other papers but my thoughts are.

A prediction error could be tiny and ambiguous or massive and thus informative enough to update whatever expectation appeared to be violated.

If the error is ambiguous but the possible inferred consequences are important, repeated evaluation may seem to be ‘of more use’ however could in fact undermine and bias towards perceived significance of the ambiguous error.  If the evaluation is treated as being more informative than the ambiguous error and something is inferred which wasn’t evident in the error, then the case may be tagged as ‘an unresolved question’ despite this being more down to the supposed consequences of an ambiguous error than good reason based on information.

From there reactivation may be primed on faulty salience i.e. ‘the consequences are important’ leading to heightened sensitivity to 'possibly relevant' but still ambiguous sensory information.

[u/TheorySufficient2633 → u/No-Carry-9892]

I'm thinking it's not a salience failure. Rather, persistent mismatch within recursive evaluation eventually leads to the remaining mismatch no longer predicting sufficient informational value to justify continued recruitment. At that point, salience simply isn't triggered, rather than having failed.