Individual differences in the cognitive mechanisms of planning strategy discovery

Falk Lieder; Ruiqi He

REVIEW 3 major objections 5 minor 16 references

Individual differences in the cognitive mechanisms of planning strategy discovery

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that people discover planning strategies through a mix of metacognitive mechanisms that differ across individuals, and that adding pseudo-rewards, subjective effort valuation, and termination deliberation to a…

desk verdict Honest null result, but the individual-differences claims rest on an unvalidated BIC grouping and should not be taken at face value. read the letter →

arxiv 2505.23519 v1 pith:4HCGYSYV submitted 2025-05-29 cs.AI

Ruiqi He , Falk Lieder This is my paper

classification cs.AI

keywords planningstrategydiscoverymetacognitivereinforcementlearningindividualdifferencespseudo-rewardssubjectiveeffortvaluationterminationdeliberation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks why some people discover efficient planning strategies faster than others, and whether a metacognitive reinforcement-learning model can be brought closer to human behavior by adding three cognitive mechanisms. The authors claim that a larger share of participants are best explained by models that include intrinsically generated pseudo-rewards, subjective effort valuation, or termination deliberation than by the plain model, and that usage differs strongly across individuals. They report that pseudo-rewards and subjective effort valuation are associated with better performance and more planning, while termination deliberation is associated with worse performance and less planning. The central finding, however, is partially negative: even with these mechanisms, the fitted models still discover the adaptive strategy more slowly than human participants do. The paper concludes that the mechanisms offer insight into individual differences but that some additional factor is still missing from the model family.

What carries the argument

The central object is the metacognitive Reinforce model, a policy-gradient learner in which a planning strategy is a weight vector over 63 features of belief-computation pairs, and cognitive operations are selected by softmax over approximate meta-level Q-values. The paper's contribution is to add three mechanisms to this learner: pseudo-reward, which issues an intrinsic reward equal to the improvement in expected value caused by a planning operation; subjective effort valuation, a per-participant free parameter that shifts the reward of planning operations according to how effortful they feel; and termination deliberation, which sets the value of stopping to plan to the expected return of acting immediately. All eight combinations of these mechanisms are fitted to each participant's click sequence, and the lowest-BIC variant defines the mechanism group that participant is assigned to. The machinery does the work of turning hypotheses about mental mechanisms into distinguishable predictions about click-by-click planning behavior.

What would settle it

A decisive check would be to fit the same participants' click sequences with an independently specified model family that uses a different feature representation but the same three mechanism ideas, then ask whether the same people are still classified as pseudo-reward, subjective-effort, and termination-deliberation users with the same performance ordering; if the groupings track model-family quirks rather than mechanism use, the proportions would shift arbitrarily. A second check would be to collect per-trial self-reports of whether participants considered stopping to plan and test whether those reports predict the termination-deliberation classification.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that human planning-strategy discovery is not one homogeneous learning process. When the predecessor metacognitive Reinforce model is extended with individually fitted pseudo-rewards, subjective effort valuation, and termination deliberation, roughly 70% of participants are best described by a variant containing at least one of these mechanisms, with about 30% best fitted by pseudo-reward variants, 35% by subjective-effort variants, and 9% by termination-deliberation variants. These groups differ in measurable behavior: pseudo-reward and subjective-effort participants score higher and plan more, while termination-deliberation participants score lower and plan less. The extension does not, however, remove the known shortfall: simulated participants still discover the adaptive strategy at a slower rate than the real participants, so the paper's stated conclusion is that the augmented model family still lacks a factor needed to match human discovery speed.

Load-bearing premise

The result stands on the assumption that assigning each participant to the model variant with the lowest BIC—a statistical fit score computed from just 120 planning trials—reveals which cognitive mechanism that participant actually used.

Editorial extensions

If this is right

People who generate intrinsic pseudo-rewards or value mental effort as less costly should discover the adaptive planning strategy faster and plan more, while people who deliberate about termination should discover it slower and plan less.
Because the same planning task is best explained by different mechanisms in different participants, average discovery curves can hide opposing subgroups, so mechanism-specific analyses should be used when studying strategy discovery.
None of the eight model variants closes the gap between model and human discovery speed, so the missing ingredient in metacognitive reinforcement learning is something other than these three mechanisms.
Fitting a participant's click sequence to the eight variants yields a mechanism profile of that individual, so the model family can serve as a measurement instrument for studying individual differences.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The 30, 35, and 9 percent usage figures are only as real as the assumption that the eight variants and the 63-feature representation span the true space of cognitive mechanisms; a different but equally plausible model family could re-assign those percentages.
A testable extension would be to manipulate the salience of information gains during planning and check whether the proportion of participants classified as pseudo-reward users moves accordingly; if it does not, the classification may be a modeling artifact rather than a cognitive mechanism.
The persistent model-human gap suggests the missing factor may live outside the current feature representation, for example in one-shot strategy transfer from prior tasks rather than in incremental weight updates over 63 features.
Because termination deliberation participants performed worse, applied systems that prompt users to explicitly evaluate when to stop planning may reduce performance in tasks where continued search is valuable, unless the prompt is paired with a better stopping rule.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my read. The headline is: honest negative result, fragile positive wrapper. The systematic 8-variant comparison is genuinely new—the mechanisms themselves come from the authors' prior work—and the null result—that none of the three mechanisms closes the model-human discovery gap—is clearly stated. That part deserves credit.

The soft spots are all around the individual-differences claims. Participants are assigned to mechanism families by lowest BIC among eight variants, and that assignment is never validated by model recovery. With 120 trials, 63 feature weights, and variants differing by one or two added parameters, BIC can easily tip toward richer variants by chance. So the '70% used at least one mechanism' figure could partly be false positives. The subsequent group comparisons are post hoc: the same click sequences determine both the label and the measured score/planning amount, so a correlation is built in. The BMS family analysis in Table 1a reports exceedance probabilities of 0% and 100%, which looks overconfident for a random-effects procedure; the text doesn't clarify enough to interpret properly. No code or data is shipped, which makes independent verification harder.

The negative result is robust—it's an in-sample comparison, but the gap persists across variants, so the conclusion that the MCRL model family is still missing something is not threatened by the grouping concern.

Who should read this: people working on computational models of planning and metacognitive RL, particularly those who know the authors' prior MCRL work. It's a modest extension, but the honest null is a useful data point. It deserves peer review, but the referees should push for model recovery or out-of-sample validation before the individual-differences claims are taken at face value. I wouldn't cite the positive claims, though I might cite the negative result.

Referee Report

3 major / 5 minor

Summary. The paper extends a metacognitive reinforcement learning (MCRL) model of planning strategy discovery by adding three mechanisms—pseudo-rewards (PR), subjective effort value (SE), and termination deliberation (TD)—yielding eight model variants. Using data from a 120-trial planning experiment, the authors fit each variant to each participant's click sequences via Bayesian optimization and compare fits with BIC and random-effects Bayesian model selection. They report that about 70% of participants are best explained by a variant containing at least one of the mechanisms, that participants best fit by PR or SE variants score higher and plan more, that TD participants score lower and plan less, and that none of the variants closes the gap between simulated and human discovery rates. The central negative result—that the model-human performance gap persists—is stated clearly and appears robust; the positive individual-differences claims, however, rest on interpreting lowest-BIC variant assignment as evidence of mechanism usage.

Significance. If the positive claims were secured, the paper would advance the understanding of individual differences in metacognitive learning during planning strategy discovery, and it would identify concrete model extensions worth pursuing. The paper's strengths are its clear articulation of the model family, use of standard model comparison tools (BMS, BIC, Mann-Whitney U tests), and the provision of OSF links to feature details and regression analyses, which support reproducibility. The negative result—that even with PR, SE, and TD, simulated discovery remains slower than human discovery—is an important boundary condition for MCRL models. However, the central positive claim that individuals 'used' particular mechanisms is not currently supported by the evidence, because the identification strategy is unvalidated and potentially circular. The paper's significance therefore hinges on whether the model-recovery and out-of-sample issues can be resolved.

major comments (3)

[Section 4, Figure 1a]
[Section 4, Table 1]
[Section 4, Figure 1b and text]

minor comments (5)

[Section 3.2 and 3.3]
[Section 4, first paragraph]
[Table 1 caption]
[Figure 1]
[Section 3.4, Pseudo-reward definition]

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the BIC-based 'usage' labels are an operationalization, not a prediction, and the persistent model–human gap is an honest negative result.

full rationale

The paper's derivation chain is a model-fitting and model-selection analysis rather than a prediction from first principles. The headline figure that about 70% of participants 'used at least one' of the added mechanisms is the count of participants whose lowest-BIC variant was not plain Reinforce (244/346 = 69.96%). This is an operationalization of 'usage' via Bayesian model selection, not a circular reduction: the BIC is computed from click-sequence likelihoods, and the 'usage' label is a latent-variable inference from those likelihoods. The grouped comparisons in Table 1 compare these fitted groups on score and planning amount; final score is not part of the fitted likelihood, and planning amount (number of clicks) enters the likelihood only as the length of the observed sequence, so these correlations are not forced by construction. The central negative result—that none of the enhanced variants significantly closed the gap between simulated and human discovery rates—is an in-sample model comparison, but it is not circular because it is a comparison of model fits to the same human data, not a fitted parameter renamed as a prediction. The main threats to the positive individual-differences claims are model-recovery/identifiability and construct validity (the absence of a reported recovery analysis), which are correctness concerns rather than circularity. The model space and mechanisms are imported from the authors' own prior work ([7], [8], [14]), so there is substantial self-citation, but those citations are prior empirical/modeling work with independent behavioral content, and the present analysis fits to human data and reports a non-trivial failure to improve alignment. Accordingly, no circular step is exhibited, and the score reflects only the self-citation density, not a circular derivation.

Assumptions & free parameters 6 free parameters · 6 assumptions · 3 invented entities

The central claims rest on a large set of fitted hyperparameters (alpha, gamma, tau, 63 initial feature weights, plus PR and SE parameters per participant) and on modeling assumptions carried over from the authors' prior work [7], [8], [14]. The three mechanisms are postulates whose only evidence in this paper is in-sample model comparison on 120 trials per participant. TD adds no new scalar parameter but restructures the termination Q-value. The most consequential assumption is that BIC-based grouping reveals which mechanism a participant actually used; if the model space is misspecified, the reported individual differences are artifacts of the fitted model family.

free parameters (6)

learning rate alpha = not reported in text
Core RL update rate fitted per participant via Bayesian optimization (Section 3.3).
discount factor gamma = not reported in text
Fitted per participant (Section 3.3).
inverse temperature tau = not reported in text
Softmax temperature over cognitive operations, fitted per participant (Section 3.3).
63 initial feature weights = not reported in text
Initial strategy parameter vector; Section 3.3 lists all 63 initial feature weights as free hyperparameters, a large number of fitted degrees of freedom.
pseudo-reward weight = not reported in text
Modulates the PR contribution to the learning signal in PR variants, one added free hyperparameter (Section 3.4).
subjective effort value (SE) = not reported in text
Per-participant effort (un)pleasantness added to the reward signal of planning operations in SE variants, one added free hyperparameter (Section 3.4).

assumptions (6)

domain assumption Planning can be represented as search in a meta-level MDP with mental computations, including termination, as actions
Section 3.1; the entire MCRL framework rests on this cognitive formalization of meta-decision-making.
domain assumption Planning strategies are represented by linear weights on 63 features of belief-computation pairs
Section 3.2; if the feature set is incomplete, the model space cannot express the human strategies under study.
domain assumption Click sequences in the planning task faithfully reflect internal planning operations
Section 3.5; the likelihood is computed over individual clicks, equating observable clicks with mental computations.
domain assumption The metacognitive Reinforce policy-gradient update can learn the adaptive strategy given the feature representation
Section 3.3; this capability is carried over from the authors' prior work [8], [9] and not re-derived here.
ad hoc to paper The pseudo-reward, defined as the expected-value improvement between consecutive belief states, is the correct intrinsic signal
Section 3.4; this operationalization is imported from the authors' prior papers [7], [14] and is assumed rather than independently validated.
domain assumption BIC-based grouping by best-fitting variant reveals the mechanism a participant actually used
Section 4; the individual-differences analysis assumes the best-fitting variant corresponds to the mechanism operating in the participant's mind.

invented entities (3)

metacognitive pseudo-reward (PR)
purpose: Intrinsic reward for information-gaining mental transitions, added to the learning signal to accelerate strategy discovery
Model construct defined in Section 3.4 and borrowed from the authors' own prior work [7], [14]; its only evidence in this paper is in-sample model comparison, with no independent behavioral or neural handle.
subjective effort value (SE)
purpose: Per-participant adjustment to the reward of planning operations capturing how pleasant or unpleasant effort is
A free parameter per participant (Section 3.4) motivated by [12]; the paper provides no independent measurement of effort valuation, only fitted values.
termination deliberation (TD)
purpose: Structural mechanism that computes the expected return of the current belief state and assigns it to the termination action after each planning operation
Section 3.4; a postulated mental computation whose only evidence is the fitting and selection of TD model variants in this paper.

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Cite this review

Pith. "Pith review of Individual differences in the cognitive mechanisms of planning strategy discovery." pith.science (2026). https://pith.science/paper/4HCGYSYV

@misc{pith2026250523519,
  author       = {Pith},
  title        = {Pith review of: Individual differences in the cognitive mechanisms of planning strategy discovery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4HCGYSYV}},
  note         = {Machine review of arXiv:2505.23519}
}

read the original abstract

People employ efficient planning strategies. But how are these strategies acquired? Previous research suggests that people can discover new planning strategies through learning from reinforcements, a process known as metacognitive reinforcement learning (MCRL). While prior work has shown that MCRL models can learn new planning strategies and explain more participants' experience-driven discovery better than alternative mechanisms, it also revealed significant individual differences in metacognitive learning. Furthermore, when fitted to human data, these models exhibit a slower rate of strategy discovery than humans. In this study, we investigate whether incorporating cognitive mechanisms that might facilitate human strategy discovery can bring models of MCRL closer to human performance. Specifically, we consider intrinsically generated metacognitive pseudo-rewards, subjective effort valuation, and termination deliberation. Analysis of planning task data shows that a larger proportion of participants used at least one of these mechanisms, with significant individual differences in their usage and varying impacts on strategy discovery. Metacognitive pseudo-rewards, subjective effort valuation, and learning the value of acting without further planning were found to facilitate strategy discovery. While these enhancements provided valuable insights into individual differences and the effect of these mechanisms on strategy discovery, they did not fully close the gap between model and human performance, prompting further exploration of additional factors that people might use to discover new planning strategies.

Figures

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Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Bergstra, D

J. Bergstra, D. Yamins, and D. Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. InICML, pages 115–123. PMLR, 2013

work page 2013

[2] [2]

Boureau, P

Y.-L. Boureau, P . Sokol-Hessner, and N. D. Daw. Deciding how to decide: Self-control and meta-decision making.Trends in cognitive sciences, 19(11):700–710, 2015

work page 2015

[3] [3]

Callaway, S

F. Callaway, S. Gul, P . Krueger, T. L. Griffiths, and F. Lieder. Learning to select computations. InUncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference, 2018

work page 2018

[4] [4]

Callaway, B

F. Callaway, B. van Opheusden, S. Gul, P . Das, P . M. Krueger, F. Lieder, and T. L. Griffiths. Rational use of cognitive resources in human planning.Nature Human Behaviour, pages 1–14, 2022

work page 2022

[5] [5]

T. L. Griffiths, F. Callaway, M. B. Chang, E. Grant, P . M. Krueger, and F. Lieder. Doing more with less: meta-reasoning and meta-learning in humans and machines.Current Opinion in Behavioral Sciences, 29:24–30, 2019

work page 2019

[6] [6]

N. Hay, S. Russell, D. Tolpin, and S. E. Shimony. Selecting computations: Theory and applications.arXiv, 2014

work page 2014

[7] [7]

He and F

R. He and F. Lieder. What are the mechanisms underlying metacognitive learning in the context of planning? InCogSci, vol- ume 45, 2023

work page 2023

[8] [8]

Experience-driven discovery of planning strategies

R. He and F. Lieder. Experience-driven discovery of planning strategies, 2024. URLhttps://arxiv.org/abs/2412.03111

work page Pith review arXiv 2024