PCOMPBIOL-D-26-00444

Decomposing response inhibition: a POMDP model

PLOS Computational Biology

Dear Dr. Wang,

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Jian Liu

Academic Editor

PLOS Computational Biology

Andrea E. Martin

Section Editor

PLOS Computational Biology

Additional Editor Comments:

The manuscript has clear merit. However, some additional clarifications are needed to improve overall readability. Please revise the text accordingly.

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full.

At this stage, the following Authors/Authors require contributions: Wenting Wang, Peter Dayan, and Tobias Kaufmann. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The study proposed a computational framework for modeling human behavior on the Stop Signal Task (SST). The main components are a probabilistic generative model that connects stimuli to subject perception, a probabilistic inference model that models human belief updates, an optimal control algorithm to model subject action, and a simulation-based inference model to infer model parameters given subject trajectory. The framework gives insights on how individual differences on the SST arise from a set of interpretable parameters. The framework shows some predictive power for ADHD scores, indicating potential applications.

I don't have major concerns about this study, but only several minor suggestions that could improve the readability of the manuscript.

The main text is overly long. I understand that due to the complexity of the proposed computational framework, the majority of the text is dedicated to explaining the technical details. Even so, I still believe the main text can be shortened to improve readability. For example, to explain the generative and inference models, there are four subsections explaining the go and stop processes, which is long, given that Figure 2 already gives a good preview of the models. The following sections have the same issue; technical details about the optimal control model and algorithm and the SBI model, should be moved to the appendix.

Line 24: "In such contexts, race models relying on independence assumptions yield biased estimates of the Stop Signal Reaction Time (SSRT)." What is the bias?

The "dependent" and "independent" SST should be stated with more clarity. "Dependent" or "independent" is about the design choice of an SST experiment; is my understanding correct? And the independent design is when there is no signal fusion between "go" and "stop" before the last readout stage of the model? Or is it about a specific SST experimental design? Or both?

Line 83: "We only included participants who met rigorous quality control criteria, including adherence to official ABCD performance standards." The sentence should be changed to better reflect that the criteria are not only about participants but also the experimental setup.

In the section "Computational phenotypes and ADHD score associations," a few things could be clearer. What are the computational phenotypes exactly referring to? I think the authors refer to all six inferred continuous parameters collectively as "phenotypes." Or is it only inverse temperature that defines phenotypes because of its wide spread? Table 4 shows the predictive power of the parameters, but the actual coefficient numbers are hard to interpret. This is arguably the most important result of this study, such that readers would like to know how good the computational model and inferred parameters are at predicting psychiatric measures (ADHD scores). To make the coefficient numbers in Table 4 more intuitive, the authors may 1) add a new plot or modify the existing Figure 11 to show inferred parameters together with ADHD scores, and 2) add a comparison to classic metrics of the SST (e.g., stopping speed) as regressors to predict ADHD scores.

Reviewer #2: This is a technically rigorous manuscript that makes a genuine methodological contribution by combining POMDP modeling with transformer-based simulation inference for large-scale behavioral data. The model is carefully formulated, the inference pipeline is well-validated, and the theoretical properties are comprehensively illustrated. However, the clinical findings are weak and risk being overclaimed, several modeling choices need better justification, and a number of errors and inconsistencies need correction. I recommend minor revision, with particular attention to: (1) tempering the clinical narrative and reporting effect sizes, (2) justifying the fixed/free parameter structure, (3) addressing the stop-parameter identifiability concern, (4) fixing the sample-size discrepancy and broken reference, and (5) providing code for review.

Major Points

1. ADHD effect sizes are missing and likely trivial. Table 4 reports only unstandardized coefficients with Wald SEs. With N = 5,114, statistical significance is nearly guaranteed for any nonzero effect. The authors should report standardized effect sizes (partial R², Cohen's f²) for all ADHD associations. Without these, the reader cannot evaluate the clinical relevance of the findings. The abstract and framing should be adjusted to match the "rather weak" associations (line 570).

2. Fixed vs. free parameter choices need justification. Fixing λ = 0.2 (hazard rate) and r̃ = 1/6 (stop prior) while freeing six other parameters is a consequential decision that is inadequately motivated ("based on previous work [21] and pilot testing," line 362). Fig 9 demonstrates that r̃ profoundly affects behavior. Meanwhile, Yu & Dayan (2005; Ref [20]) showed participants adaptively track stop probability, suggesting λ and r̃ could absorb variance currently attributed to other parameters. A sensitivity analysis varying the fixed values, or a formal model comparison, would strengthen the paper.

3. Stop-parameter recovery is moderate — implications for null ADHD findings. Recovery of δ (r = 0.67) and δ' (r = 0.60) is substantially lower than for go-related parameters. The null ADHD association with stop-signal perceptual parameters could therefore reflect estimation noise rather than a true absence of effect. This should be explicitly discussed, as stop-signal sensitivity deficits are a plausible ADHD mechanism (cf. Weigard et al., 2019; Ref [9]).

4. Non-decision time omission confounds parameter interpretation. Without a non-decision time component, the model may attribute motor/encoding latency to Go precision (χ) or inverse temperature (ϕ). This is especially relevant to the ADHD findings, since ADHD has been associated with longer non-decision times in DDM-based analyses of the same ABCD task (Weigard et al., 2023; Ref [42]). This limitation needs more than a one-sentence acknowledgment.

5. No model comparison is provided. The POMDP is positioned as superior to race models for the dependent ABCD design, but this is asserted rather than demonstrated. Even a qualitative PPC comparison against a fitted RDEX-ABCD or DDM model would substantiate the claim.

Minor Points

- Sample size discrepancy: Main text says N = 5,114; Supplementary Table B says N = 5,144. Which is correct?

- Broken reference: Line 633 contains "[?]" — a missing LaTeX citation for time perception noise.

- Wrong symbol (line 429): Text says "stop belief δ^ν_t" but should be "σ^ν_t"? δ is the stop precision parameter.

- Fig 10 caption: "highlighted (teal) model's adaptive behavior" is missing a word.

- Typo (line 310): "modeling the staircase using in ABCD" → "used in ABCD."

- Inconsistent naming: "Section B.2 of Appendix S1" (line 544) vs. "S1 Appendix" elsewhere.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No:  the authors said that "all code used for the POMDP modeling, parameter inference, and statistical analyses will be made publicly available on the Open Science Framework" (line 693), but no repository link or deposited code is provided with the current submission.

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Reviewer #1: Yes:  Xu Pan

Reviewer #2: No

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Dear Ms. Wang,

We are pleased to inform you that your manuscript 'Decomposing response inhibition: a POMDP model' has been provisionally accepted for publication in PLOS Computational Biology.

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Best regards,

Jian Liu

Academic Editor

PLOS Computational Biology

Andrea E. Martin

Section Editor

PLOS Computational Biology

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The manuscript has been reasonably revised within the current scope of the study and is now suitable for acceptance.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I would like to thank the authors for responding to my comments. In my previous round of review, my suggestions focused primarily on improving the clarity of the presentation, as I had no major concerns about the scientific value of the manuscript. The revised manuscript is significantly more readable, thanks to the restructured sections, improved schematic diagrams, and plots such as Figure 9, which effectively visualizes the core results. I have no further concerns.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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Reviewer #1: No