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REVIEW 2 major objections 2 minor 67 references

To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making

T0 review · 2 major / 2 minor · reviewed 2026-05-15 · grok-4.3

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Pith's one-line read Cognitive forcing interventions reduce overreliance on incorrect AI suggestions by prompting deeper analysis.

desk verdict Cognitive forcing cuts overreliance more than plain explanations but without process measures to confirm deeper thinking and with clear usability costs. read the letter →

arxiv 2102.09692 v1 pith:2LJHODZQ submitted 2021-02-19 cs.HC cs.AI

Zana Buçinca , Maja Barbara Malaya , Krzysztof Z. Gajos This is my paper · ORCID
classification cs.HCcs.AI
keywords cognitiveforcingoverrelianceexplainableAIAI-assisteddecision-makingdual-processtheoryhuman-AIinteractionneedforcognitiontrustin
checked against Foundation.LawOfExistenceFoundation.DiscretenessForcing
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

People often accept AI recommendations even when wrong because they apply general heuristics instead of analyzing each case and its explanation. The paper tests three cognitive forcing designs that require users to engage analytically with the AI output before deciding. In an experiment with 199 participants, these designs cut overreliance compared with standard explainable AI approaches. The designs that worked best received the lowest user satisfaction ratings, and their benefits were larger for participants who score high on Need for Cognition. The work therefore shows that explainable AI success depends on whether users are motivated to think through the provided information.

What carries the argument

Cognitive forcing interventions: interface designs that require users to perform additional analytical steps with the AI explanation before accepting or rejecting the suggestion.

What would settle it

An experiment that measures actual analytical processing (for example via eye-tracking or think-aloud protocols) and finds no increase in depth of engagement despite the forcing designs.

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Extended reading notes

Core claim

Cognitive forcing functions compel people to engage more thoughtfully with AI-generated explanations rather than relying on heuristics, and this engagement significantly reduces overreliance on wrong AI suggestions relative to simple explainable AI baselines, although it lowers subjective satisfaction and benefits people higher in Need for Cognition more.

Load-bearing premise

The three interventions actually compel deeper analytical thinking rather than simply adding friction or prompting other behavioral changes.

Editorial extensions

If this is right

  • Cognitive forcing can be used to lower acceptance of erroneous AI advice in decision tasks.
  • Any reduction in overreliance comes with lower subjective ratings of the system.
  • The benefit of forcing is moderated by individual differences in motivation to think effortfully.
  • Explainable AI solutions will not work equally well for all users without accounting for cognitive motivation.

Reading between the lines

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

  • Forcing mechanisms might transfer to high-stakes domains such as medical or financial decisions where overreliance carries larger costs.
  • Designers could explore milder versions of forcing that preserve user satisfaction while still increasing analysis.
  • Personalizing the level of forcing based on a user's measured Need for Cognition could improve both effectiveness and acceptance.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript reports results from a controlled experiment (N=199) comparing three cognitive forcing interventions—designed to promote analytical engagement with AI explanations per dual-process theory—to two simple explainable-AI baselines and a no-AI condition. It claims that the forcing designs significantly reduce overreliance on incorrect AI recommendations, albeit with lower subjective ratings, and that the benefit is moderated by Need for Cognition such that higher-NFC participants gain more from the interventions.

Significance. If the core empirical result holds, the work supplies concrete design evidence that cognitive forcing can mitigate overreliance in AI-assisted decisions, documents a satisfaction trade-off, and identifies cognitive motivation as a moderator relevant to equitable XAI deployment.

major comments (2)
  1. [Methods and Results sections] The central interpretation—that reduced overreliance results from compelled deeper analytical processing rather than non-analytic mechanisms such as added friction—is load-bearing for the dual-process framing, yet the design reports only outcome metrics (overreliance rates) without direct process measures (response latencies on explanations, eye-tracking dwell times, or comprehension probes of the AI rationale). This leaves the mechanism unverified.
  2. [Results section] The moderation analysis by Need for Cognition is presented as evidence of differential benefit, but the manuscript does not report the full regression model (including interaction term, covariates, and effect-size details) or power calculations for the subgroup comparisons, making it difficult to assess whether the reported average benefit for higher-NFC participants is robust.
minor comments (2)
  1. [Abstract] The abstract states the sample size and key comparisons but omits the decision task domain and the precise operationalization of overreliance; adding one sentence would improve standalone readability.
  2. [Results] Subjective rating scales are mentioned but the exact items, anchors, and reliability statistics are not tabulated; a supplementary table would clarify the reported trade-off.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below, indicating planned revisions to improve clarity, transparency, and completeness of the manuscript.

read point-by-point responses

  1. Referee: [Methods and Results sections] The central interpretation—that reduced overreliance results from compelled deeper analytical processing rather than non-analytic mechanisms such as added friction—is load-bearing for the dual-process framing, yet the design reports only outcome metrics (overreliance rates) without direct process measures (response latencies on explanations, eye-tracking dwell times, or comprehension probes of the AI rationale). This leaves the mechanism unverified.

    Authors: We agree that the absence of direct process measures (e.g., response latencies, eye-tracking, or comprehension probes) leaves the precise cognitive mechanism somewhat inferential rather than directly verified. Our study was designed to evaluate behavioral outcomes of the forcing interventions, which were constructed to interrupt heuristic reliance and require explicit engagement with the AI rationale, consistent with dual-process theory and prior medical decision-making research. We cannot rule out non-analytic factors such as added friction with the current data. In revision we will (1) expand the Discussion to explicitly address alternative mechanisms, (2) add a dedicated Limitations subsection noting the lack of process-tracing data, and (3) propose future studies that incorporate such measures. This is a partial revision focused on improved interpretation and transparency. revision: partial

  2. Referee: [Results section] The moderation analysis by Need for Cognition is presented as evidence of differential benefit, but the manuscript does not report the full regression model (including interaction term, covariates, and effect-size details) or power calculations for the subgroup comparisons, making it difficult to assess whether the reported average benefit for higher-NFC participants is robust.

    Authors: We thank the referee for highlighting this reporting gap. The original manuscript summarized the NFC moderation but omitted the full model specification. In the revised version we will include the complete regression results: the full model equation with the condition × NFC interaction term, all covariates, coefficient estimates with confidence intervals, effect-size metrics, and any available power or robustness checks for the subgroup analyses. This will allow readers to evaluate the strength and stability of the moderation finding directly. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity in this empirical HCI study

full rationale

This paper reports results from a controlled experiment (N=199) comparing three cognitive forcing interventions to explainable AI baselines and a no-AI condition. All central claims—reduced overreliance, subjective rating trade-offs, and moderation by Need for Cognition—are direct empirical outcome measures from participant decisions and self-reports. The work draws on dual-process theory for intervention design but presents no equations, fitted parameters renamed as predictions, self-citation chains, or uniqueness theorems that reduce any result to its own inputs by construction. The derivation chain is self-contained experimental reporting with no load-bearing circular steps.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the dual-process theory of cognition as a domain assumption and on the validity of the experimental measures of overreliance and Need for Cognition; no free parameters or invented entities are introduced.

assumptions (1)
  • domain assumption Dual-process theory of cognition: people rarely engage analytically with each individual AI recommendation and instead develop general heuristics
    Invoked to justify why explanations alone fail and why forcing functions are needed

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Cite this review

Pith. "Pith review of To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making." pith.science (2026). https://pith.science/paper/2LJHODZQ

@misc{pith2026210209692,
  author       = {Pith},
  title        = {Pith review of: To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2LJHODZQ}},
  note         = {Machine review of arXiv:2102.09692}
}
read the original abstract

People supported by AI-powered decision support tools frequently overrely on the AI: they accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the AI decisions does not appear to reduce the overreliance and some studies suggest that it might even increase it. Informed by the dual-process theory of cognition, we posit that people rarely engage analytically with each individual AI recommendation and explanation, and instead develop general heuristics about whether and when to follow the AI suggestions. Building on prior research on medical decision-making, we designed three cognitive forcing interventions to compel people to engage more thoughtfully with the AI-generated explanations. We conducted an experiment (N=199), in which we compared our three cognitive forcing designs to two simple explainable AI approaches and to a no-AI baseline. The results demonstrate that cognitive forcing significantly reduced overreliance compared to the simple explainable AI approaches. However, there was a trade-off: people assigned the least favorable subjective ratings to the designs that reduced the overreliance the most. To audit our work for intervention-generated inequalities, we investigated whether our interventions benefited equally people with different levels of Need for Cognition (i.e., motivation to engage in effortful mental activities). Our results show that, on average, cognitive forcing interventions benefited participants higher in Need for Cognition more. Our research suggests that human cognitive motivation moderates the effectiveness of explainable AI solutions.

Discussion (0). Continue with ORCID to comment.

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