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Explainers' Mental Representations of Explainees' Needs in Everyday Explanations

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Human explainers continuously update a partner model of the listener's knowledge and interests, and explainable AI should do the same.

desk verdict A careful qualitative study of explainers' partner models, with a real method caveat that tempers but doesn't kill the developmental story. read the letter →

arxiv 2411.08514 v1 pith:XX4RKUW3 submitted 2024-11-13 cs.AI

Michael Erol Schaffer , Lutz Terfloth , Carsten Schulte , Heike M. Buhl This is my paper · ORCID
classification cs.AI
keywords mentalrepresentationspartnermodeldualnaturetheorytechnologicalartifactseverydayexplanationsexplainableAIusermodelsqualitativecontentanalysis
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 tries to establish that explainers in everyday explanations build a mental representation of the listener's knowledge and interests that develops over the course of the explanation, and that this development has a regular shape. Early assumptions about the listener's knowledge focus on the artifact's Architecture (how it is built and how it works), while early assumptions about interests center on Relevance (what it is for and why it matters). As the explanation moves into a dialogical phase, both dimensions broaden: assumed knowledge comes to include Relevance and assumed interests come to include Architecture, and vague guesses harden into well-defined beliefs. The authors' larger point is that explainable AI needs a component that tracks these same two dimensions so that machine explanations can be adapted to a user's developing needs.

What carries the argument

The load-bearing object is the 'partner model': the explainer's mental representation of the explainee, here decomposed into a 'technical model' (what the listener knows about the artifact) and a set of assumptions about interests. The paper maps both onto the dual nature theory of technological artifacts, which distinguishes Architecture (observable, measurable features such as pieces, rules, and structure) from Relevance (interpretable aspects such as purpose, meaning, and strategy). The argument is carried by the coded distribution of interview segments across these categories in different explanation phases—before, at the start, middle, and end of the explanation, and after it—showing how the partner model is refined and updated.

What would settle it

An experiment that probes explainers' assumptions in real time—for example, asking them immediately after each explanation move, or tracking where they look and what they say—would settle the question; if live probes show no Architecture-to-both trajectory in knowledge or no Relevance-to-both trajectory in interests, the recalled developmental pattern would be shown to be a post-hoc reconstruction rather than a real-time mental representation.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that explainers' partner models—their assumptions about what the explainee knows and cares about—are dynamic and dual-sided. Coding of pre-, post-, and video recall-interviews from nine explainers who explained the board game Quarto shows that assumed knowledge begins Architecture-centered and later incorporates Relevance, whereas assumed interests begin Relevance-centered and later incorporate Architecture. The patterns move from vague early assumptions to well-defined beliefs, and explainers frequently end the explanation even while believing the explainee still has gaps, treating some missing knowledge as unimportant or as requiring hands-on experience rather than more talk. These findings are offered as the empirical basis for a user-model component in adaptive explainable systems.

Load-bearing premise

The findings rest on the assumption that video recall-interviews give valid, undistorted access to what explainers actually thought during the explanation.

Editorial extensions

If this is right

  • Adaptive explainable systems should maintain a partner model that tracks both the user's knowledge and the user's interests on the Architecture and Relevance dimensions, rather than a single measure of understanding.
  • An explanation system could follow the decision rules the paper derives: start when knowledge is low and interest is high, continue until the demanded knowledge level is reached, change perspective when interests shift, re-explain when knowledge stagnates, and stop when knowledge is high and interest is low.
  • Ending an explanation despite known knowledge gaps is a normal part of human explanation behavior, so XAI systems need not aim for complete coverage of every aspect.
  • The observed sequence implies that explanations of technological artifacts should typically establish Architecture first, because assumptions about Relevance knowledge develop later on that foundation.

Reading between the lines

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

  • If the two-dimensional partner model generalizes beyond the board-game case, a compact state representation—knowledge and interest values on Architecture and Relevance—could be a practical starting point for user models in XAI, though the paper itself stops at qualitative indicators.
  • A natural test the paper does not run is whether explainers whose assumptions follow the observed trajectory produce explanations that explainees find more satisfying; linking the model to explanation quality would strengthen its practical relevance.
  • For digital artifacts with many features, the partner model may need to track which subset of features a user cares about, not just the global balance of Architecture and Relevance, because not every feature will be relevant to every user.
  • If the retrospective-recall worry is real, future work could validate the developmental pattern with live behavioral markers such as the timing of questions or gaze direction, which would also provide XAI with observable signals to monitor.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The manuscript reports a qualitative study of explainers' mental representations of explainees' knowledge and interests during everyday explanations of a technological artifact (the board game Quarto). Nine explainers were interviewed before, during (via video recall), and after their explanations, and the transcripts were analyzed with deductive qualitative content analysis using the dual nature theory (Architecture vs. Relevance) as the coding lens. The central claims are that explainers' assumptions about explainees' knowledge begin centered on Architecture and develop toward both Architecture and Relevance, while assumptions about interests begin centered on Relevance and develop toward both; and that explainers often ended explanations despite perceiving residual knowledge gaps. The authors translate these findings into practical implications for user models in adaptive explainable systems.

Significance. If the developmental claims are valid, the paper provides a useful empirical starting point for XAI user modeling by identifying content dimensions (Architecture and Relevance) and temporal dynamics that an adaptive explainer should track. The study is carefully documented: the coding manual is described, inter-coder reliability is reported (Cohen's kappa = .75), and the analysis is transparent with detailed tables. The paper also explicitly limits its scope to first steps and acknowledges several methodological limitations. However, the central developmental claims currently rest on raw frequency counts that are not directly comparable across phases, and the video recall method measures retrospective reconstructions, not necessarily real-time mental states. The value of the contribution therefore depends on whether these evidentiary gaps can be resolved in revision.

major comments (4)
  1. [Section 4, Tables 3 and 5] The central developmental claims are based on raw code frequencies across phases that are not directly comparable. The number of video recall scenes differs by phase (15 start, 24 middle, 11 end), and the pre-interview probes knowledge and interests about board games in general, while the video recall and post-interviews probe knowledge and interests about Quarto specifically. The shift from 'Architecture-centered' to 'both Architecture and Relevance' may therefore reflect the change in measurement instrument rather than a true change in explainers' assumptions. The authors should either restrict comparisons to like-for-like categories (e.g., only Quarto-specific codes across VR-S, VR-M, VR-E, and Post) or report proportions per scene or per participant, and explicitly discuss the confound between phase and question focus.
  2. [Section 4, Answer to RQ1] The paper states that 'Many EXs weren't aware of this in the explanation itself but realized the knowledge gaps, particularly in the video recall-interview.' This admission directly undermines the claim that the video recall data reflect mental representations that were available to the explainer in real time to guide and adapt the explanation. The paper's framing as 'thereby enabling explainers to react to explainees' needs' is therefore not supported by the evidence. The authors should either reframe the findings as retrospective reconstructions of explainers' assumptions or provide convergent behavioral evidence (e.g., observable adaptation in the explanation itself) to support the real-time interpretation.
  3. [Section 3, Procedure and Section 4, Answer to RQ2] The interview questions differ systematically across phases: the pre-interview asks 'Which aspects of board games does the EE enjoy?' while the video recall asks 'What knowledge needs regarding the game did the EE have in that particular moment?' The paper notes that the phrase 'knowledge needs' was deliberately chosen to elicit richer answers about interests. This procedure confounds the phase being studied with the wording and focus of the elicitation question. The observed shift in interest frequencies (from Relevance in pre-interviews to Architecture in video recall-start) may be an artifact of the question change rather than a genuine developmental change in explainers' assumptions. The authors should analyze the data separately by elicitation question type or otherwise control for this confound.
  4. [Section 4, Table 3] For board-game knowledge, the proportion of Relevance codes is nearly identical in pre- and post-interviews (35/81 = 43.2% vs. 31/73 = 42.5%), while the overall shift toward 'both Architecture and Relevance' is driven entirely by Quarto-specific codes that were not elicited in the pre-interview. This indicates that the developmental finding may be a measurement artifact: the pre-interview simply did not ask about Quarto-specific knowledge. The authors should analyze board-game knowledge and Quarto knowledge separately and temper the abstract's claim that 'the assumed knowledge of explainees in the beginning is centered around Architecture and develops toward knowledge with regard to both Architecture and Relevance.'
minor comments (4)
  1. [Table 2] The percentages in the 'Total' row of Table 2 are inconsistent with the column totals: the table reports 612 (56.98%) and 379 (35.25%) for a total of 991 (100%), but 612/991 = 61.76% and 379/991 = 38.24%. The 56.98% and 35.25% figures appear to be computed with the 83 unallocated segments included in the denominator, which contradicts the stated total of 991. Please correct this presentation.
  2. [Section 4, Answer to RQ1] The abstract states that 'explainers often finished the explanation despite their perception that explainees still had gaps in knowledge,' while the results say 'a few EXs completed their explanations' despite perceived gaps. These two characterizations differ in magnitude; please align the wording.
  3. [Throughout] There are several typographical and formatting issues, including 'T able 1' (Table 1), 'T echnical Model' and 'The T echnical Model' in section headings, and 'segue' (should be 'segue' or 'transition' for clarity). These do not affect the content but should be cleaned up.
  4. [Section 5, Methodological Considerations and Limitations] The paper states that 'the rather small sample size was sufficient for our findings' without providing an explicit saturation argument or a justification in terms of the qualitative method. A brief statement on how saturation was determined, or a clearer acknowledgment that the findings are exploratory, would strengthen the limitations section.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the developmental claims are empirical coding results; the sole author self-citation is motivational, not load-bearing.

full rationale

The paper's central claims (answers to RQ1 and RQ2) are descriptive summaries of coded interview segments reported in Tables 3-6, not outputs of a model fitted to those same data. The dual nature theory supplies the coding categories (Architecture and Relevance), but whether explainers' assumptions shift in the reported way is an empirical finding based on 991 coded segments with intercoder reliability k=.75. The paper contains self-citations to the authors' prior study [49], but that citation is used as background ('In a recent study, the structure of everyday explanations... [49]') and later as a point of comparison ('This process is already known from an observation study'); it is not the source of the new interview-based results. The acknowledged limitations (video recall is retrospective; scene counts differ across phases: 15 start, 24 middle, 11 end; and the 'knowledge needs' prompt was used to elicit interests) are validity concerns about whether the interviews captured real-time mental representations, not circular reductions of the results to the method's assumptions. No equation, fitted parameter, or definition makes a predicted quantity equal to an input by construction. Therefore no circular step is present; score 1 reflects only the minor, non-load-bearing self-citation.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No fitted parameters and no new postulated entities. The 'technical model within partner model' is a conceptual reframing of existing constructs, not an entity with independent falsifiable handles. The main assumptions are the dual nature theory, the validity of video recall, and the reliability of the coding scheme.

assumptions (3)
  • domain assumption Technological artifacts have a dual nature comprising Architecture and Relevance, and both perspectives are needed for understanding.
    Invoked in Section 2 and used as the coding frame for all segments. If this duality is not a meaningful distinction for explainers, the central results lose their structure.
  • domain assumption Video recall-interviews provide valid access to explainers' mental representations during the explanation.
    Central method in Section 3. It relies on retrospective memory reconstruction from selected video scenes, which can introduce bias and post-hoc rationalization.
  • domain assumption The coding categories (knowledge/interests by Architecture/Relevance) are sufficiently exhaustive and reliable for frequency comparisons.
    Coding manual in Section 3 reports k=.75 and double coding, but 83 segments could not be allocated to either Architecture or Relevance, indicating boundary ambiguity.

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Cite this review

Pith. "Pith review of Explainers' Mental Representations of Explainees' Needs in Everyday Explanations." pith.science (2026). https://pith.science/paper/XX4RKUW3

@misc{pith2026241108514,
  author       = {Pith},
  title        = {Pith review of: Explainers' Mental Representations of Explainees' Needs in Everyday Explanations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XX4RKUW3}},
  note         = {Machine review of arXiv:2411.08514}
}
read the original abstract

In explanations, explainers have mental representations of explainees' developing knowledge and shifting interests regarding the explanandum. These mental representations are dynamic in nature and develop over time, thereby enabling explainers to react to explainees' needs by adapting and customizing the explanation. XAI should be able to react to explainees' needs in a similar manner. Therefore, a component that incorporates aspects of explainers' mental representations of explainees is required. In this study, we took first steps by investigating explainers' mental representations in everyday explanations of technological artifacts. According to the dual nature theory, technological artifacts require explanations with two distinct perspectives, namely observable and measurable features addressing "Architecture" or interpretable aspects addressing "Relevance". We conducted extended semi structured pre-, post- and video recall-interviews with explainers (N=9) in the context of an explanation. The transcribed interviews were analyzed utilizing qualitative content analysis. The explainers' answers regarding the explainees' knowledge and interests with regard to the technological artifact emphasized the vagueness of early assumptions of explainers toward strong beliefs in the course of explanations. The assumed knowledge of explainees in the beginning is centered around Architecture and develops toward knowledge with regard to both Architecture and Relevance. In contrast, explainers assumed higher interests in Relevance in the beginning to interests regarding both Architecture and Relevance in the further course of explanations. Further, explainers often finished the explanation despite their perception that explainees still had gaps in knowledge. These findings are transferred into practical implications relevant for user models for adaptive explainable systems.

Figures

Figures reproduced from arXiv: 2411.08514 by the authors.

Figure 1
Figure 1. The Partner Model for Synthesis of Explanations continuation of the explanation when the requisite level of knowledge is not yet reached or interests regarding that aspect were expressed. When interests change, the foci or perspective of explanation might need to be changed as well. Moreover, a re-explanation of certain aspects might be necessary when inter￾ests do not change and knowledge does not increase further.… view at source ↗

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