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REVIEW 3 major objections 5 minor 39 references

Behavioral and Symbolic Fillers as Delay Mitigation for Embodied Conversational Agents in Virtual Reality

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read In a VR job-interview study, a thinking animation with a vocal "Hhm" made a delayed conversational agent feel more human and natural than progress-bar indicators.

desk verdict Useful VR comparison of delay fillers with a real confound between filler type and character identity that the authors acknowledge but cannot fully rule out. read the letter →

arxiv 2508.11781 v1 pith:63QIIXOO submitted 2025-08-15 cs.HC

Denmar Mojan Gonzales , Snehanjali Kalamkar , Sophie Jörg , Jens Grubert This is my paper · ORCID
classification cs.HC
keywords VirtualRealityEmbodiedConversationalAgentDelayMitigationBehavioralFillerProgressIndicatorPresenceHumanlikenessEyeTracking
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

The paper asks whether an embodied conversational agent that needs several seconds to answer—say, because a large language model is computing—should fill the silence with human-like "thinking" behavior or with explicit progress indicators. It argues, on the basis of a 24-person within-subject VR job-interview study, that a multimodal behavioral filler (thinking animations plus a non-lexical "Hhm" vocalization) outperforms both a badge-mounted progress bar and a floating thinking-bubble progress bar. The behavioral filler significantly improved perceived response-time appropriateness, three presence subscales (parasocial interaction, engagement, social realism), humanlikeness, and naturalness, and was preferred by two-thirds of participants. The symbolic progress indicators did not improve expectedness or presence and drew gaze away from the agent's face. If the result generalizes, designers of LLM-powered VR agents should treat behavioral delay mitigation as the default, reserving progress indicators for settings where explicit time information matters more than naturalness.

What carries the argument

The central object is the multimodal behavioral filler: a set of nine "thinking" animations (gaze aversion, filler gestures) combined with a non-lexical verbal filler, "Hhm," spoken with six pitches and occurring every three questions. This filler runs during the delay between the user's question and the agent's response. The comparison conditions are a base idle animation and two symbolic progress indicators (a badge-embedded bar and a floating thinking-bubble bar). The experimental machinery is a within-subject VR job interview with MetaHuman agents, pseudo-random delays of 2, 4, and 8 seconds, questionnaire measures of perceived response time, presence, and agent impression, plus continuo

What would settle it

Run the same study with each filler type paired with every character in a counterbalanced design while keeping delays identical; if the significant advantages of the behavioral filler disappear or shift across character pairings, the effect is character-driven, not filler-driven.

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Extended reading notes

Core claim

The paper reports a within-subject VR study with 24 participants who held simulated job interviews with four embodied conversational agents, one per condition. During scripted response delays of 2, 4, or 8 seconds, the agent either played idle animations (BASE), performed nine thinking animations plus a non-lexical 'Hhm' vocalization with six pitches (BEHAVIORAL), showed a green progress bar embedded in a visitor badge (EMBEDDED), or showed a floating thinking bubble with a progress bar (EXTERNAL). The central claim is that the BEHAVIORAL filler made the perceived response time significantly more appropriate, raised parasocial interaction, engagement, and social realism, and improved humanli

Load-bearing premise

The four filler types were always paired with the same MetaHuman character each, so differences in character appearance or voice, rather than the filler itself, could drive the reported perceptual differences.

Editorial extensions

If this is right

  • If the behavioral filler's effect holds, VR agents backed by LLMs can mask multi-second computation delays with low-cost animations and a brief vocal filler rather than loading UI.
  • Symbolic progress indicators, even embedded ones, did not help users predict when the response would arrive; the gaze data suggests they redirect attention away from the agent's face, which may make response onset feel abrupt.
  • The absence of significant differences between embedded and external progress bars implies that the placement of a symbolic indicator matters less than whether a symbolic indicator is used at all.
  • For real deployments with unpredictable response times, behavioral fillers need an animation state machine that can extend or finish naturally, a design problem the paper explicitly identifies.
  • For longer delays, behavioral fillers may lose their advantage because they provide no estimate of remaining wait time, so symbolic indicators or hybrids may be needed.

Reading between the lines

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

  • The effect may be partly driven by the specific MetaHuman character paired with the behavioral filler, since filler type and character were not crossed in the design; a replication with rotated character-filler pairings would isolate the filler itself.
  • If behavioral fillers work because they signal imminent speech, agents could deliberately time a finishing gesture to the arrival of the response, making even unpredictable delays feel natural.
  • In high-stakes or information-retrieval tasks, symbolic indicators might be more appropriate than the job-interview scenario suggests; the paper itself speculates along these lines, but future work would need to test it directly.
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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

3 major / 5 minor

Summary. This paper reports a within-subject VR experiment (N=24) in which participants interviewed four MetaHuman ECAs while the agent displayed one of four delay-mitigation strategies during a 2/4/8-s thinking pause: BASE idle animation, BEHAVIORAL thinking animations with non-lexical "Hhm" fillers, EMBEDDED progress bar in a badge, and EXTERNAL progress bar in a thinking bubble. The authors measure perceived response-time appropriateness/expectedness, presence subscales, social presence, impression-of-agent scales (humanlikeness, intelligence, likeability, willingness, competence, naturalness, assuredness), gaze on face, and final hiring preference. They report significant benefits of BEHAVIORAL over all other conditions on appropriateness, parasocial interaction, engagement, humanlikeness, and naturalness, and over the two symbolic conditions on social realism and face gaze; 16/24 participants preferred BEHAVIORAL. H2 (symbolic indicators improve expectedness) and H3 (embedded better than external) were not confirmed.

Significance. If the causal interpretation were valid, the result would be practically useful for designers of LLM-based ECAs in VR, showing that naturalistic behavioral fillers can mask response delays more effectively than progress bars. The work extends prior screen-based findings to immersive VR and directly compares behavioral and symbolic strategies. The manuscript is generally clear, uses appropriate nonparametric tests with post-hoc corrections, and makes data and code available, which aids reproducibility. However, the central causal claim is weakened by the full confounding of filler type with character identity, so the study currently supports a conditional rather than a general causal conclusion.

major comments (3)
  1. [§3.3, §5 (Limitations)] The four filler conditions are perfectly confounded with character identity: Section 3.3 states that two male and two female MetaHumans were created, and Section 5 acknowledges that 'each filler type was always displayed with the same character.' Since the dependent variables include humanlikeness, naturalness, parasocial interaction, and hiring preference, the significant BEHAVIORAL advantages reported in Tables 2–3 and the 16/24 preference could in principle be caused by the specific character's appearance, voice, or perceived attractiveness rather than by the filler. Counterbalancing the presentation order does not break this confound because it does not randomize the character–filler mapping. The authors' acknowledgment that appearance 'might have influenced our results' understates the problem: with the current design, the two explanations are indistinguishable. I recommend either a
  2. [§3.1, Table 2] The manuscript reports 13 subjective dependent variables and several eye-gaze measures without any correction for multiple testing across the family of measures. Some of the reported significant effects (e.g., parasocial interaction p=0.014, engagement p=0.016, naturalness p=0.013) would not survive a very conservative Bonferroni correction, although they would survive FDR. The consistency of the pattern mitigates the concern, but the number of 'significant' outcomes should be interpreted with caution. An explicit multiple-comparison strategy or pre-registered analysis plan would strengthen the claims; at minimum, report adjusted p-values or justify the unadjusted family-wise error rate.
  3. [§4.1, §5] The self-reported expectedness measure was not significant (p=0.066), but the authors use the gaze-at-response-onset results to argue that 'participants expected the response more naturally with the BEHAVIORAL fillers.' This is a post hoc interpretation, not a planned test. The gaze measure was not validated as an expectedness measure, and the claim goes beyond the data. Please label this as exploratory and remove it from the summary of hypothesis support.
minor comments (5)
  1. [§4.5] The preference counts are inconsistent: 16 (66.7%) + 3 (12.5%) + 1 (4.2%) + 2 (8.3%) = 22, not 24. Two participants are unaccounted for. Please clarify whether some participants did not state a preference or whether the counts/percentages contain an error.
  2. [Figure 4, Tables 2/4/5] Minor typos: 'Humalikeness' in Figure 4 and 'd f1 = d fe f f ect' in Table headers should read 'Humanlikeness' and 'df1 = df_effect'.
  3. [§3.3] Replication would benefit from more detail about the ECAs' voices (e.g., TTS engine, language/accent, whether all agents used the same voice) and about the animation state machine controlling the transition from filler to response. These details are also relevant to the character-confound concern.
  4. [§4.4] The relative gaze time on the face is reported as a proportion in Table 3 (e.g., 0.66) but as a percentage in Figure 3(g). Please use a consistent scale. Also clarify whether the 'cumulative average' smoothing affects the RGTF values reported.
  5. [§3.1] No sample size justification or power analysis is provided. Given the number of dependent variables, reporting confidence intervals for the main effect sizes would aid interpretation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical user study with outcome measures independent of the experimental manipulations; no claim reduces to its inputs by construction.

full rationale

This paper does not contain a derivation chain to audit for circularity. It reports a within-subjects VR user study (n=24) comparing four delay-mitigation conditions (BASE, BEHAVIORAL, EMBEDDED, EXTERNAL) on perceived response time, presence subscales, agent impression, gaze, and preference. The outcome measures are collected through questionnaires, eye-tracking, and preference choices; they are not computed from the experimental conditions, and no parameter is fitted to the data and then renamed as a prediction. Hypotheses H1-H3 are framed as predictions, but they are tested against independent Likert-scale responses, gaze ratios, and hiring/preference choices, so the conclusions do not reduce to the inputs by definition or construction. The paper's self-citations (e.g., prior work by Kum and Lee, Elfleet and Chollet) are used only as motivation and comparison, not as load-bearing evidence that forces the results. The most substantive weakness is a validity/confound issue explicitly acknowledged by the authors: each filler type was always paired with the same MetaHuman character, so character appearance cannot be excluded as an alternative explanation (Section 5). That is a legitimate experimental-design concern, but it is not circularity as defined here: the measured outcomes are still independent of the manipulation, and the causal claim, even if insecure, is not equivalent to its inputs by construction. Accordingly, no circular steps are identified and the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numerical parameters are fitted; the study's claims rest on domain assumptions about measurement validity, delay representativeness, character equivalence, and response content neutrality. No novel physical or conceptual entities are introduced.

assumptions (4)
  • domain assumption The Temple Presence Inventory (TPI) subscales and Godspeed questionnaire items validly measure presence, humanlikeness, and naturalness in VR ECA interactions.
    The paper uses these instruments as outcome measures (Section 3.1) without validating them in this specific setting; if the instruments do not capture the intended constructs, the significant differences may not reflect the constructs claimed.
  • domain assumption The fixed pseudo-random delays (2, 4, 8 seconds) are representative of realistic LLM response delays in conversational VR agents.
    Section 3 states waiting times following related work [18]; if real delays are longer or more variable, the relative effectiveness of behavioral vs symbolic fillers could change, as the authors acknowledge for longer delays.
  • domain assumption The four MetaHuman characters are perceptually equivalent enough that condition-to-character assignment does not confound results.
    Section 5 notes the limitation that each filler type was always displayed with the same character and that appearance effects cannot be excluded; the reported differences depend on this assumption.
  • domain assumption The scripted interview responses were neutral and similar enough that response quality did not confound the filler effects.
    Section 3 states 'all responses were designed to be neutral, professional, and as similar as possible'; if perceived response quality varied systematically, it could be mistaken for a filler effect.

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Cite this review

Pith. "Pith review of Behavioral and Symbolic Fillers as Delay Mitigation for Embodied Conversational Agents in Virtual Reality." pith.science (2026). https://pith.science/paper/63QIIXOO

@misc{pith2026250811781,
  author       = {Pith},
  title        = {Pith review of: Behavioral and Symbolic Fillers as Delay Mitigation for Embodied Conversational Agents in Virtual Reality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/63QIIXOO}},
  note         = {Machine review of arXiv:2508.11781}
}
read the original abstract

When communicating with embodied conversational agents (ECAs) in virtual reality, there might be delays in the responses of the agents lasting several seconds, for example, due to more extensive computations of the answers when large language models are used. Such delays might lead to unnatural or frustrating interactions. In this paper, we investigate filler types to mitigate these effects and lead to a more positive experience and perception of the agent. In a within-subject study, we asked 24 participants to communicate with ECAs in virtual reality, comparing four strategies displayed during the delays: a multimodal behavioral filler consisting of conversational and gestural fillers, a base condition with only idle motions, and two symbolic indicators with progress bars, one embedded as a badge on the agent, the other one external and visualized as a thinking bubble. Our results indicate that the behavioral filler improved perceived response time, three subscales of presence, humanlikeness, and naturalness. Participants looked away from the face more often when symbolic indicators were displayed, but the visualizations did not lead to a more positive impression of the agent or to increased presence. The majority of participants preferred the behavioral fillers, only 12.5% and 4.2% favored the symbolic embedded and external conditions, respectively.

Figures

Figures reproduced from arXiv: 2508.11781 by the authors.

Figure 1
Figure 1. Visualization of response delay mitigation strategies for embodied conversational agents (ECA): a) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Setup of the virtual environment from the participant’s point [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Box plots for the measures perceived response time, presence, and RGTF: a) Appropriateness. b) Expectedness. c) Social Presence. d) [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Box plots for impression of the agent: a) Humalikeness. b) Perceived Intelligence. c) Likeability. d) Willingness to Interact. e) Competence. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

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