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REVIEW 4 major objections 5 minor 86 references

Zeitgebers-Based User Time Perception Analysis and Data-Driven Modeling via Transformer in VR

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Underestimating time in VR tracks better user experience, and a brain-signal model can predict the distortion.

desk verdict RQ1/RQ2 empirical results are plausible and useful, but RQ3's deep-learning claim is compromised by a trial-duration confound and a numeric inconsistency. read the letter →

arxiv 2412.08223 v1 pith:4FFF7L2K submitted 2024-12-11 cs.HC

Yi Li , Zengyu Liu , Xiandi Zhu , Ning Xie This is my paper · ORCID
classification cs.HC
keywords virtualrealitytimeperceptionzeitgebersuserexperienceEEGTransformerphysiologicalsignalsRelativeSubjectiveChange
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 argues that how long VR users think a fixed 60-second interval lasted can be systematically shifted by controllable environment cues—task demand, light color, and music tempo—and that the direction of the shift is a usable signal about how the user experienced the scene. It introduces the Relative Subjective Time Change (RSTC) method, which compares each condition against the user's own baseline instead of against physical time, and finds that relatively underestimating time is strongly correlated with higher overall user experience, presence, and engagement. It then claims that a hybrid CNN-Transformer network called TPM-Net can classify time perception as underestimation, acceptable accuracy, or overestimation from multimodal physiological data (EEG, brainwaves, human states, heart rate) plus zeitgeber flags, with reported accuracy near 86% in the text and 83% in the comparison table. If these claims hold, designers could infer user experience from physiological signals without interrupting the experience, and time perception could serve as a proxy in VR therapy and training.

What carries the argument

The two load-bearing devices are the RSTC method and TPM-Net. RSTC replaces raw subjective estimates with the difference $\Delta\hat{t} = \hat{t}_{\text{condition}} - \hat{t}_{\text{baseline}}$ between a condition and that participant's own baseline, so correlations with user-experience deltas remove stable individual differences in time judgment. TPM-Net is a five-stage pipeline: multimodal input (raw EEG; brainwave bands $\alpha$, low $\beta$, high $\beta$, $\theta$, $\gamma$; human states stress, awareness, drowsiness, meditation; heart rate; and a 4-dimensional zeitgeber flag), preprocessing with low-pass filtering and empirical mode decomposition, per-channel 1D CNN temporal encoding, feature fusion into a 128-dimensional embedding, a Transformer for long-range dependencies, and an MLP classifier over three labels (underestimated $t\ge 69$ s, acceptably accurate $51\le t<69$ s, overestimated $t<51$ s). The experiment itself uses an event-triggered prospective design: participants are told to estimate 60 s and press a button when they judge the time has elapsed.

What would settle it

Remove all duration information from TPM-Net's input—truncate or pad every trial to a fixed length, or feed only the first N seconds—and retrain; if accuracy does not fall far below the reported 83-86%, physiological content is doing the work, and if it collapses, the model is mainly classifying trial length.

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Extended reading notes

Core claim

On its own terms, the study establishes that in a 60-second prospective timing task inside VR, participants who performed a spatial rotation task pressed the stop button later than those who did not (mean 83.29 s versus 64.86 s, $p<0.01$), meaning they underestimated the elapsed time; red light and 70 BPM music pushed estimates further toward underestimation relative to baseline, while blue light and 140 BPM music moved them toward overestimation. Using the RSTC baseline-relative measures, the paper reports that the more a participant relatively underestimated time, the better they rated overall user experience, presence, and engagement ($r=0.65$, $r=0.55$, $r=0.52$, all $p<0.001$). It further claims that TPM-Net, a CNN temporal encoder feeding a Transformer over fused EEG, brainwave, human-state, heart-rate, and zeitgeber embeddings, classifies the three time-perception categories with accuracy 86.11%, macro-F1 83.10%, and UAR 79.45% in the text, outperforming naive Bayes, SVM, CNN, and BiLSTM baselines in the table.

Load-bearing premise

The load-bearing assumption enters in Section 3.3: the classification label is defined from the actual trial duration, and the input sequence spans exactly that same duration, so the network may learn from sequence length or padding rather than from EEG/HR patterns.

Editorial extensions

If this is right

  • VR designers should expect time to compress: 71.35% of participants underestimated a 60-second interval overall, rising to 86.42% in the task group.
  • Comparing each user against their own baseline roughly doubles the explained variance in overall user experience (R2 from 0.191 to 0.424), so relative time change is a stronger UX signal than raw time estimation.
  • Relative underestimation is a proxy for presence and engagement: larger relative underestimation accompanies higher presence and engagement scores, while emotion and cognitive load show no significant correlation.
  • TPM-Net can coarsely infer time-perception category from EEG, brainwaves, human states, and heart rate with reported accuracy around 83-86%, suggesting passive physiological monitoring of time perception is possible.

Reading between the lines

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

  • If the accuracy survives removal of trial-duration cues, the same model could run in real time, adapting a VR scene when physiological signals indicate the user is losing track of time.
  • The RSTC baseline-differencing logic could transfer to other subjective measures, making relative self-reports comparable across people who use different absolute scales.
  • Because task presence differed between participant groups, a within-subject replication would test whether the reported task effect on time perception is causal rather than coincidental.
  • A direct extension would pair music tempo with task rhythm or task-object color to see whether task-linked zeitgebers, which the paper finds stronger, can be tuned independently of the task itself.
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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 / 5 minor

Summary. This manuscript presents a between-subject VR experiment (N = 56) that varies three zeitgebers—light color (red/blue/white), music tempo (140 BPM/70 BPM/none), and task factor (spatial rotation task vs. none)—under a prospective 60-s time-estimation task. The authors propose the Relative Subjective Time Change (RSTC) method, which compares a participant's mean subjective time estimate in an experimental condition with their own baseline, to analyze how relative time perception relates to questionnaire-based user experience. They also propose TPM-Net, a CNN-Transformer classifier that takes multimodal physiological time series (EEG, brainwave components, heart rate, human-state signals, and zeitgeber flags) and classifies each trial as underestimated, acceptably accurate, or overestimated. The main reported findings are that the task factor significantly increases underestimation, red light and slow-tempo music further bias toward relative underestimation, relative underestimation strongly correlates with overall user experience, presence, and engagement, and TPM-Net outperforms classical and deep baselines with roughly 83–86% accuracy.

Significance. The strength of the paper is its systematic experimental setup and the RSTC baseline-relative analysis, which offers a practical way to link subjective time distortion to user experience without requiring accurate absolute time judgments; the reported RQ2 correlations are substantial (e.g., r = 0.65 for relative time perception vs. relative overall user experience). However, the RQ3 modeling claim is currently not supported because the classification label is a deterministic threshold function of the trial's actual duration, and the physiological input sequences span exactly that duration, so the network can solve the task from sequence length alone. In addition, the reported accuracy in text differs from Table 5, and several ANOVA statistics in Figure 6 are internally inconsistent. If these issues are resolved, the paper would constitute a meaningful contribution to VR time-perception research; at present the empirical conclusions for RQ1 and RQ2 are plausible but the headline RQ3 result requires substantially more evidence.

major comments (4)
  1. [3.3, 4.3] The labels in the Classification stage are deterministic threshold functions of the trial duration t (underestimated for t ≥ 69 s, acceptably accurate for 51 s ≤ t < 69 s, overestimated for t < 51 s), and the input sequences are physiological and Zei recordings covering exactly that same trial, padded within each batch to a common length. A model that only counts non-padding frames (or otherwise attends to sequence length) can recover the label with errors only within ±3 s of the two boundaries. Table 5 does not include such a length-only baseline, nor any control that removes duration from the input (e.g., fixed-length cropping, using duration as a separate covariate, or randomizing the mapping between duration and labels). Therefore the reported 83.33% accuracy in Table 5 does not establish that EEG, HR, or human-state content carries information about time perception. Please add a duration-only control and report whether TPM-Net still exceeds it; this is essential for the RQ3 conclusion.
  2. [Abstract; §4.3; Table 5] The abstract and §4.3 state that TPM-Net achieved Accuracy = 86.11%, Macro-F1 = 83.10%, UAR = 79.45%, but Table 5 lists Accuracy = 83.33%, Macro-F1 = 74.84%, UAR = 71.32% for the same model. These are different results and must be reconciled; the discrepancy is too large to be a rounding artifact and affects the paper's headline claim. Please state which result corresponds to the reported 85%/15% split and which to the 5-fold cross-validation, and report both consistently.
  3. [§4.1, Figure 6] Several reported F statistics are inconsistent with their accompanying p-values. For example, with degrees of freedom (2, 39), an F value of 1.43 cannot yield p = 0.025 (the critical F at α = 0.05 is about 3.24), and F(2, 39) = 2.34 cannot yield p = 0.046. These appear in the panels for the music-without-task and color-without-task groups, where the claimed significant effects of music tempo and light color on subjective time estimation rest. Please correct the values or the statistics; if the reported p-values are wrong, the corresponding conclusions about which zeitgebers significantly affect time perception may change.
  4. [§3.3, §4.3] The ±15% tolerance interval that defines the three classes is chosen after inspecting the collected data ('STD = 10.25' and 'typical variation observed'), so the class balance (243/194/49) and the attainable classification accuracy are partly produced by a threshold fitted to the same dataset. Please report the classification results for at least two alternative margins (e.g., ±10% and ±20%) and show that the relative ranking of TPM-Net against the baselines is stable. Without this, the reader cannot tell whether the reported accuracy is an artifact of an overly generous or specifically tuned label boundary.
minor comments (5)
  1. [Figure 4] 'Signal prepossess' in the Figure 4 caption (and in the stage description in §3.3) should read 'signal preprocessing'.
  2. [§4.3] The evaluation protocol is described as an 85%/15% split with a 20% validation portion of training data, and also as 5-fold cross-validation; please state which result in Table 5 corresponds to each protocol, since the two procedures are not equivalent.
  3. [§3.2.3] The text refers to 'Groups 1 through 4' but Table 2 numbers the experiment groups 0–4; please harmonize the numbering.
  4. [§4.4] The exclusion of one participant due to cybersickness is described in §4.4, while §3.2.1 reports four exclusions due to protocol/technical issues; please clarify whether the cybersickness participant is included among those four.
  5. [References [25], [85]] References [25] and [85] are patent documents cited with generic titles and no patent numbers; providing the full patent identifiers would allow readers to verify the human-state signal definitions.

Circularity Check

1 steps flagged · score 6.0 of 10

RQ3's classification target is a threshold function of trial duration, and the input sequences have exactly that duration, so TPM-Net's reported accuracy may reflect length/padding rather than physiological content.

  1. self definitional [Section 3.3 (Multimodal input, Signal preprocessing, Classification stage); Table 5]
    "The length of each trial varies depending on the duration taken by participants. ... Finally, we padded the sequences within each batch to ensure consistent sequence lengths before feeding into TPM-Net. ... We categorized the labels into three groups: underestimated (t ≥ 69), acceptably accurate ( 51 ≤ t <69), and overestimated ( t < 51)."

    The label is a deterministic threshold function of the participant's trial duration t (underestimated iff t≥69, overestimated iff t<51). The physiological input sequences are recorded over exactly that same trial, so their unpadded length, or the padding pattern inserted to equalize batches, is t times the sampling rate. Thus the target variable is recoverable from a length/padding feature alone, without reading any EEG, HR, brainwave, human-state, or zeitgeber value. TPM-Net's reported accuracy (83.33% in Table 5; 86.11% in the text) therefore does not establish that multimodal physiological content is being modeled; it may only be detecting the duration that defines the label. This makes the RQ3 prediction reduce to input-length thresholding by construction.

full rationale

The circularity is localized to RQ3. The RQ1 and RQ2 analyses are self-contained: RSTC is simply Δt = t_condition − t_baseline computed from collected estimates, and the correlations are standard Pearson tests; no fitted parameter is renamed as a prediction. The only self-citation ([34]) is used for background on comparing VR and real-world time estimates and is not load-bearing for RSTC or TPM-Net. However, the RQ3 claim that TPM-Net models time perception from multimodal physiological data is undercut by a by-construction coupling: the input sequence length equals the trial duration used to define the three-way label, so the network can classify by counting non-padded frames or detecting the padding pattern. The paper itself states that trial length varies with participant duration and that sequences are padded within each batch, which is the exact mechanism that exposes t to the model. The abstract's 86.11% accuracy versus Table 5's 83.33% is an additional reporting inconsistency, but it is not itself circularity. Because one central predictive result reduces to input-length thresholding by construction, while RQ1, RQ2, and the RSTC correlation retain independent content, the overall circularity score is 6.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The main commitments are experimental and statistical: the prospective timing paradigm is assumed valid, consumer EEG/HR signals are assumed informative about time perception, and the label threshold is derived from the collected data. No new physical entities are postulated.

free parameters (2)
  • Acceptably accurate time window [51,69) s = 51 to 69 seconds (±15% around 60 s)
    Chosen from post-experimental SD=10.25; defines the three classification labels and therefore the reported TPM-Net accuracy.
  • TPM-Net hyperparameters = lr=5e-4, batch=24, weight decay=5e-4, d_model=128, 300 epochs, early stopping patience=40
    Hand-chosen architecture and training settings; no sensitivity analysis is provided, and model weights are fit to the training subset.
assumptions (3)
  • domain assumption The prospective paradigm with a 60 s target yields valid subjective time estimates
    Used in Section 3.1; the paper cites prior work but does not validate it in this VR setup.
  • domain assumption Consumer EEG/HR signals and derived brainwave/human-state metrics contain information about time perception
    Assumed in Sections 2.3 and 3.2.2; supported only by prior literature, not by an internal control.
  • ad hoc to paper The ±15% tolerance interval is an appropriate label boundary
    Section 3.3 sets this from the SD of the collected data; no independent justification or preregistration.

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Cite this review

Pith. "Pith review of Zeitgebers-Based User Time Perception Analysis and Data-Driven Modeling via Transformer in VR." pith.science (2026). https://pith.science/paper/4FFF7L2K

@misc{pith2026241208223,
  author       = {Pith},
  title        = {Pith review of: Zeitgebers-Based User Time Perception Analysis and Data-Driven Modeling via Transformer in VR},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4FFF7L2K}},
  note         = {Machine review of arXiv:2412.08223}
}
read the original abstract

Virtual Reality (VR) creates a highly realistic and controllable simulation environment that can manipulate users' sense of space and time. While the sensation of "losing track of time" is often associated with enjoyable experiences, the link between time perception and user experience in VR and its underlying mechanisms remains largely unexplored. This study investigates how different zeitgebers-light color, music tempo, and task factor-influence time perception. We introduced the Relative Subjective Time Change (RSTC) method to explore the relationship between time perception and user experience. Additionally, we applied a data-driven approach called the Time Perception Modeling Network (TPM-Net), which integrates Convolutional Neural Network (CNN) and Transformer architectures to model time perception based on multimodal physiological and zeitgebers data. With 56 participants in a between-subject experiment, our results show that task factors significantly influence time perception, with red light and slow-tempo music further contributing to time underestimation. The RSTC method reveals that underestimating time in VR is strongly associated with improved user experience, presence, and engagement. Furthermore, TPM-Net shows potential for modeling time perception in VR, enabling inference of relative changes in users' time perception and corresponding changes in user experience. This study provides insights into the relationship between time perception and user experience in VR, with applications in VR-based therapy and specialized training.

Figures

Figures reproduced from arXiv: 2412.08223 by the authors.

Figure 1
Figure 1. Overview of the VR Time Perception Study Framework. The center of the image illustrates the use of different zeitgebers [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Example of the sequence of one trail. Each trail is divided [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 2
Figure 2. Experiment Process. 3.2.4 Data Collection There are three parts of data to be collected: Time Perception. We collected the following three types of time perception data to comprehensively assess participants’ time per￾ception under different experimental conditions: • Subjective time estimation (t): This refers to the time that participants subjectively perceived during the 60s timing task. It is calculated by subtr… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Overview of the data-driven TPM-Net architecture. The model consists of five stages. In the multimodal input stage, the BW Seq [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Temporal encoding module. and global dependencies. CNN is employed for fine-grained feature extraction and transient signal changes in multimodal signals [81], while the Transformer captures long-range dependencies across se￾quences, modeling global relationships in th…
Figure 6
Figure 6. Figure 6: Subjective time perception results with means and data test. (a) results of subjective time estimation with/without task. (b) results of [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Heatmap of correlation between relative subjective time [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.