Pith. sign in

REVIEW 4 major objections 6 minor 31 references

A comparative study of sensory encoding models for human navigation in virtual reality

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that a Bayesian efficient coding model best predicts physiological responses during VR navigation, outperforming fitness-maximizing and LNP models.

desk verdict BEC/FMC evaluation is circular; the reported superiority is an artifact of fitting a model to its own noisy output. read the letter →

arxiv 2501.06698 v2 pith:3OQWBF3U submitted 2025-01-12 cs.HC q-bio.NC

Tangyao Li , Qiyuan Zhan , Yitong Zhu , Bojing Hou , Yuyang Wang This is my paper · ORCID
classification cs.HCq-bio.NC
keywords sensoryencodingBayesianefficientcodingfitnessmaximizingcodelinear-nonlinear-PoissonmodelvirtualrealitynavigationphysiologicalresponsecybersicknessInfomax
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 asks which sensory encoding model best simulates how a person's physiological state changes while navigating in virtual reality, using navigation speed as the input and phasic electrodermal activity as the ground truth. It compares Bayesian Efficient Coding (BEC), Fitness Maximizing Coding (FMC), and the Linear-Nonlinear Poisson (LNP) model by fitting each model's optimized response to an Infomax benchmark, then ranking them by mean squared error across a range of error penalties $p$. The central claim is that BEC generally produces the most accurate predictions, that BEC with an FMC-style loss is slightly more accurate when the error penalty is small, and that LNP is markedly less accurate and more sensitive to $p$. If correct, this gives VR designers a principled way to anticipate discomfort, fatigue, and cognitive workload from motion data alone.

What carries the argument

The central object is the parametric nonlinearity $g_1(x;\theta)=\frac{1}{2}(1+\tanh(\theta_1 x+\theta_2))$, which represents the encoded physiological response to standardized navigation speed. BEC minimizes $\mathrm{Loss}_{\mathrm{BEC}}=\sum_i (y_{\mathrm{noise},i}-g_1(x_{\mathrm{std},i};\theta))^p$, while the FMC variant adds a weight $w(y_{\mathrm{noise},i})=|y_{\mathrm{noise},i}|$ to the same $L^p$ error. The fitting target is $y_{\mathrm{noise},i}=g_1(x_{\mathrm{std},i};\theta)+\varepsilon$ with Gaussian noise $\varepsilon\sim N(0,0.05^2)$. The LNP model instead fits a quadratic rectified nonlinearity $g_2$ under a Poisson process assumption. The benchmark is the Infomax response, defined as the cumulative distribution function of the standardized stimulus, which represents maximum information preservation; mean squared error against this benchmark carries the comparison.

What would settle it

Run the same three models on the same dataset but replace the target $y_{\mathrm{noise},i}=g_1(x_{\mathrm{std},i};\theta)+\varepsilon$ with the participant's measured phasic EDA values, or with another participant's values, and recompute the MSE to the Infomax response; if BEC no longer has the lowest MSE, the reported superiority is an artifact of the self-referential target construction.

Watch

Extended reading notes

Core claim

On the paper's own terms, the Bayesian Efficient Coding framework, which minimizes posterior entropy under a resource constraint, produces optimized responses closest to the Infomax response for a participant navigating in VR, with mean squared error around $0.0025$ to $0.0029$ across $p=0.10$ to $2.00$. Combining BEC with the Fitness Maximizing Code loss slightly lowers the error for $p<0.73$ but slightly raises it for $p\geq 0.73$, which the authors interpret as a trade-off between accuracy and long-term fitness. The LNP model yields much larger and more variable errors, roughly $0.055$ to $0.53$, suggesting that its linear filtering and Poisson spiking assumptions are a poor match for GSR-derived physiological signals. The paper concludes that BEC is the more accurate framework for simulating human navigation behavior in VR.

Load-bearing premise

The noisy physiological target the models are fitted to is generated by adding small Gaussian noise to the same curve the model is trying to learn, so the near-perfect fits for BEC and FMC may reflect self-consistency rather than genuine predictive power.

Editorial extensions

If this is right

  • If BEC is the accurate encoding model, VR systems could predict user discomfort from navigation speed alone, before symptoms become visible.
  • FMC's better small-$p$ performance suggests that when the cost of error is low, fitness-maximizing encoding captures the user's exploration behavior.
  • LNP's unstable performance across $p$ discourages its use for GSR-derived physiological signals in VR.
  • The near-constant MSE for BEC across $p$ means the main result does not hinge on a particular penalty value, simplifying practical deployment.

Reading between the lines

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

  • The quantitative comparison uses one participant, so an immediate extension is to fit each of the 53 participants separately and test whether BEC's advantage persists across individuals.
  • Because the fitting target is a noise-corrupted copy of the same function being optimized, a stronger test would use independently measured phasic EDA as the target; if BEC still wins, the claim would be much more secure.
  • If the claim holds against independent targets, a practical upshot is that speed-only streams from a headset could feed a real-time discomfort and workload estimator.
  • The crossover near $p=0.73$ between BEC and FMC is a testable prediction about how error cost shifts encoding strategy when task urgency or discomfort changes.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper compares Bayesian Efficient Coding (BEC), Fitness Maximizing Coding (FMC), and Linear-Nonlinear Poisson (LNP) models for simulating human navigation behavior in virtual reality. Using standardized navigation speed from one participant in a public VR dataset, the authors optimize each model's response and compute mean squared error (MSE) against the Infomax response curve. The central claim is that BEC generally outperforms the other models, with BEC+FMC competitive at small error penalties. The evaluation is based entirely on MSE to the Infomax theoretical benchmark for a single participant; physiological signals are described but not used as the prediction target.

Significance. If the comparison were valid, the paper would offer a straightforward benchmark of normative models for VR sensory encoding. The manuscript has some strengths: it provides explicit loss functions, uses a public dataset, and reports numbers for all p values. However, the evaluation is circular for BEC and BEC+FMC (Eqs. (1)-(2)), and no physiological or behavioral ground truth is used as the target. Consequently, the reported superiority of BEC is an artifact of fitting a model to its own output. The single-participant analysis further limits any general claim.

major comments (4)
  1. [Section III-A, Eq. (1)] The optimization target y_noise,i is defined as g(xstd,i; θ) + ε, where g is exactly the tanh nonlinearity g1(xstd,i; θ) whose parameters θ are being minimized. For p=2, the unregularized least-squares solution recovers g itself (up to noise), so the low MSE in Table II for BEC is expected by construction and cannot support the claim that BEC 'generally outperforms' the alternatives.
  2. [Section III-B, Eq. (2)] The same circularity applies to the BEC+FMC variant, since the weight w(y_noise,i)=|y_noise,i| is computed from the same self-generated target. The comparison with the LNP model is therefore not like-for-like: LNP is fit to the mean response yi (Eq. (3)) while BEC/FMC are fit to noisy samples of their own output.
  3. [Section IV, Table II] The MSE values are computed against the Infomax response, a theoretical curve derived from the stimulus CDF (Section IV), not against the phasic EDA ground truth claimed in Section III-D. Thus the metric assesses proximity to a normative curve, not predictive accuracy for physiological responses or navigation behavior.
  4. [Section IV] The entire analysis uses data from one participant, and the Discussion's interpretation of that participant's SSQ and gaming experience is anecdotal. No cross-validation, hold-out data, or statistical testing across participants is provided, so the abstract's 'superior predictions in most scenarios' is not supported.
minor comments (6)
  1. [Section III-A] The function g1(xstd,i; θ) is said to represent 'LMC response', but LMC is never defined; presumably it stands for 'large monopolar cell' as in the figures, but the acronym is not introduced in the text.
  2. [Section III-C, Eq. (3)] The variable yi is defined as 'the mean value' without specifying the quantity being averaged; clarify whether it is the mean of the phasic EDA signal or of some other physiological measure.
  3. [Section IV-A] The statement that 'the Infomax response curve being almost identical to the stimulus CDF curve' is a tautology because the Infomax response is defined as the stimulus CDF; this should be acknowledged rather than presented as an empirical finding.
  4. [Throughout] The parameter p is described as an 'error penalty', but in Eqs. (1)-(3) it is the exponent of an Lp loss; calling it a penalty conflates the loss exponent with a regularization weight such as w_penalty used for LNP.
  5. [Section III-B] There is a typo in 'reprsents' for 'represents'; similar typos ('impaire', 'make navigation decisions') appear in Sections II-B and VI.
  6. [Table II] The irregular spacing of p values (0.10, 0.31, 0.52, ...) is unusual; state how these values were chosen and whether they are used identically for all three models.

Circularity Check

3 steps flagged · score 9.0 of 10

BEC/FMC evaluation is circular: in Eqs. (1) and (2) the target y_noise is generated by the same tanh nonlinearity g1 being fitted, so the reported superiority over LNP is an artifact of self-generated data.

  1. self definitional [Section III-A, Eq. (1), bullet definitions of g1 and y_noise]
    "LossBEC(θ,p) = Σ(ynoise,i − g1(xstd,i,θ))^p ... g1(xstd,i; θ) = 1/2·(1 + tanh(θ1·xstd,i + θ2)) is the nonlinearity that represents LMC response with random initialized parameter θ ... ynoise,i = g(xstd; θ) + ϵ is the LMC response with Gaussian noise ϵ ∼ N(0, 0.05^2)."

    The optimization target y_noise is defined, in the paper's own equations, as g(x;θ)+ε, where g is the same tanh LMC nonlinearity g1 whose parameters θ are being fitted; no independent g is defined elsewhere. Minimizing Eq. (1) is therefore a denoising or parameter-recovery exercise, not a prediction of an independently measured physiological or behavioral quantity. For p=2, the optimal g1 is the conditional mean of y_noise, which is exactly the generating function g(x;θ). The tiny BEC MSE values in Table II (≈0.0025, matching ε variance 0.05^2) are the noise floor of this self-generation, not evidence that BEC predicts human navigation.

  2. self definitional [Section III-B, Eq. (2)]
    "LossFMC(θ,p) = Σ(w(ynoise,i)·(ynoise,i−g1(xstd,i,θ))^p), where w(ynoise,i) = |ynoise,i| represents the weight."

    The FMC loss uses the same synthetic y_noise from Eq. (1) both as the target and as the weight. Since y_noise is generated by g(x;θ)+ε and g(·;θ) is exactly the parametric nonlinearity g1 being optimized, minimizing Eq. (2) again recovers the generating function from its own noisy samples. Consequently, the claim that 'the Fitness Maximizing Code framework provides more accurate estimates when the error penalty is small' compares two self-fits, not an empirical prediction of physiological state or navigation behavior.

1 more flagged steps
  1. other [Section IV opening and Section V-A, Table II]
    "The Infomax response has been included to provide a comparable benchmark for reference across the three methods. ... To quantify the difference between the results, we compute the mean squared error (MSE) for each approach as a performance measure (see Table II)."

    The comparison that supports the abstract's conclusion that BEC 'generally outperforms the others' is an MSE against the Infomax response curve, not against held-out physiological or behavioral data. For BEC and FMC, the quantity being evaluated is a fit to self-generated y_noise from Eqs. (1)-(2); only the LNP loss (Eq. 3) is fitted to an empirical mean value y_i. Ranking models by distance to the same Infomax curve therefore stacks a self-fit against a data-fit, making the reported superiority of BEC an artifact of the evaluation setup rather than a comparative test on human navigation data.

full rationale

The paper's central quantitative claim is that BEC 'generally outperforms' FMC and LNP for simulating human navigation in VR. That claim is derived from Table II, but the BEC and FMC targets are constructed by the paper itself: Eq. (1) defines y_noise as g(xstd;θ)+ε, where the only parameterized g(·;θ) defined in the paper is the very tanh nonlinearity g1 being optimized, and Eq. (2) reuses this same synthetic y_noise as both target and weight. Minimizing either loss therefore recovers the generating function from noisy samples of itself; the near-constant BEC MSE values around 0.0025 are consistent with the variance of the added Gaussian noise. The LNP model, by contrast, is fitted to an empirical mean response y_i with an additional penalty term, so the comparison in Table II is not like-for-like. The Infomax benchmark is external to the fitted parameters, but it does not repair the circularity because it is a theoretical curve derived from the stimulus CDF, not an independent measurement of the physiological response that BEC and FMC claim to predict. The single-participant analysis and the acknowledged simplifications in the Limitations section further weaken the empirical claim, but the structural circularity is already present in Eqs. (1) and (2).

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

All fitted parameters are internal to the models; the target in BEC/FMC is generated from the fitted function itself, and the Infomax benchmark is derived from the stimulus distribution rather than from measured physiological responses.

free parameters (4)
  • θ1, θ2 (BEC/FMC nonlinearity parameters) = not reported
    Fitted by minimizing Eq. (1) or (2); because the target y_noise contains the same θ, the optimum is trivially the generating parameter.
  • θ0, θ1, θ2 (LNP nonlinearity parameters) = not reported
    Fitted by minimizing Eq. (3).
  • w_penalty (LNP penalty weight) = not specified
    Chosen by hand; value not given.
  • noise variance σ² = 0.05² = 0.0025
    Fixed ad hoc to generate synthetic targets y_noise.
assumptions (4)
  • domain assumption Sensory systems encode information efficiently (Efficient Coding Hypothesis)
    Invoked in Section II-C and used as the basis for BEC.
  • standard math The Infomax-optimal nonlinearity for a single neuron is the stimulus CDF
    Used as the benchmark 'Infomax response' in Section IV.
  • domain assumption The phasic EDA signal represents the ground-truth physiological state
    Stated in Section III-D but never validated against behavioral outcomes.
  • ad hoc to paper The LMC response can be modeled as a tanh nonlinearity with Gaussian noise
    Eq. (1) defines the target as the model's own output; this assumption makes the fit circular.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A comparative study of sensory encoding models for human navigation in virtual reality." pith.science (2026). https://pith.science/paper/3OQWBF3U

@misc{pith2026250106698,
  author       = {Pith},
  title        = {Pith review of: A comparative study of sensory encoding models for human navigation in virtual reality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3OQWBF3U}},
  note         = {Machine review of arXiv:2501.06698}
}
read the original abstract

In virtual reality applications, users often navigate through virtual environments, but the issue of physiological responses, such as cybersickness, fatigue, and cognitive workload, can disrupt or even halt these activities. Despite its impact, the underlying mechanisms of how the sensory system encodes information in VR remain unclear. In this study, we compare three sensory encoding models, Bayesian Efficient Coding, Fitness Maximizing Coding, and the Linear Nonlinear Poisson model, regarding their ability to simulate human navigation behavior in VR. By incorporating the factor of physiological responses into the models, we find that the Bayesian Efficient Coding model generally outperforms the others. Furthermore, the Fitness Maximizing Code framework provides more accurate estimates when the error penalty is small. Our results suggest that the Bayesian Efficient Coding framework offers superior predictions in most scenarios, providing a better understanding of human navigation behavior in VR environments.

Figures

Figures reproduced from arXiv: 2501.06698 by the authors.

Figure 1
Figure 1. The virtual environment in which the participant carries out the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Cumulative distribution function of the stimulus and the optimized [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 2
Figure 2. Cumulative distribution function of the stimulus and the optimized [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Empirical data and nonlinearity for one participant using the LNP [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages

  1. [1]

    Tracing physical behavior in virtual reality: A narrative review of applications to social psychology,

    Haley E Yaremych and Susan Persky, “Tracing physical behavior in virtual reality: A narrative review of applications to social psychology,” Journal of experimental social psychology , vol. 85, pp. 103845, 2019

    work page 2019

  2. [2]

    Motion sickness,

    JT Reason, “Motion sickness,” 1975

    work page 1975

  3. [3]

    The utility of a virtual reality locomotion interface for studying gait behavior,

    Mohamed A Sheik-Nainar and David B Kaber, “The utility of a virtual reality locomotion interface for studying gait behavior,” Human factors, vol. 49, no. 4, pp. 696–709, 2007

    work page 2007

  4. [4]

    En- hanced cognitive workload evaluation in 3d immersive environments with topsis model,

    Yuyang Wang, Jean-R ´emy Chardonnet, and Fr ´ed´eric Merienne, “En- hanced cognitive workload evaluation in 3d immersive environments with topsis model,” International Journal of Human-Computer Studies , vol. 147, pp. 102572, 2021

    work page 2021

  5. [5]

    Electrodermal responses: what happens in the brain,

    Hugo D Critchley, “Electrodermal responses: what happens in the brain,” The Neuroscientist, vol. 8, no. 2, pp. 132–142, 2002

    work page 2002

  6. [6]

    Neil R Carlson, Physiology of behavior , Pearson Higher Ed, 2012

    work page 2012

  7. [7]

    Curiosity, interest and engagement in technology-pervasive learning environments: A new research agenda,

    Marilyn P Arnone, Ruth V Small, Sarah A Chauncey, and H Patricia McKenna, “Curiosity, interest and engagement in technology-pervasive learning environments: A new research agenda,”Educational Technology Research and Development , vol. 59, pp. 181–198, 2011

    work page 2011

  8. [8]

    Efficacy of virtual reality in pedestrian safety research,

    Shuchisnigdha Deb, Daniel W Carruth, Richard Sween, Lesley Straw- derman, and Teena M Garrison, “Efficacy of virtual reality in pedestrian safety research,” Applied ergonomics, vol. 65, pp. 449–460, 2017

    work page 2017

Show all 31 references
  1. [9]

    Applicability of virtual reality systems for evaluating pedestrians’ perception and behavior,

    Miho Iryo-Asano, Yu Hasegawa, and Charitha Dias, “Applicability of virtual reality systems for evaluating pedestrians’ perception and behavior,” Transportation research procedia, vol. 34, pp. 67–74, 2018

    2018

  2. [10]

    Do we need to walk for effective virtual reality navigation? physical rotations alone may suffice,

    Bernhard E Riecke, Bobby Bodenheimer, Timothy P McNamara, Betsy Williams, Peng Peng, and Daniel Feuereissen, “Do we need to walk for effective virtual reality navigation? physical rotations alone may suffice,” in International Conference on Spatial Cognition . Springer, 2010, ...

    2010

  3. [11]

    Measuring the effect of gaming experience on virtual environment navigation tasks,

    Shamus P. Smith and Sam Du’Mont, “Measuring the effect of gaming experience on virtual environment navigation tasks,” in 2009 IEEE Symposium on 3D User Interfaces . 2009, pp. 3–10, IEEE

    2009

  4. [12]

    Comparative eval- uation of the effects of motion control on cybersickness in immersive virtual environments,

    Roshan Venkatakrishnan, Rohith Venkatakrishnan, Ayush Bhargava, Kathryn Lucaites, Hannah Solini, Matias V olonte, Andrew Robb, Sabar- ish V Babu, Wen-Chieh Lin, and Yun-Xuan Lin, “Comparative eval- uation of the effects of motion control on cybersickness in immersive virtual e...

    2020

  5. [13]

    A deep cybersickness predictor through kinematic data with encoded physiological representation,

    Ruichen Li, Yuyang Wang, Handi Yin, Jean-R ´emy Chardonnet, and Pan Hui, “A deep cybersickness predictor through kinematic data with encoded physiological representation,” in 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) . IEEE, 2023, pp. 1132–1141

    2023

  6. [14]

    Emotion recognition from physiological signal analysis: A review,

    Maria Egger, Matthias Ley, and Sten Hanke, “Emotion recognition from physiological signal analysis: A review,” Electronic Notes in Theoretical Computer Science, vol. 343, pp. 35–55, 2019

    2019

  7. [15]

    Stress detection in computer users through non-invasive monitoring of physiological signals,

    Jing Zhai and ARMANDO Barreto, “Stress detection in computer users through non-invasive monitoring of physiological signals,” Blood, vol. 5, no. 0, 2008

    2008

  8. [16]

    It feels real: physiological responses to a stressful virtual reality environment and its impact on working memory,

    Marieke AG Martens, Angus Antley, Daniel Freeman, Mel Slater, Paul J Harrison, and Elizabeth M Tunbridge, “It feels real: physiological responses to a stressful virtual reality environment and its impact on working memory,” Journal of Psychopharmacology, vol. 33, no. 10, pp. 1...

    2019

  9. [17]

    Separating individual skin conductance responses in a short interstimulus-interval paradigm,

    David M Alexander, Chris Trengove, P Johnston, Tim Cooper, JP Au- gust, and Evian Gordon, “Separating individual skin conductance responses in a short interstimulus-interval paradigm,” Journal of neuroscience methods, vol. 146, no. 1, pp. 116–123, 2005

    2005

  10. [18]

    Factors associated with virtual reality sickness in head-mounted displays: A systematic review and meta-analysis,

    Dimitrios Saredakis, Ancret Szpak, Brandon Birckhead, Hannah A. D. Keage, Albert Rizzo, and Tobias Loetscher, “Factors associated with virtual reality sickness in head-mounted displays: A systematic review and meta-analysis,” Frontiers in Human Neuroscience , vol. 14, 2020

    2020

  11. [19]

    Performance and autonomic responses during motion sickness,

    Joakim Dahlman, Anna Sj ¨ors Dahlman, Johan Lindstr ¨om, Torbj ¨orn Ledin, and Torbjorn Falkmer, “Performance and autonomic responses during motion sickness,” Human factors, vol. 51, pp. 56–66, 03 2009

    2009

  12. [20]

    Estimating cognitive work- load in an interactive virtual reality environment using eeg,

    Christoph Tremmel, Christian Herff, Tetsuya Sato, Krzysztof Rechow- icz, Yusuke Yamani, and Dean J Krusienski, “Estimating cognitive work- load in an interactive virtual reality environment using eeg,” Frontiers in human neuroscience , vol. 13, pp. 401, 2019

    2019

  13. [21]

    Possible principles underlying the transforma- tion of sensory messages,

    Horace B Barlow et al., “Possible principles underlying the transforma- tion of sensory messages,” Sensory communication, vol. 1, no. 01, pp. 217–233, 1961

    1961

  14. [22]

    Self-organization in a perceptual network,

    Ralph Linsker, “Self-organization in a perceptual network,” Computer, vol. 21, no. 3, pp. 105–117, 1988

    1988

  15. [23]

    Bayesian efficient coding,

    Il Memming Park and Jonathan W Pillow, “Bayesian efficient coding,” BioRxiv, p. 178418, 2017

    2017

  16. [24]

    Sensory perception relies on fitness- maximizing codes,

    Jonathan Schaffner, Sherry Dongqi Bao, Philippe N Tobler, Todd A Hare, and Rafael Polania, “Sensory perception relies on fitness- maximizing codes,” Nature Human Behaviour , vol. 7, no. 7, pp. 1135– 1151, 2023

    2023

  17. [25]

    A generalization of bayesian inference,

    Arthur P Dempster, “A generalization of bayesian inference,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 30, no. 2, pp. 205–232, 1968

    1968

  18. [26]

    Efficient coding,

    BM Oliver, “Efficient coding,” The Bell System Technical Journal , vol. 31, no. 4, pp. 724–750, 1952

    1952

  19. [27]

    Characterization of neural responses with stochastic stimuli,

    Eero P Simoncelli, Liam Paninski, Jonathan Pillow, Odelia Schwartz, et al., “Characterization of neural responses with stochastic stimuli,” The cognitive neurosciences , vol. 3, no. 327-338, pp. 1, 2004

    2004

  20. [28]

    Linear-nonlinear-poisson models of primate choice dynamics,

    Greg S Corrado, Leo P Sugrue, H Sebastian Seung, and William T New- some, “Linear-nonlinear-poisson models of primate choice dynamics,” Journal of the experimental analysis of behavior , vol. 84, no. 3, pp. 581–617, 2005

    2005

  21. [29]

    Dataset for predicting cybersickness from a virtual navigation task,

    Yuyang Wang, Ruichen Li, Jean-R ´emy Chardonnet, and Pan Hui, “Dataset for predicting cybersickness from a virtual navigation task,” arXiv preprint arXiv:2303.13527 , 2023

    2023 arXiv

  22. [30]

    A review of cybersickness in head-mounted displays: raising attention to individual susceptibility,

    Nana Tian, Phil Lopes, and Ronan Boulic, “A review of cybersickness in head-mounted displays: raising attention to individual susceptibility,” Virtual Reality, vol. 26, no. 4, pp. 1409–1441, 2022

    2022

  23. [31]

    Using fuzzy logic to involve individual differences for predicting cybersickness during vr navigation,

    Yuyang Wang, Jean-R ´emy Chardonnet, Fr ´ed´eric Merienne, and Jivka Ovtcharova, “Using fuzzy logic to involve individual differences for predicting cybersickness during vr navigation,” in 2021 IEEE Virtual Reality and 3D User Interfaces (VR) . IEEE, 2021, pp. 373–381

    2021

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.