Pith. sign in

REVIEW 4 major objections 2 minor 117 references

Cross-Cultural Value Attribution in Large Vision-Language Models

T0 review · 4 major / 2 minor · reviewed 2026-07-12 · grok-4.5

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

Pith's one-line read LVLMs invert the human link between socioeconomic status and Authority and override cultural cues for Middle Eastern faces.

desk verdict We only have the abstract for the LVLM cultural-bias paper; the cached full text is a different manuscript, so the three claimed bias patterns stay unauditable. read the letter →

arxiv 2604.09945 v2 pith:UV5JZQBY submitted 2026-04-10 cs.CV cs.AIcs.CL

Phillip Howard , Xin Su , Kathleen C. Fraser This is my paper · ORCID
classification cs.CVcs.AIcs.CL
keywords largevision-languagemodelsculturalbiasvalueattributioncounterfactualimagesMoralFoundationsTheoryWorldValuesSurveysocioeconomicstatusfairness
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

Large vision-language models are widely used, yet little is known about how the cultural context shown in an image shapes the moral, ethical, and political values they attribute to a person. This paper tests that question with counterfactual image sets that hold the same individual fixed while changing religion, nationality, or socioeconomic cues. Across 4.8 million generations from nine architecturally different models, three bias patterns appear repeatedly: the models reverse the socioeconomic-status-to-Authority relationship found in the World Values Survey, and two race-conditional failures let Middle Eastern appearance override the cultural context the image is meant to convey. Ablations show the SES–Authority inversion is strengthened by image conditioning and survives changes in model size. The work therefore argues that cultural stereotypes in LVLMs are systematic, measurable against human survey baselines, and not easily removed by scale alone.

What carries the argument

Counterfactual image sets that depict the same person under different cultural contexts, paired with a grounding analysis that compares LVLM cross-context variation to two large human surveys (MFQ-2 and WVS Wave 7).

What would settle it

Re-run the same counterfactual pipeline with human raters on the identical image sets; if humans do not show the SES–Authority inversion or the Middle-Eastern override, or if controlled re-edits that remove stereotype-laden props eliminate the model patterns, the central bias claims fail.

Watch

Extended reading notes

Core claim

Across 4.8 million generations from nine diverse LVLMs, three bias patterns replicate: an inversion of the socioeconomic-status-to-Authority relationship documented in the World Values Survey, plus two race-conditional failures in which Middle Eastern appearance overrides cultural-context cues. The SES–Authority inversion is amplified by image conditioning and persists across model sizes.

Load-bearing premise

The claim rests on the premise that editing cultural cues on the same face cleanly isolates culture and that survey aggregates are the right human reference for judging model stereotypes.

Editorial extensions

If this is right

  • Cultural-context audits of LVLMs should treat socioeconomic and Middle-Eastern depictions as high-priority failure modes rather than edge cases.
  • Scaling model size alone is unlikely to remove the SES–Authority inversion once image conditioning is present.
  • Grounding LVLM value judgments against MFQ-2 and WVS-style surveys becomes a reusable evaluation template for other cultural dimensions.
  • Deployments that generate moral or political inferences from portraits will systematically mis-attribute Authority and related values under socioeconomic or Middle-Eastern visual cues.

Reading between the lines

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

  • If the inversion is image-amplified, text-only moral probes may understate the cultural bias present in multimodal pipelines.
  • The Middle-Eastern override suggests race can act as a hard prior that collapses intended cultural counterfactuals, a pattern worth testing for other racialized groups.
  • Survey-grounded metrics could be turned into training-time regularizers that penalize cross-context value shifts that diverge from human baselines.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 2 minor

Summary. From the supplied abstract, the paper claims that large vision-language models systematically misattribute moral, ethical, and political values when cultural context in images is varied. Using counterfactual image sets (same person, different cultural contexts) and a multi-dimensional evaluation that pairs Moral Foundations Theory, lexical, and value-sensitivity analyses with a novel grounding comparison to MFQ-2 and WVS Wave 7, the authors report three bias patterns that replicate across nine architecturally diverse LVLMs and 4.8 million generations: an inversion of the socioeconomic-status-to-Authority relationship relative to WVS, and two race-conditional failures that override cultural cues for Middle Eastern depictions. Ablations are said to show that the SES–Authority inversion is amplified by image conditioning and persists across model sizes.

Significance. If the reported patterns are real, well-controlled, and correctly grounded against human surveys, the work would be a substantial contribution to multimodal fairness: it moves beyond standard demographic stereotype probes to culture-conditioned value attribution, introduces an external-survey grounding design that is anti-circular in principle, and claims scale (nine models, millions of generations, size and image-conditioning ablations). Those strengths cannot be credited as established results until the actual methods, controls, and statistics are available for audit.

major comments (4)
  1. The review package does not contain the manuscript for arXiv:2604.09945. The full text provided under CACHEABLE PAPER SOURCE CONTEXT is an unrelated paper (Vestibular reservoir computing, arXiv:2604.09943). Consequently none of the central claims—counterfactual construction, prompting/decoding, mapping of free text to MFT/Authority/survey axes, quantitative grounding to MFQ-2 and WVS Wave 7, or the image-conditioning and size ablations—can be checked against sections, equations, figures, or tables. A load-bearing technical review is not possible on the abstract alone.
  2. Abstract-level load-bearing premise: the claim that counterfactual image sets isolate 'cultural context' while holding the person fixed is essential to interpreting cross-context variation as cultural stereotype rather than visual confound (clothing, props, lighting, pose, religious markers). Without the construction pipeline, edit protocol, and confound checks, the three reported bias patterns cannot be validated as culture-driven.
  3. Abstract-level load-bearing premise: the 'novel grounding analysis' treats MFQ-2 and WVS Wave 7 aggregates as the human reference for labeling LVLM cross-context variation as bias (including the SES–Authority inversion). The alignment metric, unit of analysis, aggregation over free-text generations, and multiple-testing treatment are unspecified in available materials; residual definitional risk (any survey mismatch labeled bias) cannot be assessed.
  4. Scale claims (4.8M generations, nine models, replication of three patterns, image-conditioning amplification, persistence across sizes) are methodologically serious if true, but free parameters listed in the abstract—model selection, image pipeline, prompting protocol, and output-to-axis mapping—remain unauditable. Until the correct manuscript is supplied, these cannot support a soundness judgment.
minor comments (2)
  1. Title/abstract arXiv id mismatch risk: the package labels 2604.09945 (cs.CV, LVLM cultural value attribution) while the body text is 2604.09943 (cs.LG, vestibular RC). Editorial production should confirm the correct PDF is attached before any further review cycle.
  2. Abstract alone does not name the nine LVLMs, the exact cultural axes (religion/nationality/SES operationalization), or whether generations are open-ended vs forced-choice; these should be stated early once the correct manuscript is provided.

Circularity Check

0 steps flagged · score 0.0 of 10

No structural circularity in available claims: LVLM bias patterns are framed as empirical comparisons to external surveys (MFQ-2, WVS), not forced by definition or self-citation.

full rationale

Only the abstract of arXiv:2604.09945 is available for the claimed paper; the cached full manuscript is an unrelated work (vestibular reservoir computing). From the abstract alone, the load-bearing chain is: counterfactual image sets (same person, varied cultural context) → multi-dimensional LVLM value judgments → descriptive analyses plus grounding of cross-context variation against two large-scale external human surveys (MFQ-2 and WVS Wave 7) → three reported bias patterns (SES–Authority inversion vs WVS; two race-conditional Middle-Eastern overrides), with ablations on image conditioning and model size. None of these steps reduces by construction to its inputs: the human surveys are independent external benchmarks, not fitted parameters of the models under test; the counterfactual design is an experimental control, not a definition of the outcome; and the reported patterns are empirical findings across 4.8M generations and nine models, not tautologies. Residual definitional risk (labeling survey mismatch as 'bias') is a framing choice, not circular derivation. Score 0; steps empty. Full-text audit of image construction, prompts, aggregation, and grounding metrics remains impossible with the wrong manuscript cached.

Assumptions & free parameters 4 free parameters · 4 assumptions · 1 invented entities

Abstract-only review of 2604.09945. Load-bearing premises are methodological: counterfactual cultural images isolate culture; MFT categories and survey aggregates are valid human ground truth; cross-context output variation measures value attribution bias. Free parameters (prompts, image generators, model choices, thresholds) are implied but not quantified in the abstract. No invented physical entities; the 'framework' is an evaluation construct.

free parameters (4)
  • Choice of nine LVLMs and sizes
    Model sample defines which biases 'replicate across architecturally diverse models'; selection criteria not in abstract.
  • Counterfactual image construction pipeline
    How cultural context is edited (dress, symbols, SES markers) can inject or remove stereotypes; abstract gives no generator or controls.
  • Prompting / generation protocol for value judgments
    Wording of moral/ethical/political queries and decoding settings can dominate measured 'values'.
  • Mapping from free-text outputs to MFT / Authority / survey axes
    Categorization and lexical rules are free design choices that define the reported inversion and overrides.
assumptions (4)
  • domain assumption Cross-context variation in LVLM value judgments, when it diverges from MFQ-2/WVS patterns, constitutes cultural stereotype bias.
    Core interpretive axiom of the grounding analysis; stated in abstract framing.
  • domain assumption Counterfactual images of the same person across cultural contexts hold identity fixed while varying only cultural cues.
    Required for causal attribution of judgment shifts to culture rather than identity confounds.
  • domain assumption Moral Foundations Theory categories and WVS Authority-related items are appropriate axes for comparing models to humans.
    Choice of theoretical instruments; not derived in abstract.
  • ad hoc to paper 4.8M generations across nine models suffice to claim replication of three bias patterns.
    Scale claim without visible statistical protocol in abstract.
invented entities (1)
  • Evaluation framework pairing descriptive MFT/lexical/value-sensitivity analyses with survey grounding (MFQ-2, WVS Wave 7)
    purpose: Measure and interpret cultural value attribution shifts in LVLMs against human baselines.
    Methodological construct introduced for this study; independent evidence would be reuse/validation by others, not shown here.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Cross-Cultural Value Attribution in Large Vision-Language Models." pith.science (2026). https://pith.science/paper/UV5JZQBY

@misc{pith2026260409945,
  author       = {Pith},
  title        = {Pith review of: Cross-Cultural Value Attribution in Large Vision-Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UV5JZQBY}},
  note         = {Machine review of arXiv:2604.09945}
}
read the original abstract

The rapid adoption of large vision-language models (LVLMs) in recent years has been accompanied by growing fairness concerns due to their propensity to reinforce harmful societal stereotypes. While significant attention has been paid to such fairness concerns in the context of social biases, relatively little prior work has examined the presence of stereotypes in LVLMs related to cultural contexts such as religion, nationality, and socioeconomic status. In this work, we aim to narrow this gap by investigating how cultural contexts depicted in images influence the judgments LVLMs make about a person's moral, ethical, and political values. We conduct a multi-dimensional analysis of such value judgments in nine LVLMs using counterfactual image sets, which depict the same person across different cultural contexts. Our evaluation framework pairs descriptive analyses (Moral Foundations Theory categorization, lexical analyses, and value sensitivity) with a novel grounding analysis that compares LVLM cross-context variation against two large-scale human surveys (MFQ-2 and WVS Wave 7). Across 4.8 million LVLM generations, we identify three bias patterns that replicate across architecturally diverse models: an inversion of the socioeconomic-status-to-Authority relationship found in WVS, and two race-conditional failures that override cultural context cues when depicting Middle Eastern persons. Additional ablations show that the socioeconomic-status-to-Authority inversion bias is amplified by image conditioning and persists across different model sizes.

Figures

Figures reproduced from arXiv: 2604.09945 by the authors.

Figure 1
Figure 1. We prompt LVLMs with counterfactuals depicting different cultural contexts (here: (a) Christian church, (b) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Frequency of MFT foundation value assignments by model and religious context [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Frequency of MFT foundation value assignments by model and religious context [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Frequency of MFT foundation value assignments by model and socioeconomic context [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Frequency of MFT foundation value assignments by model and national context [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

117 extracted references · 1 linked inside Pith

  1. [1]

    This matrix is then used to compute the predicted signal [see Eqs

    Analysis of memory capacity for linear reservoir networks To quantitatively define memory capacity, we recall that the training objective is to fit an output matrix via ridge regression using the target signal. This matrix is then used to compute the predicted signal [see Eqs. (15) and (16) inMethods]. All information regarding the target signal within th...

  2. [2]

    Panels (a,c) and (b,d) utilize hyperparameter values trained on the Lorenz and chaotic food-chain systems, respectively

    Memory capacity of the vestibular reservoir computer Figure 7 displays the memory function M F (τ) for cou- pled and uncoupled reservoirs. Panels (a,c) and (b,d) utilize hyperparameter values trained on the Lorenz and chaotic food-chain systems, respectively. Specifically, in Fig. 7(a), the baseline model is the coupled reservoir com- puter, with its memo...

  3. [3]

    mirror attractors

    for the two chaotic target systems. The external input u= [u 1, u2, u3]⊺, obtained by solving the Lorenz system [Eq. (12)] or the chaotic food-chain model [Eq. (13)], is a sequence of lengthL transient +L train +L validation. This sequence is fed into the reservoir described by: ˙r=τf(r,u), 12 whereτdenotes the time constant,r∈ R N represents the reservoi...

  4. [4]

    Maass, T

    W. Maass, T. Natschl¨ ager, and H. Markram, Real-time computing without stable states: A new framework for neural computation based on perturbations, Neural Comput.14, 2531 (2002)

    2002

  5. [5]

    Jaeger and H

    H. Jaeger and H. Haas, Harnessing nonlinearity: Pre- dicting chaotic systems and saving energy in wireless communication, science304, 78 (2004)

    2004

  6. [6]

    Lukoˇ seviˇ cius and H

    M. Lukoˇ seviˇ cius and H. Jaeger, Reservoir computing approaches to recurrent neural network training, Com- puter science review3, 127 (2009)

    2009

  7. [7]

    Manjunath and H

    G. Manjunath and H. Jaeger, Echo state property linked to an input: Exploring a fundamental characteristic of recurrent neural networks, Neur. Comp.25, 671 (2013)

    2013

  8. [8]

    Pathak, Z

    J. Pathak, Z. Lu, B. Hunt, M. Girvan, and E. Ott, Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data, Chaos27, 121102 (2017)

    2017

Show all 117 references
  1. [9]

    Pathak, B

    J. Pathak, B. Hunt, M. Girvan, Z. Lu, and E. Ott, Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach, Phys. Rev. Lett.120, 024102 (2018)

    2018

  2. [10]

    Jiang and Y.-C

    J. Jiang and Y.-C. Lai, Model-free prediction of spa- tiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius, Phys. Rev. Research1, 033056 (2019)

    2019

  3. [11]

    C. Klos, Y. F. K. Kossio, S. Goedeke, A. Gilra, and R.- M. Memmesheimer, Dynamical learning of dynamics, Phys. Rev. Lett.125, 088103 (2020)

    2020

  4. [12]

    Patel, D

    D. Patel, D. Canaday, M. Girvan, A. Pomerance, and E. Ott, Using machine learning to predict statisti- cal properties of non-stationary dynamical processes: System climate, regime transitions, and the effect of stochasticity, Chaos31, 033149 (2021)

    2021

  5. [13]

    E. Bollt, On explaining the surprising success of reser- voir computing forecaster of chaos? the universal ma- chine learning dynamical system with contrast to var and dmd, Chaos31, 013108 (2021)

    2021

  6. [14]

    D. J. Gauthier, E. Bollt, A. Griffith, and W. A. Bar- bosa, Next generation reservoir computing, Nat. Com- mun.12, 1 (2021)

    2021

  7. [15]

    Parlitz, Learning from the past: reservoir comput- ing using delayed variables, Frontiers in Applied Math- ematics and Statistics10, 10.3389/fams.2024.1221051 (2024)

    U. Parlitz, Learning from the past: reservoir comput- ing using delayed variables, Frontiers in Applied Math- ematics and Statistics10, 10.3389/fams.2024.1221051 (2024)

    2024 doi

  8. [16]

    Vlachas, J

    P.-R. Vlachas, J. Pathak, B. R. Hunt, T. P. Sapsis, M. Girvan, E. Ott, and P. Koumoutsakos, Backprop- agation algorithms and reservoir computing in recur- rent neural networks for the forecasting of complex spa- tiotemporal dynamics, Neural Netw.126, 191 (2020)

    2020

  9. [17]

    Verstraeten, B

    D. Verstraeten, B. Schrauwen, M. D’Haene, and D. Stroobandt, An experimental unification of reservoir computing methods, Neural Netw.20, 391 (2007), echo State Networks and Liquid State Machines

    2007

  10. [18]

    Kong, H.-W

    L.-W. Kong, H.-W. Fan, C. Grebogi, and Y.-C. Lai, Machine learning prediction of critical transition and system collapse, Phys. Rev. Research3, 013090 (2021)

    2021

  11. [19]

    Kong, H.-W

    L.-W. Kong, H.-W. Fan, C. Grebogi, and Y.-C. Lai, Emergence of transient chaos and intermittency in ma- chine learning, J. Phys. Complex.2, 035014 (2021)

    2021

  12. [20]

    Panahi, L.-W

    S. Panahi, L.-W. Kong, M. Moradi, Z.-M. Zhai, B. Glaz, M. Haile, and Y.-C. Lai, Machine learning prediction of tipping in complex dynamical systems, Phys. Rev. Res. 6, 043194 (2024)

    2024

  13. [21]

    Tanaka, T

    G. Tanaka, T. Yamane, J. B. H´ eroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, Recent advances in physical reservoir com- puting: A review, Neu. Net.115, 100 (2019)

    2019

  14. [22]

    Nakajima, Physical reservoir computing—an intro- ductory perspective, Jpn

    K. Nakajima, Physical reservoir computing—an intro- ductory perspective, Jpn. J. Appl. Phys.59, 060501 (2020)

    2020

  15. [23]

    Nakajima and I

    K. Nakajima and I. Fischer,Reservoir computing, edited by R. A. Meyers (Springer Singapore, Singapore, 2021)

    2021

  16. [24]

    Stepney, Physical reservoir computing: a tutorial, Natural Computing , 1 (2024)

    S. Stepney, Physical reservoir computing: a tutorial, Natural Computing , 1 (2024)

    2024

  17. [25]

    Zhang and D

    H. Zhang and D. V. Vargas, A survey on reservoir computing and its interdisciplinary applications be- yond traditional machine learning, IEEE Access11, 81033–81070 (2023)

    2023

  18. [26]

    Appeltant, M

    L. Appeltant, M. C. Soriano, G. Van der Sande, J. Danckaert, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, Information processing using a single dynamical node as complex system, Na- ture communications2, 468 (2011)

    2011

  19. [27]

    Mor´ an, V

    A. Mor´ an, V. Canals, F. Galan-Prado, C. F. Frasser, D. Radhakrishnan, S. Safavi, and J. L. Rossell´ o, Hardware-optimized reservoir computing system for edge intelligence applications, Cognitive Computation , 1 (2023)

    2023

  20. [28]

    Milano, G

    G. Milano, G. Pedretti, K. Montano, S. Ricci, S. Hashemkhani, L. Boarino, D. Ielmini, and C. Riccia- rdi, In materia reservoir computing with a fully memris- tive architecture based on self-organizing nanowire net- works, Nature materials21, 195 (2022)

    2022

  21. [29]

    Zhong, J

    Y. Zhong, J. Tang, X. Li, X. Liang, Z. Liu, Y. Li, Y. Xi, P. Yao, Z. Hao, B. Gao,et al., A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing, Nature Electronics5, 672 (2022)

    2022

  22. [30]

    M. M. Rajib, W. Al Misba, M. F. F. Chowdhury, M. S. Alam, and J. Atulasimha, Skyrmion based energy-efficient straintronic physical reservoir comput- ing, Neuromorphic Computing and Engineering2, 044011 (2022)

    2022

  23. [31]

    Liang, J

    X. Liang, J. Tang, Y. Zhong, B. Gao, H. Qian, and H. Wu, Physical reservoir computing with emerging electronics, Nature Electronics7, 193 (2024)

    2024

  24. [32]

    Larger, A

    L. Larger, A. Bayl´ on-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification, Phys. Rev. X7, 011015 (2017)

    2017

  25. [33]

    W. Du, C. Li, Y. Huang, J. Zou, L. Luo, C. Teng, H.-C. Kuo, J. Wu, and Z. Wang, An optoelectronic reservoir computing for temporal information processing, IEEE Electron Device Letters43, 406 (2022)

    2022

  26. [34]

    Picco, A

    E. Picco, A. Lupo, and S. Massar, Deep photonic reser- voir computer for speech recognition, IEEE Transac- tions on Neural Networks and Learning Systems (2024)

    2024

  27. [35]

    Wang and F

    X. Wang and F. Cichos, Harnessing synthetic active par- ticles for physical reservoir computing, Nature Commu- nications15, 774 (2024). 14

    2024

  28. [36]

    Abbas, H

    A. Abbas, H. Abdel-Ghani, and I. S. Maksymov, Clas- sical and quantum physical reservoir computing for on- board artificial intelligence systems: A perspective, Dy- namics4, 643 (2024)

    2024

  29. [37]

    Palacios, R

    A. Palacios, R. Mart´ ınez-Pe˜ na, M. C. Soriano, G. L. Giorgi, and R. Zambrini, Role of coherence in many- body quantum reservoir computing, Communications Physics7, 369 (2024)

    2024

  30. [38]

    C. Zhu, P. J. Ehlers, H. I. Nurdin, and D. Soh, Practical few-atom quantum reservoir computing (2024)

    2024

  31. [39]

    M. C. Soriano, D. Brunner, M. Escalona-Mor ˜A¡n, C. R. Mirasso, and I. Fischer, Minimal approach to neuro- inspired information processing, Frontiers in Com- putational Neuroscience9, 10.3389/fncom.2015.00068 (2015)

    2015 doi

  32. [40]

    Cucchi, C

    M. Cucchi, C. Gruener, L. Petrauskas, P. Steiner, H. Tseng, A. Fischer, B. Penkovsky, C. Matthus, P. Birkholz, H. Kleemann, and K. Leo, Reservoir com- puting with biocompatible organic electrochemical net- works for brain-inspired biosignal classification, Science Advances7, e...

    2021

  33. [41]

    Illeperuma, R

    M. Illeperuma, R. Pina, V. De Silva, and X. Liu, Novel directions for neuromorphic machine intelligence guided by functional connectivity: A review, Machines12, 574 (2024)

    2024

  34. [42]

    He and P

    S. He and P. Musgrave, Physical reservoir computing on a soft bio-inspired swimmer, Neural Networks181, 106766 (2025)

    2025

  35. [43]

    Cucchi, S

    M. Cucchi, S. Abreu, G. Ciccone, D. Brunner, and H. Kleemann, Hands-on reservoir computing: a tutorial for practical implementation, Neuromorphic Computing and Engineering2, 032002 (2022)

    2022

  36. [44]

    Schrauwen, D

    B. Schrauwen, D. Verstraeten, and J. Van Campenhout, An overview of reservoir computing: theory, applica- tions and implementations, inProceedings of the 15th European Symposium on Artificial Neural Networks. p. 471-482 2007(2007) pp. 471–482

    2007

  37. [45]

    H. Ma, D. Prosperino, and C. R¨ ath, A novel approach to minimal reservoir computing, Scientific Reports13, 10.1038/s41598-023-39886-w (2023)

    2023 doi

  38. [46]

    Gonon, L

    L. Gonon, L. Grigoryeva, and J.-P. Ortega, Infinite- dimensional reservoir computing, Neural Networks179, 106486 (2024)

    2024

  39. [47]

    N. D. Haynes, M. C. Soriano, D. P. Rosin, I. Fischer, and D. J. Gauthier, Reservoir computing with a single time-delay autonomous Boolean node, Phys. Rev. E91, 020801 (2015)

    2015

  40. [48]

    G. Dion, S. Mejaouri, and J. Sylvestre, Reservoir com- puting with a single delay-coupled non-linear mechani- cal oscillator, Journal of Applied Physics124(2018)

    2018

  41. [49]

    J. Li, C. Zhao, K. Hamedani, and Y. Yi, Analog hard- ware implementation of spike-based delayed feedback reservoir computing system, in2017 International Joint Conference on Neural Networks (IJCNN)(IEEE, 2017) pp. 3439–3446

    2017

  42. [50]

    Q. H. Tran and K. Nakajima, Higher-order quantum reservoir computing (2020)

    2020

  43. [51]

    Boshgazi, A

    S. Boshgazi, A. Jabbari, K. Mehrany, and M. Memarian, Virtual reservoir computer using an optical resonator, Optical Materials Express12, 1140 (2022)

    2022

  44. [52]

    Nichele and A

    S. Nichele and A. Molund, Deep learning with cellu- lar automaton-based reservoir computing, Complex Sys- tems26, 319–340 (2017)

    2017

  45. [53]

    Goudarzi, M

    A. Goudarzi, M. R. Lakin, and D. Stefanovic, Dna reser- voir computing: a novel molecular computing approach, inInternational Workshop on DNA-Based Computers (Springer, 2013) pp. 76–89

    2013

  46. [54]

    Jaurigue, Chaotic attractor reconstruction using small reservoirs—the influence of topology, Machine Learning: Science and Technology5, 035058 (2024)

    L. Jaurigue, Chaotic attractor reconstruction using small reservoirs—the influence of topology, Machine Learning: Science and Technology5, 035058 (2024)

    2024

  47. [55]

    Jaeger, Short term memory in echo state networks, GMD Forschungszentrum Informationstechnik (2001)

    H. Jaeger, Short term memory in echo state networks, GMD Forschungszentrum Informationstechnik (2001)

    2001

  48. [56]

    T. L. Carroll, Optimizing memory in reservoir comput- ers, Chaos: An Interdisciplinary Journal of Nonlinear Science32(2022)

    2022

  49. [57]

    Dambre, D

    J. Dambre, D. Verstraeten, B. Schrauwen, and S. Mas- sar, Information processing capacity of dynamical sys- tems, Scientific reports2, 514 (2012)

    2012

  50. [58]

    H. Ma, D. Prosperino, A. Haluszczynski, and C. R¨ ath, Efficient forecasting of chaotic systems with block- diagonal and binary reservoir computing, Chaos: An In- terdisciplinary Journal of Nonlinear Science33(2023)

    2023

  51. [59]

    E. N. Lorenz, Deterministic nonperiodic flow, J. Atmos. Sci.20, 130 (1963)

    1963

  52. [60]

    Hastings and T

    A. Hastings and T. Powell, Chaos in a three-species food chain, Ecology72, 896 (1991)

    1991

  53. [61]

    McCann and P

    K. McCann and P. Yodzis, Nonlinear dynamics and population disappearances, Ame. Naturalist144, 873 (1994)

    1994

  54. [62]

    S. M. Highstein, R. R. Fay, and A. N. Popper,The vestibular system, Vol. 24 (Springer, 2004)

    2004

  55. [63]

    J. M. Goldberg,The vestibular system: a sixth sense (Oxford University Press, USA, 2012)

    2012

  56. [64]

    Khan and R

    S. Khan and R. Chang, Anatomy of the vestibular sys- tem: a review, NeuroRehabilitation32, 437 (2013)

    2013

  57. [65]

    K. E. Cullen, Vestibular processing during natural self- motion: implications for perception and action, Nature Reviews Neuroscience20, 346 (2019)

    2019

  58. [66]

    Hudspeth and R

    A. Hudspeth and R. Jacobs, Stereocilia mediate trans- duction in vertebrate hair cells (auditory system/cil- ium/vestibular system)., Proceedings of the National Academy of Sciences76, 1506 (1979)

    1979

  59. [67]

    P. Guth, P. Perin, C. Norris, and P. Valli, The vestibu- lar hair cells: post-transductional signal processing, Progress in neurobiology54, 193 (1998)

    1998

  60. [68]

    Wall Iii, D

    C. Wall Iii, D. Merfeld, S. Rauch, and F. Black, Vestibu- lar prostheses: the engineering and biomedical issues, Journal of Vestibular Research12, 95 (2003)

    2003

  61. [69]

    S. A. Moshizi, C. J. Pastras, R. Sharma, M. P. Mah- mud, R. Ryan, A. Razmjou, and M. Asadnia, Recent advancements in bioelectronic devices to interface with the peripheral vestibular system, Biosensors and Bio- electronics214, 114521 (2022)

    2022

  62. [70]

    Lacour and L

    M. Lacour and L. Borel, Vestibular control of posture and gait., Archives Italiennes de Biologie131, 81 (1993)

    1993

  63. [71]

    S. L. Whitney, G. F. Marchetti, M. Pritcher, and J. M. Furman, Gaze stabilization and gait performance in vestibular dysfunction, Gait & posture29, 194 (2009)

    2009

  64. [72]

    Fransson, A

    P.-A. Fransson, A. Hafstrom, M. Karlberg, M. Mag- nusson, A. Tjader, and R. Johansson, Postural con- trol adaptation during galvanic vestibular and vibra- tory proprioceptive stimulation, IEEE Transactions on Biomedical Engineering50, 1310 (2003)

    2003

  65. [73]

    Patane, C

    F. Patane, C. Laschi, H. Miwa, E. Guglielmelli, P. Dario, and A. Takanishi, Design and development of a biologically-inspired artificial vestibular system for robot heads, in2004 IEEE/RSJ International Confer- 15 ence on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH...

    2004

  66. [74]

    Tin and C.-S

    C. Tin and C.-S. Poon, Internal models in sensorimotor integration: perspectives from adaptive control theory, Journal of Neural Engineering2, S147 (2005)

    2005

  67. [75]

    Mergner, G

    T. Mergner, G. Schweigart, and L. Fennell, Vestibular humanoid postural control, Journal of Physiology-Paris 103, 178–194 (2009)

    2009

  68. [76]

    S. L. Whitney, A. H. Alghadir, and S. Anwer, Recent evidence about the effectiveness of vestibular rehabil- itation, Current treatment options in neurology18, 1 (2016)

    2016

  69. [77]

    Sulway and S

    S. Sulway and S. L. Whitney, Advances in vestibular rehabilitation, Vestibular Disorders82, 164 (2019)

    2019

  70. [78]

    C. F. Santos, J. Belinha, F. Gentil, M. Parente, B. Areias, and R. N. Jorge, Biomechanical study of the vestibular system of the inner ear using a numer- ical method, Procedia IUTAM24, 30 (2017)

    2017

  71. [79]

    A. Q. Momani and F. M. Cardullo, A review of the recent literature on the mathematical modeling of the vestibular system, in2018 AIAA Modeling and Simu- lation Technologies Conference(American Institute of Aeronautics and Astronautics, 2018)

    2018

  72. [80]

    W. Steinhausen, Ueber die beobachtung der cupula in den bogengangsampullen des labyrinths des lebenden hechts., Pfl¨ ugers Archiv f¨ ur die gesamte Physiologie des Menschen und der Tiere (1933)

    1933

  73. [81]

    Straka, M

    H. Straka, M. G. Paulin, and L. F. Hoffman, Trans- lations of steinhausen’s publications provide insight into their contributions to peripheral vestibular neuro- science, Frontiers in Neurology12, 676723 (2021)

    2021

  74. [82]

    Gernandt, Response of mammalian vestibular neu- rons to horizontal rotation and caloric stimulation, Jour- nal of neurophysiology12, 173 (1949)

    B. Gernandt, Response of mammalian vestibular neu- rons to horizontal rotation and caloric stimulation, Jour- nal of neurophysiology12, 173 (1949)

    1949

  75. [83]

    A. A. J. van Egmond, J. J. Groen, and L. B. W. Jong- kees, The mechanics of the semicircular canal, The Jour- nal of Physiology110, 1–17 (1949)

    1949

  76. [84]

    Groen, The semicircular canal system of the organs of equilibrium-i, Physics in Medicine & Biology1, 103 (1956)

    J. Groen, The semicircular canal system of the organs of equilibrium-i, Physics in Medicine & Biology1, 103 (1956)

    1956

  77. [85]

    Groen, The semicircular canal system of the organs of equilibrium-ii, Physics in Medicine & Biology1, 225 (1957)

    J. Groen, The semicircular canal system of the organs of equilibrium-ii, Physics in Medicine & Biology1, 225 (1957)

    1957

  78. [86]

    L. R. Young, The current status of vestibular system models, Automatica5, 369 (1969)

    1969

  79. [87]

    Schmid, M

    R. Schmid, M. Stefanelli, and E. Mira, Mathematical modelling: a contribution to clinical vestibular analysis, Acta oto-laryngologica72, 292 (1971)

    1971

  80. [88]

    Benson, N

    A. Benson, N. Bischof, W. Collins, A. Fregly, A. Gray- biel, F. Guedry, W. Johnson, L. Jongkees, H. Kornhu- ber, R. Mayne,et al., A systems concept of the vestibu- lar organs, Vestibular system part 2: psychophysics, ap- plied aspects and general interpretations , 493 (1974)

    1974

  81. [89]

    J. L. Meiry,The vestibular system and human dynamic space orientation., Ph.D. thesis, Massachusetts Institute of Technology (1965)

    1965

  82. [90]

    J. A. Houck, R. J. Telban, and F. M. Cardullo,Motion cueing algorithm development: Human-centered linear and nonlinear approaches, Tech. Rep. (NASA, 2005)

    2005

  83. [91]

    R. A. Peters,Dynamics of the vestibular system and their relation to motion perception, spatial disorienta- tion, and illusions, Tech. Rep. (NASA, 1969)

    1969

  84. [92]

    R. D. Rabbitt, E. R. Damiano, and J. W. Grant, Biome- chanics of the semicircular canals and otolith organs, in The vestibular system(Springer, 2004) pp. 153–201

    2004

  85. [93]

    Gastaldi, S

    L. Gastaldi, S. Pastorelli, M. Sorli,et al., Vestibular apparatus: dynamic model of the semicircular canals, WIT Trans. Biomed. Heal13, 223 (2009)

    2009

  86. [94]

    T. C. Stewart, B. Tripp, and C. Eliasmith, Python scripting in the nengo simulator, Frontiers in neuroin- formatics3, 359 (2009)

    2009

  87. [95]

    A. P. Bradshaw, I. S. Curthoys, M. J. Todd, J. S. Mag- nussen, D. S. Taubman, S. T. Aw, and G. M. Halmagyi, A mathematical model of human semicircular canal ge- ometry: a new basis for interpreting vestibular physi- ology, Journal of the Association for Research in Oto- laryng...

    2010

  88. [96]

    McCollum and D

    G. McCollum and D. A. Hanes, Symmetries of the cen- tral vestibular system: forming movements for grav- ity and a three-dimensional world, Symmetry2, 1544 (2010)

    2010

  89. [97]

    Asadnia, A

    M. Asadnia, A. G. P. Kottapalli, K. D. Karavitaki, M. E. Warkiani, J. Miao, D. P. Corey, and M. Tri- antafyllou, From biological cilia to artificial flow sensors: Biomimetic soft polymer nanosensors with high sensing performance, Scientific reports6, 1 (2016)

    2016

  90. [98]

    Asadi, S

    H. Asadi, S. Mohamed, C. P. Lim, S. Nahavandi, and E. Nalivaiko, Semicircular canal modeling in human per- ception, Reviews in the Neurosciences28, 537 (2017)

    2017

  91. [99]

    M. G. Paulin and L. F. Hoffman, Models of vestibular semicircular canal afferent neuron firing activity, Jour- nal of Neurophysiology122, 2548 (2019)

    2019

  92. [100]

    Ramat, Understanding the rotational vestibular oc- ular reflex: from differential equations to laplace trans- forms, Progress in Brain Research248, 29 (2019)

    S. Ramat, Understanding the rotational vestibular oc- ular reflex: from differential equations to laplace trans- forms, Progress in Brain Research248, 29 (2019)

    2019

  93. [101]

    F. Lu, M. Nguyen, Y. Nguyen, R. Karthik, and Z. Tcheng, Modeling the human vestibular system as a controlled system, Department of Bioengineering- University of California, San Diego, San Diego (2021)

    2021

  94. [102]

    Y. Y. Minyailo and A. Kruchinina, Linear mathemati- cal model of relation between extraocular muscles and vestibular system, Moscow University Mechanics Bul- letin79, 12 (2024)

    2024

  95. [103]

    Moore, M

    L. Moore, M. Hachemaoui, E. Idoux, N. Vibert, and P. Vidal, The linear and non-linear relationships be- tween action potential discharge rates and membrane potential in model vestibular neurons., Nonlinear Stud- ies11(2004)

    2004

  96. [104]

    A. M. Green and D. E. Angelaki, Internal models and neural computation in the vestibular system, Experi- mental brain research200, 197 (2010)

    2010

  97. [105]

    Glasauer, M

    S. Glasauer, M. Dieterich, and T. Brandt, Neuronal network-based mathematical modeling of perceived ver- ticality in acute unilateral vestibular lesions: from nerve to thalamus and cortex, Journal of neurology265, 101 (2018)

    2018

  98. [106]

    T. K. Clark, M. C. Newman, F. Karmali, C. M. Oman, and D. M. Merfeld, Mathematical models for dynamic, multisensory spatial orientation perception, Progress in brain research248, 65 (2019)

    2019

  99. [107]

    Alexandrov, T

    V. Alexandrov, T. Alexandrova, R. Vega, G. Castillo- Quiroz, A. Angeles-Vazquez, M. Reyes-Romero, and E. Soto, Information process in vestibular system, WSEAS Transactions in Biology and Medicine12, 193 (2007). 16

    2007

  100. [108]

    Sadovnichy, V

    V. Sadovnichy, V. Alexandrov, E. Soto, T. Alexandrova, T. Astakhova, R. Vega, N. Kulikovskaya, V. Kurilov, S. Migunov, and N. Shulenina, A mathematical model of the response of the semicircular canal and otolith to vestibular system rotation under gravity, Journal of Mathemati...

    2007

  101. [109]

    V. V. Aleksandrov, M. R. Romero, E. Soto, R. Vega, T. B. Alexandrova, D. I. Bugrov, A. V. Lebedev, S. S. Lemak, and K. V. Tikhonova, Mathematical modeling of output signal for the correction of the vestibular system inertial biosensors, in2014 International Sym- posium on Iner...

    2014

  102. [110]

    Canelo, I

    ´A. Canelo, I. Tejado Balsera, J. E. Traver Becerra, B. M. Vinagre Jara, and C. Nuevo Gallardo, Modeling of the human vestibular system and integration in a simulator for the study of orientation and balance control, Actas de las XXXIX Jornadas de Autom´ atica, Badajoz, 5-7 de...

    2018

  103. [111]

    Griffith, A

    A. Griffith, A. Pomerance, and D. J. Gauthier, Forecast- ing chaotic systems with very low connectivity reservoir computers, Chaos: An Interdisciplinary Journal of Non- linear Science29(2019)

    2019

  104. [112]

    Wang and C

    Y. Wang and C. A. Shoemaker, A general stochas- tic algorithmic framework for minimizing expensive black box objective functions based on surrogate models and sensitivity analysis, arXiv preprint arXiv:1410.6271 (2014)

    2014 arXiv

  105. [113]

    D. E. Goldberg,Genetic algorithms(pearson education India, 2013)

    2013

  106. [114]

    Takens, The reconstruction theorem for endomor- phisms, Bulletin of the Brazilian Mathematical Society 33, 231 (2002)

    F. Takens, The reconstruction theorem for endomor- phisms, Bulletin of the Brazilian Mathematical Society 33, 231 (2002)

    2002

  107. [115]

    M. T. Rosenstein, J. J. Collins, and C. J. De Luca, A practical method for calculating largest lyapunov expo- nents from small data sets, Physica D: Nonlinear Phe- nomena65, 117 (1993)

    1993

  108. [116]

    Zhai, L.-W

    Z.-M. Zhai, L.-W. Kong, and Y.-C. Lai, Emergence of a resonance in machine learning, Physical Review Re- search5, 033127 (2023)

    2023

  109. [117]

    Pardo,Statistical Inference Based on Divergence Measures, Statistics: A Series of Textbooks and Mono- graphs (CRC Press, Boca Raton, Florida, 2018)

    L. Pardo,Statistical Inference Based on Divergence Measures, Statistics: A Series of Textbooks and Mono- graphs (CRC Press, Boca Raton, Florida, 2018). 17 SUPPLEMENT AR Y INFORMA TION Appendix S1: V estibular System The vestibular system is a sensory mechanism responsible for ...

    2018

Pith tools

Reviewed July 12, 2026 · model on record in the stance chip above.