Pith. sign in

REVIEW 6 major objections 5 minor 65 references

AdaptiveCoPilot: Design and Testing of a NeuroAdaptive LLM Cockpit Guidance System in both Novice and Expert Pilots

T0 review · 6 major / 5 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 AdaptiveCoPilot claims that a closed-loop system reading fNIRS workload signals and using a small language model to adapt cue modality and information load keeps pilots in optimal working-memory states during VR preflight checklists, with…

desk verdict A genuine proof-of-concept for LLM-driven neuroadaptive guidance, but the abstract oversells the results and the main outcome is the same classifier the system is designed to optimize. read the letter →

arxiv 2501.04156 v1 pith:FTVWYZC2 submitted 2025-01-07 cs.HC

Shaoyue Wen , Michael Middleton , Songming Ping , Nayan N Chawla , Guande Wu , Bradley S Feest , Chihab Nadri , Yunmei Liu
show 7 more authors
David Kaber Maryam Zahabi Ryan P. McMahan Sonia Castelo Ryan Mckendrick Jing Qian Claudio Silva
This is my paper · ORCID
classification cs.HC
keywords neuroadaptivesystemcognitiveworkloadfNIRSlargelanguagemodelvirtualrealitypilottrainingadaptiveguidancepreflightchecklist
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 claims that a cockpit assistant can read a pilot's cognitive workload from brain signals and adapt its own guidance in real time, and that this keeps pilots in a healthier 'optimal' workload state—at least for working memory—during a VR preflight checklist. The system, AdaptiveCoPilot, closes a loop: fNIRS (functional near-infrared spectroscopy) prefrontal readings are classified into underload, optical, and overload states for memory, perception, and attention; a small language model then chooses which cue modality (visual, audio, text) and how much information to deliver. The authors report a significant increase in optimal working-memory states for the adaptive condition against both a paper-checklist baseline and a randomly guided control, and treat this as a proof-of-concept that neuroadaptive LLM guidance is feasible. They are explicit that the broader hypothesis—better workload leading to faster, error-free performance—was only partially supported, and they frame their results as indicative trends from a small pilot sample.

What carries the argument

The load-bearing mechanism is the closed loop among three components: the fNIRS preprocessing and classification pipeline (wavelet filtering, a sliding 10-second window, and three separate multinomial classifiers for working memory, perception, and attention, each trained with a Rasch-model labeling methodology from prior work), a set of 28 adaptive rules derived from expert interviews and workload theory that map underload, optimal, and overload states to modality and information-density choices, and a quantized PHI-3 LLM prompted with chain-of-thought reasoning that turns the current state, task tree, and gaze into a concrete guidance action. The claimed effect is that this loop produces more 'optimal' labels on the memory classifier than fixed or random guidance does.

What would settle it

A concrete test: compute, within each 10-second window, whether the proportion of time the memory classifier spends in 'optimal' predicts the probability of a procedure error or a step-completion delay in the next window. The paper already contains a hint in this direction, because error counts rose under adaptive guidance while optimal memory labels also rose; if that inverse relationship holds in the recorded ROS data, the 'optimal' label is not a valid proxy for performance. A second, cleaner experiment would run the same LLM guidance with the classifier labels replaced by labels generated at the same marginal rates but uncorrelated with behavior, which would isolate whether the adaptation is responding to the brain signal or merely to the guidance schedule.

Watch

Extended reading notes

Core claim

On its own terms, the central discovery is that an adaptive guidance loop driven by a real-time workload classifier can move the classified state itself: pilots using AdaptiveCoPilot had significantly higher rates of 'optimal' labels on the working-memory classifier than pilots using either the checklist baseline or a same-cadence random guidance condition, a difference the authors describe as strong evidence. The same effect did not hold for attention, and for perception the random condition outperformed the adaptive one even though both beat the baseline. Completion time trended lower with adaptive guidance but was not statistically significant, and error counts were significantly higher in the adaptive condition than in the baseline, which the authors attribute to complacency and a possible speed-accuracy trade-off. The paper's conclusion is therefore narrower than its framing: the system's demonstrated effect is on working-memory workload classification, and the authors recommend future work on the perceptual and attentional rules, on complacency, and on the LLM's evident confusion between attention and perception states.

Load-bearing premise

The whole outcome measure rests on the pre-trained fNIRS classifiers correctly labeling each moment as underload, optimal, or overload for each cognitive facet in this specific VR preflight task; if those labels are miscalibrated here, then increasing the frequency of 'optimal' labels may not correspond to any real improvement in pilot workload or performance.

Editorial extensions

If this is right

  • If the working-memory effect replicates, real-time fNIRS-based adaptation could be built into cockpit training aids, not just for UH-60 preflight but for any checklist-driven procedure where working memory is the bottleneck.
  • The result implies that adaptive rules do affect the classified state: the adaptive condition beat both a no-guidance baseline and a same-rate random guidance condition on memory, so the adaptation itself—not merely the presence of feedback—is what moves the classifier.
  • Because the random condition improved perception more than the adaptive one, the paper's own conclusion is that its strategies for managing perceptual and attentional load are ineffective; future systems need separate, better-tuned rules for those facets.
  • The increased error rates under adaptive guidance suggest a complacency or speed-accuracy trade-off; the authors recommend future systems explicitly manage complacency, for example by flagging when a pilot is taking too long on a step.
  • The qualitative interviews point to training, rather than operational flight, as the most plausible near-term deployment: experts said experienced pilots would not want the system during routine operations, but novices could benefit.

Reading between the lines

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

  • The strongest experimental contrast in the paper is adaptive vs random at a fixed 10-second cadence; a stricter test of 'neuroadaptivity' would compare against guidance selected by the same LLM from the same context but with the classifier labels withheld, isolating whether the brain-derived signal is what drives the effect or whether the LLM's contextual reasoning alone would do as well.
  • The classifier labels come from models trained on a Rasch-based labeling method that defines 'optimal' as a midpoint of capability; driving pilots toward that midpoint may be beneficial mainly for trainees who are below their capacity, and the same rule could degrade experts who are already near their ceiling—the paper's own expertise interviews hint at this.
  • One could extend the study to other high-stakes procedural work (medical checklists, ATC handovers) since the mechanism—fNIRS workload classification feeding an LLM that adapts modality and detail—is task-agnostic; the paper does not claim this, but the design generalizes.
  • Because the paper found no significant completion-time benefit and a significant error increase, the 'optimal' label may not be aligned with objective performance; a direct test would be whether the proportion of optimal-classified time in a window correlates with next-step error probability.
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

6 major / 5 minor

Summary. AdaptiveCoPilot is a VR preflight guidance system that uses fNIRS-based classifiers of working memory, perception, and attention to drive a PHI-3 LLM that adapts the modality and information load of cockpit instructions. A formative interview study with three expert pilots produced design requirements and 28 adaptive rules, and an eight-pilot within-subject VR experiment compared baseline (paper checklist), random guidance, and adaptive guidance. The paper reports a statistically significant increase in 'optimal' working-memory label rates under adaptive guidance relative to both baseline and random conditions, and it reports non-significant completion-time differences plus a significant difference in error counts whose direction is stated inconsistently. The Discussion honestly narrows the central claim to the working-memory classifier result, but the Abstract and Conclusion overstate the findings by presenting reduced completion time and improved perception states as established results.

Significance. If the fNIRS classifier labels are valid for this VR preflight task, the working-memory result is an interesting proof-of-concept for closed-loop neuroadaptive training guidance, and the paper's qualitative findings plus design strategies are useful for future adaptive cockpit systems. The manuscript also benefits from an unusually candid Discussion that acknowledges the perception, attention, completion-time, and error findings did not support the original hypotheses. However, the primary quantitative outcome is the rate of 'optimal' labels produced by the same classifiers whose outputs drive the adaptive controller, and no independent validation anchors those labels to objective task performance in this study; the paper's significance therefore hinges on an inherited and unverified classifier, the underlying model of which is not released.

major comments (6)
  1. [§5.2.1, §5.2.2, §6.3] The central quantitative claim is circular in structure. The adaptive policy in §4.1 and §5.2.2 is explicitly designed to move the fNIRS classifier outputs into the 'optimal' region, and the primary success measure in §6.3 is the rate of 'optimal' labels from the same classifiers introduced in §5.2.1. Without an independent validation connecting these labels to objective performance (e.g., step accuracy, error recovery, or task time within this VR preflight task), the significant working-memory result may be a feedback artifact rather than evidence of improved cognitive state. The paper should provide such an anchoring analysis, or explicitly reframe the contribution as 'increases in the classifier's optimal-label rate' with the circularity stated as a limitation.
  2. [Abstract and §6.3 Perception analysis] The Abstract's claim that AdaptiveCoPilot produced 'higher rates of optimal cognitive load states on the facets of working memory and perception' is not supported by the paper's own statistics. In §6.3, the random-vs-adaptive comparison for perception has beta = 0.421 (p < 0.001), meaning the random condition had a higher optimal-perception label rate than the adaptive condition; only the baseline-vs-adaptive comparison favored adaptive. The Discussion (Sec. 7) correctly acknowledges that 'the random condition was better then both,' so the Abstract and the 'Results indicate' sentence should be corrected.
  3. [Abstract, §9 Conclusion, §6.3 Completion Time] The Abstract and Conclusion state that AdaptiveCoPilot 'accelerated task completion time' and 'reduced task completion times,' but the completion-time gamma model in §6.3 found no significant differences (baseline vs adaptive p = 0.3034; random vs adaptive p = 0.3998). A non-significant trend toward faster completion is not the same as an accelerated completion time. The Conclusion's sentence 'Results show that AdaptiveCoPilot accelerated task completion time relative to the baseline and random system condition' is therefore unsupported and must be revised, especially in light of the Limitations section's own warning that quantitative findings should be read as indicative trends.
  4. [§6.3 Error Counts] The error-count results are internally inconsistent. The text reports a rate ratio of 0.644 for adaptive vs baseline (p = 0.0228), which, under the stated parameterization, means the adaptive condition had roughly 35% fewer errors than baseline; yet the same paragraph concludes 'a lower error count with baseline relative to adaptive,' and the Discussion (§7) and strategy list (§7.1) describe 'an increase in error counts in the guidance conditions' and 'higher error rates compared to the baseline.' The manuscript must state the model's reference coding unambiguously and reconcile these conflicting descriptions, because the direction of the error effect is load-bearing for the paper's interpretation of complacency and speed-accuracy trade-offs.
  5. [§6.3 Statistical model specification] The mixed-effects models are not specified in enough detail to assess the reported p-values. The working-memory model yields z = -16.173 and z = -30.737 with only eight participants (and, per §6.1, only six usable fNIRS sessions), which suggests that the unit of analysis may be individual 10 Hz time windows or per-procedure observations rather than participants. The manuscript should report the exact model formulas, the random-effects structure, the definition of the observation unit, and the effective sample size used in each analysis. Without this information, the extremely small p-values for the memory result cannot be trusted, and a pseudoreplication risk remains.
  6. [§6.1 and §6.3] The manuscript reports that fNIRS sessions from two of the eight quantitative participants 'could not be used in our final quantitative evaluation,' but the subsequent statistical analyses in §6.3 do not state that all fNIRS-based results are based on N = 6 rather than N = 8. The sample size for each model should be reported explicitly, and the implications for statistical power and generalizability should be discussed, especially since the Limitations section emphasizes the small sample.
minor comments (5)
  1. [Throughout] Several typos should be corrected: 'Prepossessing' for 'Preprocessing' (§5.1), 'cognivive' (Introduction), 'Similarily' (§2), 'neuoradaptive' (§1), and 'there pilots' (§7.1).
  2. [§10 Supplemental Materials] The supplemental list numbers two items as '(5)' (qualitative evaluation questions and prompt examples); the numbering should be fixed, and the file names should be listed explicitly so readers can locate each artifact.
  3. [§5.2.1] The sentence 'Our classifiers rely on multinomial symbolic regression, previously trained on a Rasch model labeling methodology, updating at 10hz' is ambiguous about whether the classifier updates at 10 Hz or the input features do; please rephrase and specify the temporal granularity of the labels.
  4. [Figures 4–7] The figures show raw trends across procedures and conditions but do not include confidence intervals or model-based estimates; adding error bars or shaded intervals would make the reported mixed-effects contrasts easier to interpret.
  5. [References] References [3] and [4] appear to describe the same work with different bibliographic metadata; please verify and retain only the correct source.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the evaluation is an empirical closed-loop comparison with an externally sourced classifier.

full rationale

The central quantitative claim is an empirical between-condition comparison, not a derivation. AdaptiveCoPilot's PHI-3 policy is a designed heuristic informed by theory and expert interviews; it is not fitted to this study's outcome data. The fNIRS classifier in Sec. 5.2.1 comes from McKendrick et al. [42], a separate published study with its own training data and Rasch-based labeling; although two of the present authors overlap, the citation is external evidence rather than a self-referential justification. The closed-loop concern that the classifier output is both controller input and evaluation outcome is a measurement-validity threat, not an identity by construction: the adaptive condition could have failed, and indeed showed no attention benefit, no significant completion-time benefit, and higher error counts than baseline. The paper's Sec. 7 discussion explicitly restricts the positive finding to the memory classifier and notes PHI-3's confusion on perception and attention. Abstract statements about reduced completion time are unsupported by the reported p-values, but that is a correctness and calibration issue outside circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the validity of the fNIRS classifier labels, which come from prior work by a co-author and are not independently verified. The system's timings and preprocessing choices are free parameters that affect the outcome. No new physical or conceptual entities are introduced.

free parameters (3)
  • fNIRS workload classifier parameters (per-facet multinomial symbolic regression models) = Trained on Rasch-labeled data in [42], not disclosed
    The primary outcome measure (optimal/underload/overload labels for memory, perception, attention) comes from these fitted models; they are the yardstick for the main results.
  • fNIRS preprocessing hyperparameters = wavelet threshold 0.1, low-pass 0.12 Hz, 10 s sliding window
    Chosen from prior methods (Sec 5.1) and affect the classification output; they are hand-selected rather than derived.
  • Guidance timing parameters = guidance at 10 s after correct action; error alert after 20 s inactivity
    Hand-selected system timings (Sec 5.1, Sec 6) that define the feedback schedule in random and adaptive conditions.
assumptions (4)
  • domain assumption fNIRS prefrontal hemodynamics index workload facets (working memory, perception, attention)
    Assumed throughout, especially Sec 5.2.1 and Fig 3; no validation in this paper beyond citation [42].
  • domain assumption Rasch-based labels in McKendrick et al. [42] are valid ground truth for underload/optimal/overload
    The classifiers were trained on this labeling; the paper treats the resulting 'optimal' label as the success criterion.
  • domain assumption Yerkes-Dodson curvilinear performance-load relationship and Wickens' Multiple Resource Theory justify the adaptive strategies
    Used in Sec 4.1 to justify adaptive strategies; not empirically verified in this study.
  • domain assumption PHI-3 LLM's chain-of-thought reasoning reliably selects appropriate strategies
    The paper itself reports the LLM was confused differentiating attention and perception (Discussion Sec 7), so this assumption is shaky.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AdaptiveCoPilot: Design and Testing of a NeuroAdaptive LLM Cockpit Guidance System in both Novice and Expert Pilots." pith.science (2026). https://pith.science/paper/FTVWYZC2

@misc{pith2026250104156,
  author       = {Pith},
  title        = {Pith review of: AdaptiveCoPilot: Design and Testing of a NeuroAdaptive LLM Cockpit Guidance System in both Novice and Expert Pilots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FTVWYZC2}},
  note         = {Machine review of arXiv:2501.04156}
}
read the original abstract

Pilots operating modern cockpits often face high cognitive demands due to complex interfaces and multitasking requirements, which can lead to overload and decreased performance. This study introduces AdaptiveCoPilot, a neuroadaptive guidance system that adapts visual, auditory, and textual cues in real time based on the pilot's cognitive workload, measured via functional Near-Infrared Spectroscopy (fNIRS). A formative study with expert pilots (N=3) identified adaptive rules for modality switching and information load adjustments during preflight tasks. These insights informed the design of AdaptiveCoPilot, which integrates cognitive state assessments, behavioral data, and adaptive strategies within a context-aware Large Language Model (LLM). The system was evaluated in a virtual reality (VR) simulated cockpit with licensed pilots (N=8), comparing its performance against baseline and random feedback conditions. The results indicate that the pilots using AdaptiveCoPilot exhibited higher rates of optimal cognitive load states on the facets of working memory and perception, along with reduced task completion times. Based on the formative study, experimental findings, qualitative interviews, we propose a set of strategies for future development of neuroadaptive pilot guidance systems and highlight the potential of neuroadaptive systems to enhance pilot performance and safety in aviation environments.

Figures

Figures reproduced from arXiv: 2501.04156 by the authors.

Figure 1
Figure 1. A. AdaptiveCoPilot is a neuroadaptive guidance system for pilot virtual reality preflight checklist training. B. Cognitive workload state is assessed through fNIRS neuroimaging classification models. C. AdaptiveCoPilot combines cognitive workload states, behavioral information, and carefully constructed adaptive rules into a context aware LLM. D. The Neural-Informed LLM reasons over the input to inform modality and … view at source ↗
Figure 2
Figure 2. The front and backend systems. A change in arrow color [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Cognitive classifier: fNIRS data is preprocessed using [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: All Workload Across Conditions to interact with the checklist, the cockpit systems, and shown the error correction dashboard. While in the baseline condition the pilot was instructed to only use the checklist unless they hear an audible error noise, and then to use the…
Figure 5
Figure 5. Figure 5: Optimal Workload Across Conditions and Procedures. We [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Error Rates Across Conditions and Procedures. We see a [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Completion Time Across Conditions and Procedures. We [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

65 extracted references · 51 canonical work pages

  1. [1]

    C. I. Aguilar Reyes, D. Wozniak, A. Ham, and M. Zahabi. Design and evaluation of an adaptive virtual reality training system.Virtual Reality, 27(3):2509–2528, 2023. 2

    work page 2023

  2. [2]

    L. M. Andreessen, P. Gerjets, D. Meurers, and T. O. Zander. Toward neuroadaptive support technologies for improving digital reading: a passive BCI-based assessment of mental workload imposed by text difficulty and presentation speed during reading. User Modeling and User-Adapted Interaction, 31(1):75–104, Mar. 2021. doi: 10.1007/ s11257-020-09273-5 3

    work page 2021

  3. [4]

    S. Auer, J. Gerken, H. Reiterer, and H.-C. Jetter. Comparison between virtual reality and physical flight simulators for cockpit familiarization. Proceedings of the ACM Symposium on Virtual Reality , pp. 15–23,

    work page

  4. [5]

    H. Ayaz, S. Bunce, P. Shewokis, K. Izzetoglu, B. Willems, and B. Onaral. Using Brain Activity to Predict Task Performance and Op- erator Efficiency. In H. Zhang, A. Hussain, D. Liu, and Z. Wang, eds., Advances in Brain Inspired Cognitive Systems, pp. 147–155. Springer, Berlin, Heidelberg, 2012. doi: 10.1007/978-3-642-31561-9 16 5

    work page doi:10.1007/978-3-642-31561-9 2012

  5. [6]

    H. Ayaz, M. Izzetoglu, P. A. Shewokis, and B. Onaral. Sliding-window motion artifact rejection for Functional Near-Infrared Spectroscopy. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 6567–6570, Aug. 2010. ISSN: 1558-4615. doi: 10.1109/IEMBS.2010.5627113 5

    work page arXiv 2010

  6. [7]

    H. Ayaz, P. A. Shewokis, S. Bunce, K. Izzetoglu, B. Willems, and B. Onaral. Optical brain monitoring for operator training and mental workload assessment. NeuroImage, 59(1):36–47, Jan. 2012. doi: 10. 1016/j.neuroimage.2011.06.023 5

    work page 2012

  7. [8]

    H. Ayaz, P. A. Shewokis, A. Curtin, M. Izzetoglu, K. Izzetoglu, and B. Onaral. Using MazeSuite and functional near infrared spectroscopy to study learning in spatial navigation. Journal of Visualized Experi- ments: JoVE, (56):3443, Oct. 2011. doi: 10.3791/3443 2

    work page doi:10.3791/3443 2011

  8. [9]

    L. G. Barron and M. R. Rose. Multitasking as a Predictor of Pilot Per- formance: Validity Beyond Serial Single-Task Assessments. Military Psychology, 29(4):316–326, Apr. 2017. Publisher: Routledge eprint: https://doi.org/10.1037/mil0000168. doi: 10.1037/mil0000168 2

    work page doi:10.1037/mil0000168 2017

Show all 65 references
  1. [10]

    S. A. Barter, J. Q. Clayton, and G. Clark. Aspects of fatigue affecting the design and maintenance of modern military aircraft. International Journal of Fatigue, 15(4):325–332, July 1993. doi: 10.1016/0142-1123 (93)90382-Z 2

    1993 doi

  2. [11]

    B. Bell, S. Feiner, and T. H¨ollerer. View management for virtual and augmented reality. In Proceedings of the 14th Annual ACM Symposium on User Interface Software and Technology, pp. 101–110. ACM, 2001. 3

    2001

  3. [12]

    S. Biswas. Evaluating errors and improving performance of chatgpt. International Journal of Clinical Medicine Education and Research, 2(6):182–188, 2023. 3

    2023

  4. [13]

    Cao Ph D, S

    S. Cao Ph D, S. K. Kearns Ph D, E. Niechwiej-Szwedo Ph D, E. Irving Ph D, et al. Computational cognitive modeling of pilot performance in pre-flight and take-off procedures. Journal of Aviation/Aerospace Education & Research, 33(4):2, 2024. 2

    2024

  5. [14]

    Castelo, J

    S. Castelo, J. Rulff, P. Solunke, E. McGowan, G. Wu, I. Roman, R. Lopez, B. Steers, Q. Sun, J. Bello, B. Feest, M. Middleton, R. Mck- endrick, and C. Silva. Hubar: A visual analytics tool to explore human behavior based on fnirs in ar guidance systems. IEEE transactions on vis...

    2024

  6. [15]

    Charles and J

    R. Charles and J. Nixon. Measuring mental workload using physiolog- ical measures: a systematic review. Applied Ergonomics, 74:221–232,

  7. [16]

    Chen et al

    S. Chen et al. Evaluation of chatgpt family of models for biomedical reasoning and classification. arXiv preprint, 2023. 3

    2023

  8. [17]

    M. Corbett. From law to folklore: work stress and the Yerkes-Dodson Law. Journal of Managerial Psychology, 30(6):741–752, Jan. 2015. Publisher: Emerald Group Publishing Limited. doi: 10.1108/JMP-03 -2013-0085 2

    2015 doi

  9. [18]

    Degani and E

    A. Degani and E. L. Wiener. Human factors of flight-deck checklists: The normal checklist. Technical Report NAS 1.26:177549, May 1991. NTRS Author Affiliations: San Jose State Univ., Miami Univ. NTRS Document ID: 19910017830 NTRS Research Center: Legacy CDMS (CDMS). 2, 4

    1991

  10. [19]

    Dehais, S

    F. Dehais, S. Ladouce, J. J. Torre Tresols, L. Darmet, and D. Callan. Mental Workload Classification during simulated flight operations based on cardiac and neural dynamics recorded using the MUSE 2 low-cost system. In 14th International Conference on Applied Human Factors and...

    2023

  11. [20]

    A. T. Duchowski, K. Krejtz, N. A. Gehrer, T. Bafna, and P. Bækgaard. The Low/High Index of Pupillary Activity. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20, pp. 1–12. Association for Computing Machinery, New York, NY , USA, Apr. 2020...

    2020

  12. [21]

    A. T. Duchowski, K. Krejtz, I. Krejtz, et al. The index of pupillary activity: Measuring cognitive load vis-a-vis task difficulty with pupil oscillation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018. 3

    2018

  13. [22]

    Funahashi

    S. Funahashi. Working Memory in the Prefrontal Cortex. Brain Sciences, 7(5):49, May 2017. Number: 5 Publisher: Multidisciplinary Digital Publishing Institute. doi: 10.3390/brainsci7050049 2

    2017 doi

  14. [23]

    Gartner and C

    W. Gartner and C. Murphy. Measurement of human resources: An annotated bibliography. Center for the Study of Higher Education, School of Education, University of Michigan, 1979. 2

    1979

  15. [24]

    Guthridge and V

    R. Guthridge and V . Clinton-Lisell. Evaluating the efficacy of virtual reality (vr) training devices for pilot training. Journal of Aviation Technology and Engineering, 12(2):Article 1, 2023. doi: 10.7771/2159 -6670.1286 4

    2023

  16. [25]

    Haapalainen, S

    E. Haapalainen, S. Kim, J. F. Forlizzi, and A. K. Dey. Psycho- physiological measures for assessing cognitive load. Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 301–310, 2010. 3

    2010

  17. [26]

    Hart and L

    S. Hart and L. Staveland. Development of nasa-tlx (task load index): Results of empirical and theoretical research. In Advances in Psychol- ogy, vol. 52, pp. 139–183. Elsevier, 1988. 2

    1988

  18. [27]

    J. A. Healey and R. W. Picard. Detecting stress during real-world driving tasks using physiological sensors. Trans. Intell. Transport. Sys., 6(2):156–166, 2005. 3

    2005

  19. [28]

    R. F. Helfrich, M. Huang, G. Wilson, and R. T. Knight. Prefrontal cortex modulates posterior alpha oscillations during top-down guided visual perception. Proceedings of the National Academy of Sciences, 114(35):9457–9462, Aug. 2017. Publisher: Proceedings of the National Acade...

    2017 doi

  20. [29]

    R. L. Helmreich, A. C. Merritt, and J. A. Wilhelm. The Evolution of Crew Resource Management Training in Commercial Aviation. The International Journal of Aviation Psychology, 9(1):19–32, Jan. 1999. Publisher: Taylor & Francis. doi: 10.1207/s15327108ijap0901 2 2

    1999 doi

  21. [30]

    S. Hill, H. Iavecchia, J. Byers, A. Bittner Jr, A. Zaklad, and R. Christ. Comparison of subjective workload ratings from six pilots with perfor- mance ratings on the modified cooper-harper scale. Aviation, Space, and Environmental Medicine, 63(6):485–491, 1992. 2

    1992

  22. [31]

    X. Hu, A. Moore, J. Coleman Eubanks, A. Aiyaz, and R. P. McMa- han. Evaluating Interaction Cue Purpose and Timing for Learning and Retaining Virtual Reality Training. In Proceedings of the 2020 ACM Symposium on Spatial User Interaction, SUI ’20, pp. 1–9. Association for Comput...

    2020

  23. [32]

    X. Hu, A. G. Moore, J. C. Eubanks, A. A. Aiyaz, and R. P. McMahan. The Effects of Delayed Interaction Cues in Virtual Reality Training. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 63–69, Mar. 2020. doi: 10.1109/ VRW50115...

    2020

  24. [33]

    J. Hunter. A cost-effective interactive 3d virtual reality system applied to aviation training. Virtual Reality in Aviation Training, pp. 127–140,

  25. [34]

    R. S. Jensen. Pilot Judgment and Crew Resource Management. Rout- ledge, London, Mar. 2017. doi: 10.4324/9781315246796 2

    2017 doi

  26. [35]

    Jin et al

    Q. Jin et al. Genegpt: augmenting large language models with domain tools for improved access to biomedical information. arXiv preprint,

  27. [36]

    Jovanovi´c, M

    M. Jovanovi´c, M. Baez, and F. Casati. Chatbots as conversational healthcare services. IEEE Internet Computing, 25(3):44–51, 2020. doi: 10.1109/MIC.2020.2972634 3

    2020

  28. [37]

    Kaplan, S

    J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020. 4

    2001 arXiv

  29. [38]

    Kasarskis, J

    P. Kasarskis, J. Stehwien, C. D. Wickens, et al. Comparison of expert and novice scan behaviors during vfr flight. In US Air Force Academy,

  30. [39]

    J. D. M.-W. C. Kenton and L. K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT, vol. 1, p. 2, 2019. 4

    2019

  31. [40]

    Koubaa et al

    A. Koubaa et al. Robot Operating System (ROS)., vol. 1. Springer,

  32. [41]

    Lindlbauer, A

    D. Lindlbauer, A. M. Feit, and O. Hilliges. Context-aware online adaptation of mixed reality interfaces. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology, pp. 147–160. ACM, 2019. 3

    2019

  33. [42]

    McKendrick, B

    R. McKendrick, B. Feest, A. Harwood, and B. Falcone. Theories and Methods for Labeling Cognitive Workload: Classification and Transfer Learning. Frontiers in Human Neuroscience, 13, Sept. 2019. Publisher: Frontiers. doi: 10.3389/fnhum.2019.00295 2, 5

    2019

  34. [43]

    McKendrick and A

    R. McKendrick and A. Harwood. Cognitive Workload and Workload Transitions Elicit Curvilinear Hemodynamics During Spatial Working Memory. Frontiers in Human Neuroscience, 13, 2019. 2

    2019

  35. [44]

    R. D. McKendrick and E. Cherry. A Deeper Look at the NASA TLX and Where It Falls Short. Proceedings of the Human Factors and Er- gonomics Society Annual Meeting, 62(1):44–48, Sept. 2018. Publisher: SAGE Publications Inc. doi: 10.1177/1541931218621010 2

    2018 doi

  36. [45]

    Molavi and G

    B. Molavi and G. A. Dumont. Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiological Measurement, 33(2):259, Jan. 2012. Publisher: IOP Publishing. doi: 10.1088/0967 -3334/33/2/259 5

    2012 doi

  37. [46]

    N. Moray. Mental workload: Its theory and measurement. Springer Science & Business Media, 2013. 2

    2013

  38. [47]

    C. H. Morris and Y . K. Leung. Pilot mental workload: how well do pilots really perform? Ergonomics, 49(15):1581– 1596, Dec. 2006. Publisher: Taylor & Francis eprint: https://doi.org/10.1080/00140130600857987. doi: 10.1080/ 00140130600857987 2

    2006 doi

  39. [48]

    S. Y . Mousavi, R. Low, and J. Sweller. Reducing cognitive load by mixing auditory and visual presentation modes. Journal of educational psychology, 87(2):319, 1995. 4

    1995

  40. [49]

    M¨uhlberger et al

    A. M¨uhlberger et al. Virtual reality training in aviation: A comparison of traditional and vr methods. Aviation, Space, and Environmental Medicine, 72(9):857–863, 2001. 2

    2001

  41. [50]

    Oberhauser and D

    M. Oberhauser and D. Dreyer. A virtual reality flight simulator for human factors engineering. Cogn. Technol. Work., 19(2-3):263–277,

  42. [51]

    Parasuraman and P

    R. Parasuraman and P. A. Hancock. Mitigating the adverse effects of cognitive aging on flight performance: Adaptive task allocation strategies. The International Journal of Aviation Psychology, 18(2):154– 183, 2008. 4

    2008

  43. [52]

    Pinti, I

    P. Pinti, I. Tachtsidis, A. Hamilton, J. Hirsch, C. Aichelburg, S. Gilbert, and P. W. Burgess. The present and future use of functional near- infrared spectroscopy (fNIRS) for cognitive neuroscience. Annals of the New York Academy of Sciences, 1464(1):5–29, Mar. 2020. doi: 10....

    2020

  44. [53]

    Socha, L

    V . Socha, L. Socha, L. Hanakova, V . Valenta, S. Kusmirek, and A. Lalis. Pilots’ Performance and Workload Assessment: Transition from Ana- logue to Glass-Cockpit. Applied Sciences, 10(15):5211, Jan. 2020. Number: 15 Publisher: Multidisciplinary Digital Publishing Institute. d...

    2020 doi

  45. [54]

    Spence and J

    C. Spence and J. Driver. Audiovisual links in endogenous covert spatial attention. Journal of Experimental Psychology: Human Perception and Performance, 23(5):1333–1350, 1997. 4

    1997

  46. [55]

    J. Sweller. Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2):257–285, 1988. 4

    1988

  47. [56]

    Thorpe, K

    A. Thorpe, K. Nesbitt, and A. Eidels. A Systematic Review of Empir- ical Measures of Workload Capacity. ACM Transactions on Applied Perception, 17(3):12:1–12:26, Oct. 2020. doi: 10.1145/3422869 2

    2020 doi

  48. [57]

    C. D. Wickens. Multiple Resources and Mental Workload. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3):449–455, June 2008. doi: 10.1518/001872008X288394 2

    2008 doi

  49. [58]

    C. D. Wickens. Multiple resources and mental workload. In Human Factors and Ergonomics Society Annual Meeting Proceedings, 2008. 3

    2008

  50. [59]

    G. Wu, J. Qian, S. C. Quispe, S. Chen, J. Rulff, and C. T. Silva. Artist: Automated text simplification for task guidance in augmented reality. In F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. O. T. Dugas, and I. Shklovski, eds., Proceedings of the CHI C...

    2024

  51. [60]

    M. S. Young, K. A. Brookhuis, C. D. Wickens, and P. A. Hancock. State of science: mental workload in ergonomics. Ergonomics, 58(1):1–17, Jan. 2015. doi: 10.1080/00140139.2014.956151 2

    2015

  52. [61]

    T. O. Zander, L. R. Krol, N. P. Birbaumer, and K. Gramann. Neu- roadaptive technology enables implicit cursor control based on me- dial prefrontal cortex activity. Proceedings of the National Academy of Sciences, 113(52):14898–14903, Dec. 2016. doi: 10.1073/pnas. 1605155114 3

    2016 doi

  53. [62]

    Z. Zeng, Y . Sun, X. Liu, Y . Jie, and Y . Zeng. Visual cognition-based op- timised design of primary flight displays in cockpits. The Aeronautical Journal, pp. 1–18, 2024. 4

    2024

  54. [63]

    Zhang, Y

    W. Zhang, Y . Liu, and D. B. Kaber. Effect of interface design on cognitive workload in unmanned aerial vehicle control. International Journal of Human-Computer Studies, 189:103287, Sept. 2024. doi: 10. 1016/j.ijhcs.2024.103287 2

    2024

  55. [64]

    Zheng, Y

    C. Zheng, Y . Wu, C. Shi, S. Ma, J. Luo, and X. Ma. Competent but rigid: Identifying the gap in empowering AI to participate equally in group decision-making. In A. Schmidt, K. V ¨a¨an¨anen, T. Goyal, P. O. Kristensson, A. Peters, S. Mueller, J. R. Williamson, and M. L. Wilson...

    2023

  56. [2017]

    doi: 10.1007/S10111-017-0421-7 2

    doi

  57. [2021]

    doi: 10.1145/3473856.3473860 3

Pith tools

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