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Predicting the upper bound of human path keeping performance using the two-point visual steering model: a comparison of four implementations

Chen Li et al.

Ergonomics2026https://doi.org/10.1080/00140139.2026.2650468article
AJG 3
Weight
0.50

What the paper says

Prior work showed that a three-step serial implementation of the two-point visual steering model in ACT-R can predict the upper bound of human path-keeping performance, but it is unknown whether the cognitive architecture or the number of processing steps drives this ability. This study compares four implementations: no cognitive architecture, ACT-R, and two variants of QN-MHP, each parameterised to minimise average path-keeping error without fitting to human data. Validation against humans reveals that implementations lacking a cognitive architecture or separate processing of the two visual points exceed human performance, failing to predict realistic upper bounds. Two-step serial processing within a cognitive architecture is necessary and sufficient to retain the predictive capabilities for an upper bound of human steering performance.

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https://doi.org/https://doi.org/10.1080/00140139.2026.2650468

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@article{chen2026,
  title        = {{Predicting the upper bound of human path keeping performance using the two-point visual steering model: a comparison of four implementations}},
  author       = {Chen Li et al.},
  journal      = {Ergonomics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/00140139.2026.2650468},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.