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Record W4415958764 · doi:10.1016/j.apergo.2025.104680

Design of 2D Fitts’ law experiments: An odd thing about targets

2025· article· en· W4415958764 on OpenAlexaff
Maria Francesca Roig-Maimó, Ramon Mas-Sansó, I. Scott MacKenzie

Bibliographic record

VenueApplied Ergonomics · 2025
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsYork University
FundersMinisterio de Ciencia, Innovación y Universidades
KeywordsTask (project management)Test (biology)Movement (music)ThroughputState (computer science)

Abstract

fetched live from OpenAlex

We explored the ISO 9241-411 multi-directional tapping (2D) test for evaluating computer pointing techniques. Based on Fitts' law, the test evenly distributes targets around a circular layout and requires the same movement distance between targets for all trials in a sequence. We demonstrate that this is only possible when using an odd number of targets. We reviewed 200 research papers using the 2D task and found that about a third of them use an even number of targets, and consequently report incorrect values for throughput, reinforcing the need to advertise this overlooked but relevant issue. Our findings imply that adhering to an odd number of targets and to the correct definition of movement distance ensures methodological consistency, leading to valid throughput values. Consequently, we argue for revisiting and updating the ISO 9241-411 standard to explicitly state that an odd number of targets is required for the 2D task.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.273
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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