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Record W2151647141 · doi:10.1145/2145204.2145305

Investigating effects of visual and tactile feedback on spatial coordination in collaborative handheld systems

2012· article· en· W2151647141 on OpenAlexaff
Koji Yatani, Darren Gergle, Khai N. Truong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMobile deviceComputer scienceHuman–computer interactionWorkspaceVisual feedbackChannel (broadcasting)Visual spaceMultimediaComputer visionArtificial intelligencePsychologyComputer networkWorld Wide WebPerception

Abstract

fetched live from OpenAlex

Mobile and handheld devices have become platforms to support remote collaboration. But, their small form-factor may impact the effectiveness of the visual feedback channel often used to help users maintain an awareness of their partner's activities during synchronous collaborative tasks. We investigated how visual and tactile feedback affects collaboration on mobile devices, with emphasis on spatial coordination in a shared workspace. From two user studies, our results highlight different benefits of each feedback channel in collaborative handheld systems. Visual feedback can provide precise spatial information for collaborators, but degrades collaboration when the feedback is occluded, and sometimes can distract the user's attention. Spatial tactile feedback can reduce the overload of information in visual space and gently guides the user's attention to an area of interest. Our results also show that visual and tactile feedback can complement each other, and systems using both feedback channels can support better spatial coordination than systems using only one form of feedback.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.021
GPT teacher head0.294
Teacher spread0.274 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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