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Record W4390674690 · doi:10.1080/10400435.2023.2295262

User confidence and electrodermal activity during haptic exploration for perceptual comparisons using a robotic system

2024· article· en· W4390674690 on OpenAlexaff
Heather Capel, Thaiany Pedrozo Campos Antunes, Lina Becerra Puyo, Johana Paola Ríos Ortegón, Kim Adams

Bibliographic record

VenueAssistive Technology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Alberta
FundersHealth Research
KeywordsHaptic technologyPerceptionObject (grammar)Haptic perceptionHuman–computer interactionPsychologyTask (project management)ArousalComputer scienceSimulationArtificial intelligenceEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Children with physical impairments may have trouble effectively performing the hand movements used in haptic exploration and may miss information about object properties. Assistive robotic systems with haptic feedback may enable children with physical impairments to haptically explore objects. However, it is important to understand if they might be encountering difficulty in assessing object properties with the system. As such, we examined two methods to ascertain a user's uncertainty or stress when doing the exploration, user confidence and electrodermal activity (EDA). Twenty adults and ten children without physical impairments manipulated four pairs of objects to examine size, roughness, hardness and shape. All participants performed the manipulation by controlling a robotic system and by manual exploration in a randomized crossover design. Adults' confidence was lower when using the robotic system and correlated with lower accuracy at determining object properties. Children's responses indicated that they may not have understood how to self-rate confidence. EDA, a potentially objective measure of stress during the task, was actually lower for adults when they used the robotic system, suggesting less physiological arousal than when using their hands. Children's EDA was variable.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.553

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 science0.0000.000
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 head0.072
GPT teacher head0.323
Teacher spread0.252 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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