MétaCan
Menu
Back to cohort

Do Our Hands See What Our Eyes See: Investigating the Relationships Between Spatial and Haptic Abilities

2022· article· en· W4225419647 on OpenAlexaff
Michelle A. Sveistrup, J Langlois, Timothy D. Wilson

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsHaptic technologyMental rotationSightTest (biology)ComprehensionPsychologyCognitionGazeCognitive psychologyComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Introduction Spatial abilities (SA) are the cognitive ability to manipulate mental images of objects to problem solve and are linked to comprehension of three‐dimensional (3D) spatial knowledge in gross anatomy and in learning clinical procedures. Similarly, haptic abilities (HA) refer to translating tactile information from the immediate environment into mental images and are involved in handling anatomical specimens and technical skill acquisition common to clinicians. Both abilities independently play an important role in learning anatomy, yet the relationships between SA and HA are unknown. The objective of this study is to explore SA‐HA interactions. Methods The Mental Rotations Test (MRT) and the newly developed haptic abilities test (HAT) respectively quantified SA and HA in undergraduate students. The HAT, modelled after the MRT, consisted of untimed, matching questions utilizing MRT‐styled 3D handheld wooden objects (~10cm3) under three sensory conditions using sight (S), haptics (H) and the combination of both sight and haptics (SH). While HAT condition scores and response times determined accuracy, video recorded subject gaze and haptic behaviours on a question‐by‐question basis. Scores for MRT are presented as mean ± SD and HAT scores as median (Q1‐Q3). Results Subjects (n=16, 10F, 20‐28yrs) completed the MRT and HAT and were categorized into high (n=9, 4F, MRT: 14.8±3) and low (n=7, 6F, MRT: 6.3±3) (H/LSA) groups ( p<0.0001 ). Scores obtained in the H condition [14 (13‐15) vs 14 (14‐14), p=0.0031 ] were significantly lower than in the S [15 (15‐15) vs 14 (14‐15)] and SH conditions [15 (15‐15) vs 14 (14‐15)] in H/LSA groups respectively. Spearman’s correlation coefficient illustrated MRT scores were moderately related in the S condition (r=0.506, p=0.038 ), but unrelated in the SH and H conditions (SH: r=0.325, p=0.203 ; H: r=0.162, p=0.534 ). The number of pupillary fixations per question was similar (S:6.0±3.0 vs 5.9±1.4, p=0.910 ; SH: 4.5±2.0 vs 6.7±2.0, p=0.099 ) for H/LSA groups respectively. Initial descriptive analyses of hand behaviours (n=4) suggest H/LSA objects differently. Discussion Despite significant difference in SA, when individuals are presented identical environmental 3D objects to view, view and touch, or touch alone, it is difficult to differentiate SA. These data suggest that the addition of haptic sensory information may aid LSA individuals completing spatial tasks. Although visual attention and timing of haptic behaviours may be similar between H/LSA subjects, further kinematic analysis of haptic strategies may be more meaningful to isolate how somatosensory inputs aid problem solving behaviours in LSA persons. This is some of the first data to simultaneously measure hand and gaze behaviour during spatially challenging tasks in individuals with divergent SA. Given recent migrations to online anatomy teaching and learning environments due to COVID‐19, understanding learner behaviour when sensory inputs, like haptics, are removed is valuable evidence that can inform future anatomy curriculum and resource development.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.033
GPT teacher head0.247
Teacher spread0.214 · 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 designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicAnatomy and Medical TechnologyFrench-language works237,207