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Guiding Low Spatial Ability Individuals Through Visual Salience Cueing: The Dual Importance Of Where And When To Look

2016· article· en· W4389024310 on OpenAlexafffundabout
Victoria A. Roach, Graham Fraser, James H. Kryklywy, Derek Mitchell

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMental rotationPsychologyTest (biology)ApprehensionSpatial abilitySalience (neuroscience)AudiologyCognitive psychologyDevelopmental psychologyMedicineCognitionPsychiatry

Abstract

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Background Research in education suggests that spatial ability may be a predictor of student success in spatially complex disciplines, including anatomy. Many anatomical structures and systems interact both functionally, and spatially within the human body, and as a result, mastery of anatomy requires a comprehensive understanding of these spatial relationships. Debate exists regarding the malleability of spatial ability, yet some researchers demonstrate that spatial ability can be enhanced through various training protocols, typically requiring long‐term experience. It is proposed that the ability of spatially low‐performing individuals can be trained in a brief period of time through visual guidance. Methods Training consisted of a visual guidance protocol based on the visual apprehension patterns of high spatial individuals during the completion of an electronic Mental Rotations Test (EMRT) based on the line drawings of Shepherd and Metzler. The effects of visual guidance were evaluated using 33 low spatial individuals, as classified by the Vandenberg and Kuse Mental Rotation test in a counterbalanced crossover design. (Mean score = 5.1 ± 1.9 (SD); Mean Age = 25.9± 5.0 yrs; n=33, 24F: 9M) Individuals in the treatment groups (Early, or Late Training) were exposed to both a guided electronic mental rotation test (EMRT) and an unguided EMRT, while control group participants completed the unguided EMRT twice. Results All groups demonstrated an increase in EMRT scores on their second iteration of the test (F2, 30) = 28.29 (p<0.001); however an interaction was observed between treatment group and test iteration F(2, 30) = 4.22 (p = 0.024). The effect of visual guidance on EMRT scores was contingent on when the visual guidance was applied. When guidance was applied at the outset of training, the observed increase in scores were significantly greater than that expected based on repetition alone (Early Guidance: 6.91 ± 2.46 (95% CI) vs. Late Guidance: 2.82 ± 1.69 and No Guidance: 3.55 ± 1.43) (t(10) = 2.58, p = 0.028). Analysis of agreement in salience patterns revealed that Late Guidance agreed with exemplars in 22% of question (κ = 0.11) on the Unguided EMRT, and 37% on the Guided EMRT (κ =0.25). Early Guidance agreed with exemplars on 22% of questions (κ = 0.11) on the Unguided EMRT, and 44% on the Guided EMRT (κ =0.33). Controls agreed on 44% and 25% of questions (κ = 0.33 and 0.09) on both Unguided EMRTs. Summary The current findings suggest that by guiding low spatial individuals “where” to look during spatial problem solving at the outset of training, better search approaches may be adopted and improvements in overall spatial reasoning may be observed. It is proposed that visual guidance derived from eye tracking may be translated to improvements in spatially complex domains such as STEMM (Science, Technology, Engineering, Mathematics and Medicine), surgery, aeronautics and anatomy. Support or Funding Information This research was conducted with support from the Social Sciences and Humanities Research Council of Canada.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.233

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 studies0.0000.000
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 head0.258
Teacher spread0.238 · 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".

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Citations0
Published2016
Admission routes3
Has abstractyes

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