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Record W1926237779 · doi:10.1017/cbo9780511610448.013

Multimedia Learning: GuidingVisuospatial Thinking with Instructional Animation

2005· book-chapter· en· W1926237779 on OpenAlex
Richard E. Mayer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge University Press eBooks · 2005
Typebook-chapter
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCognitive psychologySpatial abilityPerceptionPsychologyAnimationCognitionWorking memoryVisual perceptionLateralization of brain functionSpatial memoryVariety (cybernetics)Computer scienceNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter discusses sex differences that are found in a variety of tests of visuospatial abilities ranging from standardized paper-and-pencil or computerized tasks to tests of way-finding ability and geographical knowledge. Visuospatial information processing involves interplay of multiple cognitive processes, including visual and spatial sensation and perception, a limited capacity visuospatial working memory, and longer-term memories where visual and spatial information may be encoded in many ways. Certain visuospatial and mathematical abilities are related, and visuospatial sex differences have been suggested to contribute to observed sex differences in mathematics performance. Many cultures show similar patterns of visuospatial sex differences, a finding that seems to support theories based on the principles of evolutionary psychology. The chapter explores how factors rooted in biology, specifically the what-where visual systems, hemispheric lateralization, and exposure to sex steroid hormones, may relate to visuospatial skill and to sex differences in those abilities.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.960
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.013
GPT teacher head0.179
Teacher spread0.166 · 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