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Record W2296532009 · doi:10.1080/17470218.2016.1158301

Spatial habit competes with effort to determine human spatial organization

2016· article· en· W2296532009 on OpenAlexafffund
Mona J. H. Zhu, Evan F. Risko

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2016
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsFlinders University
KeywordsHabitSpatial organizationCognitive psychologyPsychologyCommunicationSocial psychologyBiologyEcology

Abstract

fetched live from OpenAlex

Despite the important role that the physical environment plays in shaping human cognition, few studies have endeavoured to experimentally examine the principles underlying how individuals organize objects in their space. The current investigation examines the idea that humans organize objects in their space in order to minimize effort or maximize performance. We devised a novel spatial organization task whereby participants freely arranged objects in the context of a writing task. Critically, we manipulated the frequency with which each object was used and assessed participants' spontaneous placements. In the first set of experiments, participants showed a counterintuitive tendency to match pen pairs with their initial placements rather than placing pens in the less effortful configuration. However, in Experiment 2, where the difference in physical effort between different locations was increased, participants were more likely to reorganize the pens into the less effortful configuration. We begin developing a theory of human spatial organization wherein the observed initial bias may represent a kind of spatial habit formation that competes with effort/performance considerations to shape future spatial organization.

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 score0.079
Threshold uncertainty score0.740

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)0.0010.000

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 head0.012
GPT teacher head0.277
Teacher spread0.265 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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