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Record W2947368108 · doi:10.22215/etd/2018-13292

A Biologically Plausible Neuron Model of Mental Rotation

2018· dissertation· en· W2947368108 on OpenAlexaff
Sean N. Riley

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsCarleton University
Fundersnot available
KeywordsMental rotationRotation (mathematics)Artificial intelligenceComputer scienceRepresentation (politics)Variety (cybernetics)Mental representationTask (project management)Pattern recognition (psychology)Computer visionNeurosciencePsychologyCognitionEngineering

Abstract

fetched live from OpenAlex

This thesis presents a biologically plausible account of mental rotation.To this end, there is evidence that mental rotation is a spatial imagery task that can invoke a variety of strategies, depending on the nature of the stimuli.This thesis uses simple but unfamiliar stimuli, which engenders a continuous, whole-unit rotation.The model is comprised of 43,000 simulated neurons spread across a variety of neuron ensembles.These ensembles work together to form a neuronal representation of the spatial map entailed by the stimuli, then rotates that spatial map into a series of new orientations according to simulated movement along an intended axis of rotation.Two sets of simulations were run: one focusing on the biological accuracy of spatial maps, with the second focusing on the biological accuracy of neurons.Overall, both sets of simulations were able to re-produce the reaction times found in behavioural studies of mental rotation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.252
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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