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Record W2151341374 · doi:10.1098/rstb.2011.0153

Haptic object perception: spatial dimensionality and relation to vision

2011· review· en· W2151341374 on OpenAlexaff
Roberta L. Klatzky, Susan J. Lederman

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2011
Typereview
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsQueen's University
Fundersnot available
KeywordsHaptic technologyPerceptionArtificial intelligenceComputer visionHaptic perceptionComputer scienceObject (grammar)Curse of dimensionalityCognitive neuroscience of visual object recognitionPsychologyNeuroscience

Abstract

fetched live from OpenAlex

Enabled by the remarkable dexterity of the human hand, specialized haptic exploration is a hallmark of object perception by touch. Haptic exploration normally takes place in a spatial world that is three-dimensional; nevertheless, stimuli of reduced spatial dimensionality are also used to display spatial information. This paper examines the consequences of full (three-dimensional) versus reduced (two-dimensional) spatial dimensionality for object processing by touch, particularly in comparison with vision. We begin with perceptual recognition of common human-made artefacts, then extend our discussion of spatial dimensionality in touch and vision to include faces, drawing from research on haptic recognition of facial identity and emotional expressions. Faces have often been characterized as constituting a specialized input for human perception. We find that contrary to vision, haptic processing of common objects is impaired by reduced spatial dimensionality, whereas haptic face processing is not. We interpret these results in terms of fundamental differences in object perception across the modalities, particularly the special role of manual exploration in extracting a three-dimensional structure.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.198
GPT teacher head0.362
Teacher spread0.163 · 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 designOther design
Domainnot available
GenreReview

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

Citations63
Published2011
Admission routes1
Has abstractyes

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