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Record W2126938811 · doi:10.1037/cjep2007024

Haptic face processing.

2007· article· en· W2126938811 on OpenAlexafffund
Susan J. Lederman, Andrea R. Kilgour, Ryo Kitada, Roberta L. Klatzky, Cheryl Hamilton

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2007
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of ManitobaQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsHaptic technologyPsychologyCognitive psychologyPerceptionNeuroimagingNeuropsychologyIdentity (music)Facial expressionFace (sociological concept)Haptic perceptionFace perceptionCognitive scienceCommunicationComputer scienceCognitionArtificial intelligenceNeuroscienceAesthetics

Abstract

fetched live from OpenAlex

We present an overview of a new multidisciplinary research program that focuses on haptic processing of human facial identity and facial expressions of emotion. A series of perceptual and neuroscience experiments with live faces and/or rigid three-dimensional facemasks is outlined. To date, several converging methodologies have been adopted: behavioural experimental studies with neurologically intact participants, neuropsychological behavioural research with prosopagnosic individuals, and neuroimaging studies using fMRI techniques. In each case, we have asked what would happen if the hands were substituted for the eyes. We confirm that humans can haptically determine both identity and facial expressions of emotion in facial displays at levels well above chance. Clearly, face processing is a bimodal phenomenon. The processes and representations that underlie such patterns of behaviour are also considered.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0150.004

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 head0.083
GPT teacher head0.351
Teacher spread0.268 · 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 designObservational
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

Citations12
Published2007
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentaleSame topicFace Recognition and PerceptionFrench-language works237,207