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Record W2035930689 · doi:10.1152/jn.00396.2012

Reframing the action and perception dissociation in DF: haptics matters, but how?

2012· article· en· W2035930689 on OpenAlexafffund
Robert L. Whitwell, Gavin Buckingham

Bibliographic record

VenueJournal of Neurophysiology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyCognitive psychologyPerceptionAction (physics)GestureDorsumDissociation (chemistry)Embodied cognitionCognitionCognitive scienceCommunicationNeuroscienceArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Goodale and Milner's (1992) "vision-for-action" and "vision-for-perception" account of the division of labor between the dorsal and ventral "streams" has come to dominate contemporary views of the functional roles of these two pathways. Nevertheless, some lines of evidence for the model remain controversial. Recently, Thomas Schenk reexamined visual form agnosic patient DF's spared anticipatory grip scaling to object size, one of the principal empirical pillars of the model. Based on this new evidence, Schenk rejects the original interpretation of DF's spared ability that was based on segregated processing of object size and argues that DF's spared grip scaling relies on haptic feedback to calibrate visual egocentric cues that relate the posture of the hand to the visible edges of the goal-object. However, a careful consideration of the tasks that Schenk employed reveals some problems with his claim. We suspect that the core issues of this controversy will require a closer examination of the role that cognition plays in the operation of the dorsal and ventral streams in healthy controls and in patient DF.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.017
Scholarly communication0.0040.019
Open science0.0020.003
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.275
Teacher spread0.236 · 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

Citations26
Published2012
Admission routes2
Has abstractyes

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