MétaCan
Menu
Back to cohort
Record W2980737326 · doi:10.1111/nyas.14261

Allocentric representations for target memory and reaching in human cortex

2019· review· en· W2980737326 on OpenAlexafffund
Ying Chen, J. Douglas Crawford

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2019
Typereview
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsYork UniversityQueen's University
FundersCanada Research Chairs
KeywordsPsychologyNeurosciencePrecuneusPosterior parietal cortexPremotor cortexCognitive psychologyFunctional magnetic resonance imagingDorsum

Abstract

fetched live from OpenAlex

The use of allocentric cues for movement guidance is complex because it involves the integration of visual targets and independent landmarks and the conversion of this information into egocentric commands for action. Here, we focus on the mechanisms for encoding reach targets relative to visual landmarks in humans. First, we consider the behavioral results suggesting that both of these cues influence target memory, but are then transformed-at the first opportunity-into egocentric commands for action. We then consider the cortical mechanisms for these behaviors. We discuss different allocentric versus egocentric mechanisms for coding of target directional selectivity in memory (inferior temporal gyrus versus superior occipital gyrus) and distinguish these mechanisms from parieto-frontal activation for planning egocentric direction of actual reach movements. Then, we consider where and how the former allocentric representations of remembered reach targets are converted into the latter egocentric plans. In particular, our recent neuroimaging study suggests that four areas in the parietal and frontal cortex (right precuneus, bilateral dorsal premotor cortex, and right presupplementary area) participate in this allo-to-ego conversion. Finally, we provide a functional overview describing how and why egocentric and landmark-centered representations are segregated early in the visual system, but then reintegrated in the parieto-frontal cortex for action.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.279
GPT teacher head0.424
Teacher spread0.145 · 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 designNot applicable
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

Citations27
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueAnnals of the New York Academy of SciencesSame topicMotor Control and AdaptationFrench-language works237,207