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Record W2891337590 · doi:10.1101/413815

Transsaccadic feature interactions in multiple reference frames: an fMRIa study

2018· preprint· en· W2891337590 on OpenAlexaff
Bianca R. Baltaretu, Benjamin T. Dunkley, Simona Monaco, Ying Chen, J. Douglas Crawford

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsQueen's UniversitySickKids FoundationYork University
Fundersnot available
KeywordsSupramarginal gyrusNeurosciencePsychologySaccadic maskingPosterior parietal cortexPremotor cortexMiddle frontal gyrusSaccadeSuperior frontal gyrusIntraparietal sulcusFrontal eye fieldsStimulus (psychology)Extrastriate cortexMiddle temporal gyrusVisual cortexEye movementCognitive psychologyCognitionAnatomyFunctional magnetic resonance imagingBiology

Abstract

fetched live from OpenAlex

Abstract Transsaccadic integration of visual features can operate in various frames of reference, but the corresponding neural mechanisms have not been differentiated. A recent fMRIa (adaptation) study identified two cortical regions in supramarginal gyrus (SMG) and extrastriate cortex that were sensitive to transsaccadic changes in stimulus orientation (Dunkley et al., 2016). Here, we modified this paradigm to identify the neural correlates for transsaccadic comparison of object orientations in: 1) Spatially Congruent (SC), 2) Retinally Congruent (RC) or 3) Spatially Incongruent (SI)) coordinates. Functional data were recorded from 12 human participants while they observed a grating (oriented 45° or 135°) before a saccade, and then judged whether a post-saccadic grating (in SC, RC, or SI configuration) had the same or different orientation. Our analysis focused on areas that showed a significant repetition suppression (Different > Same) or repetition enhancement (Same > Different) BOLD responses. Several cortical areas were significantly modulated in all three conditions: premotor/motor cortex (likely related to the manual response), and posterior-middle intraparietal sulcus. In the SC condition, uniquely activated areas included left SMG and left lateral occipitotemporal gyrus (LOtG). In the RC condition, unique areas included inferior frontal gyrus and the left lateral BA 7. In the SI condition, uniquely activated areas included the frontal eye field, medial BA 7, and right LOtG. Overall, the SC results were significantly different from both RC and SI. These data suggest that different cortical networks are used to compare pre- and post-saccadic orientation information, depending on the spatial nature of the task. Significance Statement Every time one makes a saccade, the brain must compare and integrate stored visual information with new information. It has recently been shown that ‘transsaccadic integration’ of visual object orientation involves specific areas within parietal and occipital cortex (Dunkley et al., 2016). Here, we show that this pattern of cortical activation also depends on the spatial nature of the task: when the visual object is fixed relative to space, the eye, or relative to neither space nor the eye, different frontal, parietal, and occipital regions are engaged. More generally, these findings suggest that different aspects of trans-saccadic integration flexibly employ different cortical networks.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.325
Teacher spread0.239 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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