Attending to what and where: Background connectivity integrates category-based and spatial attention
Bibliographic record
Abstract
We recently demonstrated that attention to visual categories is associated with increased coupling between low-level visual areas selective for basic features and high-level areas selective for the attended category (Al-Aidroos, Said, & Turk-Browne, 2012, PNAS). For example, retinotopic area V4 coupled more with the fusiform face area (FFA) under face attention and the parahippocampal place area (PPA) under scene attention. Here, we investigate how spatial attention affects coupling, and how such modulation might interact with category-based coupling. Conventional neural measures often suggest that spatial and feature-based/category-based attention operate independently, so integrated changes in coupling may help to explain how people coordinate multiple attentional goals. Participants completed a combined space/category attention task in which they fixated centrally while viewing face images on one side of fixation and scene images on the other. Thus, across fMRI runs, they attended to left faces, right faces, left scenes, or right scenes. All images appeared in the upper visual field, projecting to the perceptually-dominant ventral stream. We used background connectivity to assess coupling: Stimulus-evoked responses and global noise were removed from the data, allowing analysis of the noise correlations between areas for the four attentional states. We found three main results. First, when attending to upper visual field images, FFA/PPA connectivity was enhanced for ventral V1-V3, but suppressed for dorsal V1-V3. Second, attending to images in one hemifield enhanced FFA/PPA connectivity with contralateral, more than ipsilateral, visual areas. These spatial attention results generalize our previous category findings, suggesting that modulation of coupling is a fundamental mechanism for top-down attention. Third, enhanced connectivity with task-relevant category areas was limited to task-relevant spatial areas (e.g., left-face attention enhanced FFA but not PPA connectivity, and only with right but not left V4). In this way, changes in coupling can support the integration of two distinct types of top-down attention. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".