Spatial and feature-selective attention have distinct effects on population-level tuning
Bibliographic record
Abstract
Abstract Attention is a fundamental brain process by which we selectively prioritize relevant information in our environment. Cognitively, we can employ different methods for selecting visual information for further processing, but the extent to which these are implemented by similar or different neural processes remains unclear. Spatial and feature-selective attention both change the stimulus related information signaled by single-cells and neural populations, but relatively few studies have directly compared the effects of these distinct types of attention. We scanned participants (n=20) using MEG, while they covertly attended to an object on the left or the right of fixation (spatial attention manipulation) and reported the object’s shape or color (feature-selective attention manipulation). We used multivariate pattern classification to measure population stimulus-coding in occipital and frontal areas, for attended and non-attended stimulus features, at attended and non-attended locations. In occipital cortex, we show that both spatial and feature-selective attention enhanced object representations, and the effects of these two attention types interacted multiplicatively. We also found that spatial and feature-selective attention induced qualitatively different patterns of enhancement in occipital cortex for the encoding of stimulus color. Specifically, feature-based attention primarily enhanced small color differences, while spatial attention produced greater enhancement for larger differences. We demonstrate that principles of response-gain and tuning curve sharpening that have been applied to describe the effects of attention at the level of a single neuron can account for these differences. An information flow analysis suggested that these attentional effects may be driven by feedback from frontal areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".