Feedforward attentional selection in sensory cortex
Bibliographic record
Abstract
Salient objects stand out (pop-out) from their surroundings, grabbing our attention. Whether this phenomenon is a consequence of bottom-up sensory processing or predicated on top-down influence is debated. We show that the neural computation of attentional pop-out is embedded in the earliest cortical sensory response, seemingly void of feedback from higher-level areas. We measured synaptic and spiking activity across cortical columns in mid-level area V4 of monkeys searching for an attention-grabbing stimulus. Indexed by reaction times and behavioral accuracy, attention was captured at variable times. This moment of attentional capture occurred within the earliest feedforward response, both in terms of timing and spatial location. Moreover, the magnitude of the earliest sensory response predicted reaction times. Crucially, errant attentional selection and consequent behavior was associated with errant selection in sensory cortex. Together, these findings demonstrate a dominant role for feedforward activation of sensory cortex for dictating attentional priority and subsequent behavior. In brief Why do certain objects stand out from their surroundings and seemingly grab our attention? In this study, Westerberg et al. determine that attentional selection for salient objects in our environment is computed in sensory cortex as soon as sensory information arrives. Highlights Early sensory responses in V4 predict attentional selection and behavioral responses Errant attentional selection in sensory cortex precedes errant behavior Tonic modulation of sensory cortex can regulate attentional selection
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".