Goal-directed attention alters the tuning of object-based representations in extrastriate cortex
Bibliographic record
Abstract
Humans survive in environments that contain a vast quantity and variety of visual information. All items of perceived visual information must be represented within a limited number of brain networks. The human brain requires mechanisms for selecting only a relevant fraction of perceived information for more in-depth processing, where neural representations of that information may be actively maintained and utilized for goal-directed behavior. Object-based attention is crucial for goal-directed behavior and yet remains poorly understood. Thus, in the study we investigate how neural representations of visual object information are guided by selective attention. The magnitude of activation in human extrastriate cortex has been shown to be modulated by attention; however, object-based attention is not likely to be fully explained by a localized gain mechanism. Thus, we measured information coded in spatially distributed patterns of brain activity with fMRI while human participants performed a task requiring selective processing of a relevant visual object category that differed across conditions. Using pattern classification and spatial correlation techniques, we found that the direction of selective attention is implemented as a shift in the tuning of object-based information representations within extrastriate cortex. In contrast, we found that representations within lateral prefrontal cortex (PFC) coded for the attention condition rather than the concrete representations of object category. In sum, our findings are consistent with a model of object-based selective attention in which representations coded within extrastriate cortex are tuned to favor the representation of goal-relevant information, guided by more abstract representations within lateral PFC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".