MEG activity in visual areas of the human brain during target selection and sustained attention
Bibliographic record
Abstract
We combined MEG and magnetic resonance imaging (MRI) to examine evoked activity in visual areas during a task that involves both target selection and sustained attention. During task trials, 9 human subjects were presented with two white moving random dot patterns (RDPs, the target and the distractor), left and right of a central fixation spot on a dark background. After a brief delay, each RDP color changed to red, blue, or green. Subjects were required to select the target using a color rank selection rule (red > blue > green), sustain attention to it, and identify a transient change in either its direction (clockwise/counterclockwise) or color (pink/grey). All possible stimulus configurations were presented randomly. We found that following color cue onset, early visual areas along the cuneus and lingual gyrus (V1 and V2) were activated bilaterally starting as early as 120 ms after cue onset. Activation in other areas such as V3, V3A, and V4 was significantly stronger contralateral to the target stimulus, peaking at ∼170 ms from color cue onset. These data demonstrate that target selection becomes evident in early visual cortex with a latency of about 170 ms following cue onset. During the sustained attention period, changes in the direction of the RDPs evoked peak activation in contralateral area MT (Talairach: -40/-64/16; 39/-64/17), while changes in color evoked activity in contralateral areas V2/V3 (-21/-74/11; 27/-67/5). These activations were stronger (∼60%) for targets than for distracters, becoming most pronounced at ∼180 ms from change onset. Our results reveal that MEG activity in early visual areas of the human brain reflects target selection, as well as the effects of sustaining attention on that stimulus. This may be the result of interactions of feed-forward and feedback signals originated in different areas of the hierarchy of visual processing.
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 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 | 0.000 |
| 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".