Get ready! Mental alertness enhances perceptual processing and visual awareness
Bibliographic record
Abstract
A growing body of research highlights that our ability to predict future events shapes our subjective experience of the world. For example, recent studies show that phasic alertness – i.e., increased response preparation and heightened attention following a warning signal – influences perceptual awareness. Following this research trajectory, we investigated how mental alertness and response preparation interface with visual awareness. In particular, our goal was to unravel the specific influences of pre-stimulus processes involved in response preparation over visual awareness. To that end, participants completed a target discrimination task where we combined a temporal cueing approach with a backward masking strategy, while we recorded brain activity using 64 channels electroencephalography. A temporally predictive cue preceded the target event for half of trials, thereby allowing participants to reliably estimate the latency of the target event. For each trial, participants provided an objective response, where they indicated the orientation of the target (left vs. right), as well as a subjective judgment about its visibility (seen vs. unseen). Our results show that heightened mental alertness benefits both performance (i.e., improved ideomotor response and perceptual sensitivity) and perceptual awareness (i.e., increased reports of visibility). At the neural level, cueing prompted opposite effects over the magnitude of power in frontal theta and occipital alpha oscillations, two neural responses that likely index discrete brain processes linked to response preparation and mental alertness. Consistent with our behavioral results, findings also revealed that increased alertness modulated the amplitude of the P3b, an event-related potential linked to perceptual awareness. Finally, using a computational model, we found that these effects likely reflect the influence of alertness over the rate of perceptual evidence accumulation, thereby implying that alertness primarily influence awareness through perceptual processes. Meeting abstract presented at VSS 2018
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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".