Vivid imagery of objects primes perception of subliminal spatial information
Bibliographic record
Abstract
The integration of imagination with perception of reality is associated with numerous neurocognitive and biological adaptive functions. Given the overlap between neural processes and regions governing internally versus externally generated imagery, the interaction between these processual components of high-level vision has been studied for over a century, without yielding a satisfactory account. Opposite to traditional theories like the Perky effect or sensory boost, we hypothesized that voluntary conscious mental imagery of an object enhances the processing of unconscious incoming visual spatial information. Hence, aspects of internal imagery such as vividness or motion should drive such an interaction. We probed how subliminal spatial information might be influenced under imagery conditions. Using behavioural data, we show that imagery improves the unconscious concurrent perception of visual spatial information. This priming effect seems driven by the gradient of imagery vividness: both explicitly required by task demands and implicitly generated. We found that imagery can direct visual perception when the visual system is strongly biased towards predicting that an object is present. Because the observed results vary systematically with self-reported vividness, rather than being epiphenomenal, the subjective experience of vividness is a deterministic condition for imagery priming.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".