How Visual is Visual Prediction?
Bibliographic record
Abstract
Infants are readily able to use their recent experience to shape their future behavior. Recent work has confirmed that infants generate neural predictions based on their recent experience (Emberson, Richards, & Aslin, 2015) and that neural predictions trigger visual system activity similar to that elicited by visual stimulation. This study uses behavioral methods to ask, how visual is visual prediction? In Experiment 1, we confirmed that when additional trials provide additional visual experience with the experimental shape, infants exhibit a robust novelty preference. In Experiment 2, we removed the visual stimulus from some trials and presented the predictive auditory cue alone, allowing the effects of neural prediction to be assessed. We found no evidence of looking preferences at test, suggesting that visual prediction does not contribute to the computation of visual familiarity. In Experiment 3, we provided infants with a degraded visual stimulus to test whether visual prediction could bias visual perception under ambiguous conditions. Again, we found no evidence of looking preferences at test, suggesting that visual prediction is not biasing perception of an uncertain stimulus. Overall, our results suggest that visual prediction is not visual, in the strictest sense, despite the presence of visual system activation.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".