Top-down modulation in the infant brain: Learning-induced expectations rapidly affect the sensory cortex at 6 months
Bibliographic record
Abstract
Recent theoretical work emphasizes the role of expectation in neural processing, shifting the focus from feed-forward cortical hierarchies to models that include extensive feedback (e.g., predictive coding). Empirical support for expectation-related feedback is compelling but restricted to adult humans and nonhuman animals. Given the considerable differences in neural organization, connectivity, and efficiency between infant and adult brains, it is a crucial yet open question whether expectation-related feedback is an inherent property of the cortex (i.e., operational early in development) or whether expectation-related feedback develops with extensive experience and neural maturation. To determine whether infants' expectations about future sensory input modulate their sensory cortices without the confounds of stimulus novelty or repetition suppression, we used a cross-modal (audiovisual) omission paradigm and used functional near-infrared spectroscopy (fNIRS) to record hemodynamic responses in the infant cortex. We show that the occipital cortex of 6-month-old infants exhibits the signature of expectation-based feedback. Crucially, we found that this region does not respond to auditory stimuli if they are not predictive of a visual event. Overall, these findings suggest that the young infant's brain is already capable of some rudimentary form of expectation-based feedback.
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 | 0.001 |
| 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".