Uncovering neural-based visual-orthographic representations from mental imagery
Bibliographic record
Abstract
Clarifying the neural and representational basis of mental imagery has elicited significant interest in the study of visual recognition. Recently, numerous attempts have been directed at uncovering the structure and the content of visual imagery. However, these attempts have mostly targeted simple visual features (e.g., orientations, shapes, or single letters), limiting the theoretical and practical implications of this research. To address these limitations, the current study aimed to decode and to reconstruct the appearance of single words from mental imagery with the aid of functional magnetic resonance imaging (fMRI). We collected fMRI data from 13 healthy right-handed adults while they passively viewed or mentally imagined the appearance of three-letter concrete nouns with a consonant-vowel-consonant structure. Consistent with previous findings, multivariate analyses demonstrated that pairs of words can be discriminated from neural patterns when words are viewed and, also, when they are imagined. However, decoding relied more extensively on early visual areas in the former case, for perception, and more extensively on higher-level visual areas, such as the visual word form area (vWFA), in the latter case, for imagery. To assess and to visualize the representational content underlying successful decoding, imagery-based image reconstruction was conducted by mapping the neural patterns of visual words during imagery onto a representational feature space extracted from neural signals during perception. This analysis revealed successful levels of imagery-based image reconstruction for single words in the early visual cortex as well as in the vWFA. Thus, our findings speak to overlapping neural representations between imagery and perception, both in low-level visual areas and higher-order visual cortex. Further, they shed light on the fine-grained neural representations of visual-orthographic information during mental imagery.
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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 | 0.001 | 0.001 |
| 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".