Reconstructing Spatiotemporal Trajectories of Visual Object Memories in the Human Brain
Bibliographic record
Abstract
How the human brain reconstructs, step-by-step, the core elements of past experiences is still unclear. Here, we map the spatiotemporal trajectories along which visual object memories are reconstructed during associative recall. Specifically, we inquire whether retrieval reinstates feature representations in a copy-like but reversed direction with respect to the initial perceptual experience, or alternatively, this reconstruction involves format transformations and regions beyond initial perception. Participants from two cohorts studied new associations between verbs and randomly paired object images, and subsequently recalled the objects when presented with the corresponding verb cue. We first analyze multivariate fMRI patterns to map where in the brain high- and low-level object features can be decoded during perception and retrieval, showing that retrieval is dominated by conceptual features, represented in comparatively late visual and parietal areas. A separately acquired EEG dataset is then used to track the temporal evolution of the reactivated patterns using similarity-based EEG-fMRI fusion. This fusion suggests that memory reconstruction proceeds from anterior frontotemporal to posterior occipital and parietal regions, in line with a conceptual-to-perceptual gradient but only partly following the same trajectories as during perception. Specifically, a linear regression statistically confirms that the sequential activation of ventral visual stream regions is reversed between image perception and retrieval. The fusion analysis also suggests an information relay to frontoparietal areas late during retrieval. Together, the results shed light onto the temporal dynamics of memory recall and the transformations that the information undergoes between the initial experience and its later reconstruction from memory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".