Using imagination and the contents of memory to create new scene and object representations: A functional MRI study
Bibliographic record
Abstract
Humans can use the contents of memory to construct scenarios and events that they have not encountered before, a process colloquially known as imagination. Much of our current understanding of the neural mechanisms mediating imagination is limited by paradigms that rely on participants' subjective reports of imagined content. Here, we used a novel behavioral paradigm that was designed to systematically evaluate the contents of an individual's imagination. Participants first learned the layout of four distinct rooms containing five wall segments with differing geometrical characteristics, each associated with a unique object. During functional MRI, participants were then shown two different wall segments or objects on each trial and asked to first, retrieve the associated objects or walls, respectively (retrieval phase) and then second, imagine the two objects side-by-side or combine the two wall segments (imagination phase). Importantly, the contents of each participant's imagination were interrogated by having them make a same/different judgment about the properties of the imagined objects or scenes. Using univariate and multivariate analyses, we observed widespread activity across occipito-temporal cortex for the retrieval of objects and for the imaginative creation of scenes. Interestingly, a classifier, whether trained on the imagination or retrieval data, was able to successfully differentiate the neural patterns associated with the imagination of scenes from that of objects. Our results reveal neural differences in the cued retrieval of object and scene memoranda, demonstrate that different representations underlie the creation and/or imagination of scene and object content, and highlight a novel behavioral paradigm that can be used to systematically evaluate the contents of an individual's imagination.
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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 | 0.002 |
| 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 | 0.001 |
| 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".