fMRI response patterns in human somato-motor cortex predict memory advantage for real objects versus their images
Bibliographic record
Abstract
Real objects are more memorable than two-dimensional (2-D) images of the same items, a phenomenon known as the "Real Object Memory Advantage", or ROMA (Snow et al., 2014). Although emerging evidence indicates that real objects are processed differently to images because they afford physical interaction (Gomez, Skiba and Snow, in press), little is known about the underlying mechanism for the ROMA. Here, we used fMRI to identify brain areas that decode, at the time of recollection, the format in which an object was displayed during encoding. Participants first completed a behavioral learning task in which they were asked to remember a large set of everyday household objects. Half of the stimuli were presented as real-world objects; the other half were 2-D images of objects presented on a computer monitor. The images were matched closely to their real-world counterparts for size, apparent distance, viewpoint, background, and illumination, and all stimuli were presented within reach. Participants later completed a recognition task in the MRI scanner. During each scan, participants viewed text descriptors (e.g., 'hammer') and were asked to decide whether each item was viewed as a real object, a 2-D image, or was not viewed at all, during the study phase. Overall, most observers showed superior memory performance for items previously viewed as real objects versus 2-D images, consistent with earlier findings (Snow et al., 2014). Critically, searchlight multivariate pattern analysis (MVPA) of the fMRI data revealed that motor and somatosensory areas in parietal cortex (regions involved during grasping and somatosensation), but not ventral visual areas (regions involved in object perception), were able to decode stimulus format, even though participants did not interact manually with any of the stimuli during the study phase. These results suggest that the ROMA is due to re-activation of dorsal somato-motor networks at the time of retrieval. Meeting abstract presented at VSS 2018
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".