fMRI reveals different activation patterns for real objects vs. photographs of objects
Bibliographic record
Abstract
Hundreds of functional magnetic resonance imaging (fMRI) experiments have revealed the neural substrates of object processing using photos of objects. Here we used univariate and multivariate pattern analysis (MVPA) of fMRI responses to determine whether photos are represented similarly to real objects in the human brain. The stimuli were everyday objects of comparable size and elongation. The photos were matched closely to the real objects for size and viewpoint. The stimuli were presented in rapid succession in the fMRI scanner using a custom-designed conveyor belt. We used a block design in which subjects viewed four exemplars (of different color, form, etc.) of one object type (e.g., whisks) in either real or photo format in each block. Univariate subtraction analysis revealed higher activation for real objects than photos in object-selective areas of the ventral visual stream (including the middle temporal gyrus, lateral occipital cortex, and fusiform gyrus) and dorsally in primary somatosensory cortex. Surprisingly, whole-brain searchlight representational similarity analysis showed that the lateral occipitotemporal cortex (LOTC) and anterior intraparietal sulcus (aIPS) were sensitive to the format in which stimuli were viewed, with stronger correlations for stimuli displayed in the same (i.e., Real-Real or Photo-Photo) versus different (i.e., Real-Photo) viewing dimensions. Finally, multidimensional scaling within independently-defined volumes of interest in left LOTC and aIPS revealed a global grouping that reflected a categorical distinction between real object and image displays, with more distinct representations for the real object exemplars than the images. Taken together, our results indicate that the human brain does not treat photographs as being equivalent to real objects. Real objects might elicit different brain-based responses to photos because they provide richer visual information, they have definite haptic qualities, and they afford genuine action. Meeting abstract presented at VSS 2016
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 | 0.000 |
| 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".