Viewpoint adaptation revealed potential representational differences between 2D images and 3D objects
Bibliographic record
Abstract
For convenience and experimental control, cognitive science has relied largely on images as stimuli rather than the real, tangible objects encountered in the real world. Recent evidence suggests that the cognitive processing of images may differ from real objects, especially in the processing of spatial locations and actions, thought to be mediated by the dorsal visual stream. Perceptual and semantic processing in the ventral visual stream, however, has been assumed to be largely unaffected by the realism of objects. Several studies have found that one key difference accounting for differences between real objects and images is actability; however, less research has investigated another potential difference - the three-dimensional nature of real objects as conveyed by cues like binocular disparity. To investigate the extent to which perception is affected by the realism of a stimulus, we compared viewpoint adaptation when stimuli (a face or a kettle) were 2D (flat images without binocular disparity) vs. 3D (i.e., real, tangible objects or stereoscopic images with binocular disparity). For both faces and kettles, adaptation to 3D stimuli induced stronger viewpoint aftereffects than adaptation to 2D images when the adapting orientation was rightward. A computational model suggested that the difference in aftereffects could be explained by broader viewpoint tuning for 3D compared to 2D stimuli. Overall, our finding narrowed the gap between understanding the neural processing of visual images and real-world objects by suggesting that compared to 2D images, real and simulated 3D objects evoke more broadly tuned neural representations, which may result in stronger viewpoint invariance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".