The Neural Correlate Of Size Constancy Measured With SSVEP In Virtual Reality
Bibliographic record
Abstract
When standing in a hallway and a person walks away from you, the retinal image of the person decreases, however, you still perceive them as being the same size. This is referred to as size constancy. If the retinal size were to remain constant as they get further we would perceive the person as getting larger. Previous findings from fMRI suggest that the perceived size of an object correlates with activation in V1 (Murray et al, 2006; Sperandio, et al., 2012). We explored how much the steady state visually evoked potential (SSVEP) would be modulated by the perceived size of an object relative to its retinal size. Participants viewed an environment presented in virtual reality (Oculus Rift) that had either strong distance cues (a hallway with stereo view), or limited distance cues (a featureless environment with monocular viewing). During a given trial participants saw an alternating black and white square flashing at 5hz at either 40cm or 80cm. The size of the near object increased and then decreased between 1.4 and 5.6cm over the course of 40 seconds. The sizes used for the far object were matched to the retinal sizes of the near object. At a fixed simulated distance, the amplitude of the SSVEP showed a strong dependence on the retinal size. At the same retinal size, the SSVEP amplitude was larger for the far distance compared to the near stimuli in the hallway environment. We conclude that the SSVEP over occipital cortex, presumable driven mainly by activity in V1, reflects the activation of size constancy mechanisms. ACKNOWLEDGEMENTS This study is supported by DFG IRTG 1901 and a research studentship from the NSERC CREATE program to MM. 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 | 0.004 |
| 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 | 0.001 |
| 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".