Development of the mirror-image sensitivity for different object categories—Evidence from the mirror costs of object images in children and adults
Bibliographic record
Abstract
Object recognition relies on a multitude of factors, including size, orientation, and so on. Mirrored orientation, particularly due to children's mirror confusion in reading, holds special significance among various object orientations. Brain imaging studies suggest that the visual ventral and dorsal streams exhibit distinct orientation sensitivity across diverse object categories. Yet, it remains unclear whether mirror orientation sensitivity also varies among these categories during development at the behavioral level. Here, we explored the mirror sensitivity of children and adults across five distinct categories, which encompass tools that activate both the visual ventral stream for function information and the dorsal stream for manipulation information, and animals and faces that mainly activate the ventral stream. Two types of symbols, letters and Chinese characters, were also included. Mirror sensitivity was assessed through mirror costs-that is, the additional reaction time or error rate in the mirrored versus the same orientation condition when judging the identity of object pairs. The mirror costs in reaction times and error rates consistently revealed that children exhibited null mirror costs for tools, and the mirror costs for tools in adults were minimal, if any, and were smaller than those for letters and characters. The mirror costs reflected in absolute reaction time and error rate were similar across adults and children, but when the overall difference in reaction times was considered, adults showed a larger mirror cost than children. Overall, our investigation unveils categorical distinctions and development in mirror sensitivity of object recognition across the ventral and dorsal streams.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".