The impact of culture on the processing of spatial frequencies during the recognition of homogenous objects
Bibliographic record
Abstract
Several studies have shown cultural differences in the fixation patterns observed during tasks of different nature, like face identification (Blais et al., 2008) and object recognition (Kelly et al., 2010). The general pattern of findings suggests that East Asians rely more on peripheral processing and deploy their attention more broadly than Westerners (Miellet et al.,2013). In line with this, studies have shown that East Asians process faces in lower spatial frequencies (SF) than Westerners (Tardif et al., 2017). However, it is not clear if this cultural difference in SF processing is specific to faces. In fact, it has not been found during the processing of scenes and non-homogenous objects (Blais et al., 2018). Compared with scenes and most everyday objects, faces have the property of having homogeneous configurations. The present study thus verified if a cultural difference in SF tunings occurs while processing homogeneous objects : Greebles (Gauthier & Tarr, 1997). We tested 121 participants who were born in Western or East Asians countries.The online study consisted of 600 trials of a same-different task, using the SF bubbles method (Willenbockel et al.,2010). One-sample t-tests (Pixel test from the Stat4CI; Chauvin et al., 2005) indicate that SF ranging between 2 and 11 cycles/object (tcrit=3.89, p<.05), and between 3 and 16 cycles/object (tcrit=3.53, p<.05) were used by East Asians and Westerners, respectively. We generated 1000 bootstrap samples to compare the SF used by both cultural groups, and found that low SF ranging between 1 and 3 cycles/object were significantly more used by East Asians (p<.025). Our results suggest that cultural differences in the processing of SF can be generalized to other objects sharing similar configurations. Future studies will aim to understand the source of these differences.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".