Influence of Ethnicity, Gender and Answering Mode on a Virtual Point-to-Origin Task
Bibliographic record
Abstract
In a virtual point-to-origin task, participants seem to show different response patterns and underlying strategies for orientation, such as "turner" and "non-turner" response patterns. Turners respond as if succeeding to update simulated heading changes, and non-turners respond as if failing to update their heading, resulting in left-right hemisphere errors. We present two other response patterns, "non-movers" and "spinners," that also appear to result in failures to update heading. We have three specific goals in mind: (1) extend previous findings of higher turner rates with spatial language response mode using a point-to-origin task instead of a triangle completion task; (2) replicate the gender effect of males more likely responding as turners; (3) examine ethnicity influence. Designed as a classroom study, we presented participants (N = 498) with four passages through a virtual star field. Participants selected the direction pointing to the origin from four multiple-choice items. Response mode was either pictograms or written language, chosen to compare with similar studies and see if these response modes have an effect on virtual orientation behavior. Results show a majority of participants (48.35%) classified as non-turners, 32.93% turners, 15.57% as non-movers, and 3.14% as spinners. A multinomial regression model reached 49% classification performance. Written spatial language, compared to pictograms, made turner response patterns more likely; this effect was more pronounced for Chinese participants and among females, but not male Caucasians. Moreover, higher turner numbers for written spatial language extends Avraamides findings of higher turner numbers when participants turned their bodies toward the origin but not when they responded verbally. Using pictorial response mode (i.e., top-down picture of a head) may have increased cognitive load because it could be considered more embodied. It remains to be seen how we can reduce the reference frame conflict that might have caused increased cognitive load. Second, our results are inconsistent with previous research in that males overall did not show more turner behavior than females. Future research may look at possible underlying factors, such as cultural norms. Third, individualistic cultures (Caucasians; Greif, 1994) lean toward turner response patterns, whereas collectivist cultures (Asian) lean toward non-turner response patterns.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".