Hunting for the SNARC: Spatial-Numerical Associations in an Order Judgment Task are Surprisingly Elusive
Bibliographic record
Abstract
Spatial-numerical associations (SNA) have been studied in various numerical and nonnumerical tasks.Classically, participants respond faster with their right hand to larger numbers and faster with their left hand to smaller numbers even when number identity is irrelevant.SNAs are often explained as activation of a left-to-right mental number line.Thus, SNAs may reflect magnitude processes, relative order, or both.In the present research, I explored the SNA in an order judgment task.In three experiments, participants judged whether three digit sequences were ordered (e.g., 1 2 3) or unordered (e.g., 2 1 3).People with more efficient calculation skill are faster and more accurate on order judgments than those with less efficient skills.Thus this task is assumed to capture important aspects of the mental representation of numerical sequences.Accordingly, I hypothesized that participants would show a SNA similar to that in other number tasks (e.g., parity, number comparison), that is, faster right hand responses to number sequences that are assumed to activate the right side (e.g., 6 7 8) and faster left hand responses to number sequences that are assumed to activate the left side (e.g., 2 3 4) of the mental number line.In three experiments, ordered but not unordered sequences showed consistent evidence for a typical SNA.In Experiment 1, Asian-educated individuals showed a typical, stable SNA effect on both ascending and descending sequences whereas Arabic-speaking participants showed no significant SNA effects.English-speaking participants showed a typical SNA on descending sequences and a reversed SNA on ascending sequences.In Experiments 2 and 3, only individuals with better arithmetic fluency showed a typical SNA.In Experiments 2 and 3, analyses using a multi-factor ANOVA design did not show evidence for SNARC effects.Instead, SNARC Many years ago I encountered Dr William Petrusic.He taught me to follow the data.He inspired me to pursue my PhD.He was a rich repository of knowledge, of stories, and of a belief in a just world.Thank-you to Elaine Petrusic for the many cups of tea, cookies and conversation whenever I would come into their home to talk about research.Bill's passing was a loss.I could not have completed this degree without the support of my supervisor, Dr. Jo-Anne LeFevre.Jo-Anne
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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 | 0.017 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".