Grasping numbers: How numerical magnitude affects kinematics of reach to grasp actions
Bibliographic record
Abstract
The SNARC (spatial-numerical association of response codes) effect is a well-studied phenomenon showing a spatial organisation of the magnitude of numbers. Smaller numbers (0-4) are associated with the left side of an individuals perceived surroundings, while larger numbers (6-9) are associated with the right side of an individuals perceived surroundings. The goal of our study was to determine if a larger numerical value on an object will increase the maximum distance between the thumb and index finger when reaching for the object also known as the maximum grip aperture. Further, it was of interest if the effect would be seen when using a cluster of small shapes as an indicator of numeracy as opposed to an Arabic numeral. Two sets of blocks were used: one with Arabic numerical values (1, 2, 8, 9) written on them and the other with heart-shaped stickers on them. For each set of blocks the values 1 and 9 were on the faces of the smaller block, while the values 2 and 8 were on faces of the larger block which served as a foil. A control block the same size as the smaller blocks with nothing on its face was also used. University students were recruited for the study, each participant was only tested using one set of blocks. Participants were tasked with grasping blocks after an audio cue. While reaching for the block, participants were instructed to say the number of hearts or Arabic numeral on the block. This was done for both the left and right hand of each participant. During each grasp of the block maximum grip aperture was recorded. Research is still undergoing with results to be finalized in the coming weeks. Based on prior experiments we hypothesize that maximum grip aperture should be altered depending on the size of the numerical value on the object. A tendency to have a larger maximum grip aperture when reaching for the larger number should occur, even though the objects are the same size. Using a cluster of shapes as an indicator of numeracy should also result in a similar difference to the one seen with numerical values. Since numerical processing occurs in the right hemisphere of the brain, the effect should be more pronounced in the left hand than the right. *Indicates presenter
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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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".