Insight into spatial updating provided by the joint kinematics examined during a continuous pointing and walking task
Bibliographic record
Abstract
A continuous pointing method recently introduced by Campos et al. (2009) provides a detailed examination of how spatial updating unfolds during the course of forward walking movements. Their task involves having participants walk along a straight path while keeping their right index finger fixated on a target located beside the walking path. Arm azimuth angle (i.e., shoulder rotation about the vertical axis) is used to indicate the participants’ target-relative positions and showed that it can be used to calculate perceived distance traveled and perceived self-velocity during the entire walking trajectory. In their study, Campos et al. (2009) measured arm azimuth angle as the rotation of the end effector-head plane with respect to the frontal plane. One limitation of this method is that it examines movement of the end effector without considering the joint angle displacements that are involved in completing the task. Therefore, in the current study, trunk, shoulder and elbow angles were calculated for continuous pointing movements using data collected from 19 retro-reflective markers attached to the upper body. Our study shows that using this more precise kinematic analysis can provide unique insights into the characteristics of underlying spatial processes. For example, when perceived distance traveled was calculated using only shoulder azimuth angle (i.e., shoulder plane of elevation), there was a calculated under-perception of the actual distance traveled as walking distance increased. However, by using a trajectory profile that combined shoulder azimuth and trunk azimuth (i.e., lateral trunk rotation) angles, there was a close approximation between the perceived and actual distances traveled throughout the walking movements. By understanding the control of continuous pointing movements at a more refined kinematic level, we can begin to understand how humans use egocentric information about their own body movements to perform spatial updating. Acknowledgments: NSERC
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".