Dataset For The Predicted And Perceived Sensory Consequences Of Movement
Bibliographic record
Abstract
In this project, we examine the extent to which changes in perceived hand location after visuomotor rotation adaptation are due to updated predictions about sensory consequences or rather recalibrated proprioception. We find that at least half of the shift in state estimates is due to recalibrated proprioception. There are two files in this dataset. The first has data on reaches in the training tasks as well as in the no-cursor reaching tasks, and is called 'reaches.txt'. The second has data on where the participants indicated they thought their right hand was by tapping on a touch screen with their left hand. This file is called 'taps.txt'. Reaches All reaches are first rotated so that the visual target has a Y coordinate of 0. Then they are splined to a resolution of 10 ms. And then put through a 2.5 Hz lowpass Butterworth filter. After that, some endpoint parameters (X and Y coordinate, angle) are calculated, As well as some parameters of the point of maximum velocity (velocity, X and Y coordinates and angle). Columns with independent variables: subject: subject number (0..20) condition: aligned or rotated (0: aligned, 1: rotated) phase: type of reach (0: training, 1: no-cursor reaches, 2: top-up training) iteration: repetitions of the task (0,1,2,3,4) NB: there are 4 iterations (0..3) in aligned, but 5 in rotated (0..4) trial: trialnumber (0..89; training: 0..xx, no-cursor reaches: 0..21, top-up training: 0..xx) target_angle: target angle in degrees (15,25,35,45,55,65,75) rotation: rotation of the visual feedback in degrees (0..30) Columns with dependent variables: endX: X coordinate of the endpoint endY: Y coordinate of the endpoint end_angle: deviation of the angle of the endpoint of the reach relative to the angle of the visual target in radians max_vel: maximum velocity in the reach (m/s?) max_velX: X coordinate at the point of maximum velocity max_velY: Y coordinate at the point of maximum velocity max_vel_angle: angle at the point of maximum velocity, relative to the angle of the visual target in radians Taps This table contains data on where people tapped on the touchscreen in a pre-processed form. All taps are described as an angle relative to where people indicated the home position was. This has 0 degrees to the right and 90 degrees straight ahead and so on. Columns with in independent variables: subject: subject number (0..20) rotated: tap recorded after rotated training or not (boolean: 0=false, 1=true) active: tap recorded after active movement or not (boolean: 0=false, 1=true) delayed: tap recorded delayed or not (boolean: 0=false - or online response, 1=true) trial: trial number (0..24) Columns with dependent variables: robot_angle: where the robot and hence the hand, actually was when responding on the touchscreen (in degrees angle) tap_angle: where the participant indicate their hand was
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.048 |
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".