The brain weights body‐based cues higher than vision when estimating walked distances
Bibliographic record
Abstract
Optic flow is the stream of retinal information generated when an observer's body, head or eyes move relative to their environment, and it plays a defining role in many influential theories of active perception. Traditionally, studies of optic flow have used artificially generated flow in the absence of the body-based cues typically coincident with self-motion (e.g. proprioceptive, efference copy, and vestibular). While optic flow alone can be used to judge the direction, speed and magnitude of self-motion, little is known about the precise extent to which it is used during natural locomotor behaviours such as walking. In this study, walked distances were estimated in an open outdoor environment. This study employed two novel complementary techniques to dissociate the contributions of optic flow from body-based cues when estimating distance travelled in a flat, open, outdoor environment void of distinct proximal visual landmarks. First, lenses were used to magnify or minify the visual environment. Second, two walked distances were presented in succession and were either the same or different in magnitude; vision was either present or absent in each. A computational model was developed based on the results of both experiments. Highly convergent cue-weighting values were observed, indicating that the brain consistently weighted body-based cues about twice as high as optic flow, the combination of the two cues being additive. The current experiments represent some of the first to isolate and quantify the contributions of optic flow during natural human locomotor behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".