Cue combination used to update the navigator’s self-localization, not the home location.
Bibliographic record
Abstract
This study investigated when the Bayesian cue combination of piloting and path integration occurs in human homing behaviors. The Bayesian cue combination was hypothesized to occur in estimating the home location or self-localization. In Experiment 1, the participants learned the locations of 5 objects (1 located at the learning position) in the presence of distal landmarks before walking a 2-leg path without viewing the landmarks and objects. At the end of the path, the participants indicated the original locations of the objects in 4 cue conditions: (a) path integration only, (b) landmarks only where the participants were disoriented and the landmarks reappeared, (c) both path integration and the reappearing landmarks, and (d) path integration and conflicting landmarks rotated 45°. The participants' heading, position, and homing estimations were calculated. The ratio of the length of the second leg to that of the first leg was manipulated to be 0.5, 1, or 2. The results showed evidence of the Bayesian cue combination for heading estimates in all leg ratios, and for homing estimates in a small leg ratio (0.5) but not in a large leg ratio (2). The following experiments replicated the results of the Bayesian cue combination for heading but not for homing estimates for the large leg ratio (2) when participants did a typical homing task without learning the locations of objects (Experiment 2) and when proximal landmarks replaced distal landmarks (Experiments 3-4). These findings suggest that the Bayesian cue combination occurs in self-localization prior to homing. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".