Toward a theory of the empirical tracking of individuals: Cognitive flexibility and the functions of attention in integrated tracking
Bibliographic record
Abstract
How do humans manage to keep track of a gradually changing object or person as the same persisting individual despite the fact that the extraction of information about this individual must often rely on heterogeneous information sources and heterogeneous tracking methods? The article introduces the Empirical Tracking of Individuals (ETI) theory to address this problem. This theory proposes an analysis of the concept of integrated tracking, which refers to the capacity to acquire, store, and update information about the identity and location of individuals in our environment. It hypothesizes that certain functions of attention are a key to explaining how the cognitive flexibility of the human mind overcomes the heterogeneity of sources and methods in integrated tracking. At least two premises lend support to this hypothesis. First, heterogeneity of tracking sources is overcome by the combination of information from multiple perceptual modalities and a phenomenon of multisensory ‘transparency’. Second, heterogeneity of tracking sources and methods may also be overcome by inferences that combine information across domains to acquire reasons to believe propositions about the target's location and identity.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".