A Cognitive Ethology Study of First- and Third-Person Perspectives
Bibliographic record
Abstract
The aim of the present study was to test the cognitive ethology approach, which seeks to link cognitions and behaviours as they operate in everyday life with those studied in controlled lab-based investigations. Our test bed was the understanding of first-person and third-person perspectives, which in lab-based investigations have been defined in a diverse and multi-faceted manner. We hypothesized that because these lab-based investigations seek to connect with how first- and third-person perspective operates in everyday life, then either some of the divergent lab-based definitions are missing their mark or the everyday conceptualization of first- and third-person perspective is multi-faceted. Our investigation revealed the latter. By applying a cognitive ethology approach we were able to determine that a) peoples' everyday understanding of perspective is diverse yet reliable, and b) a lab-based investigation that applies these diverse understandings in a controlled setting can accurately predict how people will perform. These findings provide a 'proof of concept' for the cognitive ethology approach. Moreover, the present data demonstrate that previous lab-based studies, that often had very different understandings of first- and third-person perspective, were each in and of themselves valid. That is, each is capturing part of a broader understanding of perspective in everyday life. Our results also revealed a novel social factor not included in traditional conceptualizations of first-person third-perspective, that of eye gaze, i.e., eye contact is equated strongly with first-person perspective and the lack of eye-contact with third-person perspective.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".