Distributed Cognition and the Experience of Presence in the Mars Exploration Rover Mission
Bibliographic record
Abstract
Although research on presence in virtual environments has increased in the last few decades due to the rise of immersive technologies, it has not examined how it is achieved in distributed cognitive systems. To this end, we examine the sense of presence on the Martian landscape experienced by scientific team members in the Mars Exploration Rover (MER) mission (2004-2018). How this was achieved is not obvious because the sensorimotor coupling that typically underlies presence in mundane situations was absent. Nonetheless, we argue that the Three-Level model can provide a framework for exploring how presence was achieved. This account distinguishes between proto-presence, core-presence, and extended-presence, each level dependent on being able to respond effectively to affordances at a particular level of abstraction, operating at different timescales. We maintain that scientists' sense of presence on Mars involved core-presence and extended-presence rather than proto-presence. Extended-presence involved successfully establishing distal intentions (D-intentions) during strategic planning, i.e., long term conceptual goals. Core-presence involved successfully enacting proximal intentions (P-intentions) during tactical planning by carrying out specific actions on a particular target, abstracting away from sensorimotor details. This was made possible by team members "becoming the rover," which enhanced their ability to identify relevant affordances revealed through images. We argue, however, that because Mars exploration is a collective activity involving shared agency by a distributed cognitive system, the experience of presence was a collective presence of the team through the rover.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".