SOAP Opera: Self as Object and Agent in Prioritizing Attention
Bibliographic record
Abstract
A growing body of evidence has demonstrated that multiple sources of salience tune attentional sets toward aspects of the environment, including affectively and motivationally significant categories of stimuli such as angry faces and reward-associated target locations. Recent evidence further indicates that objects that have gained personal significance through ownership can elicit similar attentional prioritization. Here we discuss current research on sources of attentional prioritization that shape our awareness of the visual world from moment to moment and the underlying neural systems and contextualize what is known about attentional prioritization of our possessions within that research. We review behavioral and neuroimaging research on the influence of self-relevance and ownership on cognition and discuss challenges to this literature stemming from different modes of conceptualizing and operationalizing the self. We argue that ownership taps into both "self-as-object," which characterizes the self as an object with a constellation of traits and attributes, and "self-as-subject," which characterizes the self as an agentic perceiver and knower. Despite an abundance of research probing neural and behavioral indices of self-as-object and its effects on attention, there exists a paucity of research on the influence of self-relevance of attention when self is operationalized from the perspective of a first-person subject. To begin to address this gap, we propose the Self as Ownership in Attentional Prioritization (SOAP) framework to explain how ownership increases salience through attention to external representations of self-identity (i.e., self as object) and attention to contextually mediated permission to act (i.e., self as subject).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".