Together I Strive: The Motivational Benefits of Shared Reality with Instrumental Others
Bibliographic record
Abstract
Why are some people more successful than others? Thus far, research suggests that success is not only related to individual factors (e.g., self-control), but can also be facilitated by relationships with others. Successful people seem to recognize this, as they tend to like and draw closer to both instrumental objects and instrumental humans (IOs; others who make goal success more likely). For instance, students who are successful at their academic goals tend to evaluate both their study materials and study partners more positively when these goals are activated and important. Yet instrumental people have one crucially distinct feature that instrumental objects do not: a mind of their own. One key way to relate to the minds of others is by establishing a shared reality—the experience of shared attitudes and judgments about the world with another person. Therefore, I propose that shared reality is an important, previously unexplored, component of successful people’s relationship with their IOs. In six studies (N = 1,968), I explored (1) whether people are motivated to perceive, and indeed experience, greater shared reality with IOs vs. NIOs (non-instrumental others), and (2) whether those who do so are more likely to achieve their goals. Participants displayed greater shared reality with IOs versus NIOs—on both an implicit memory measure (Study 1) and on self-report measures (Studies 3-6). Further, when experimentally induced to feel like a goal was particularly important (vs. less important), participants reported decreased shared reality with NIOs (Study 2). Participants who experienced a greater sense of shared reality with IOs reported more goal success initially (Studies 3a, 3b, 5, 6), 3-4 weeks later (Study 3c), and achieved higher GPAs (Study 5). These effects generally held when controlling for NIO shared reality, as well as IO liking, closeness, and epistemic trust. Self-efficacy consistently mediated the effect of IO shared reality on goal success (Studies 4, 5c), indicating that IO shared reality may bolster people’s epistemic confidence in their abilities. Overall, findings suggest that being motivated to relate to the minds of IOs through shared reality may play an important role in goal success.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".