I can see myself enjoying that: Using imagery perspective to circumvent bias in self-perceptions of interest.
Bibliographic record
Abstract
People experience life satisfaction when pursuing activities that genuinely interest them. Unfortunately, cultural stereotypes (e.g., "science is not for girls") and preexisting self-beliefs can bias people's memories, thereby hindering their ability to identify the domains that they actually experience as interesting. The current experiments tested a novel method for circumventing this problem by manipulating visual imagery perspective as people recalled their experiences. Four experiments measured (or manipulated) participants' actual experience of interest as they completed a task; the experiments also measured (or manipulated) participants' self-beliefs about their interest in the domain. The experiments then manipulated imagery perspective as participants recalled their interest in the task. Prior research suggests that imagery from an actor's first-person perspective facilitates a bottom-up processing style, whereas imagery from an external third-person facilitates a top-down processing style (Libby & Eibach, 2011). Consistent with this account, across all 4 experiments, first-person imagery (vs. third-person) caused people's recall to be less biased by the top-down influence of their self-beliefs and better aligned with their past experienced interest. The final experiment demonstrated downstream consequences of these effects on female undergraduates' intentions to pursue future activities in a domain (STEM) that negative stereotypes typically might dissuade them from pursuing. Thus, the present results suggest that first-person imagery can be a useful tool to reduce the influence of biased self-beliefs, while increasing sensitivity to past bottom-up experiences during recall. Further, these results hold practical implications for reducing psychological barriers that can keep underrepresented individuals from pursuing interests in counterstereotypical domains. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".