Does the future look bright? Processing style determines the impact of valence weighting biases and self-beliefs on expectations
Bibliographic record
Abstract
People regularly form expectations about their future, and whether those expectations are positive or negative can have important consequences. So, what determines the valence of people’s expectations? Research seeking to answer this question by using an individual-differences approach has established that trait biases in optimistic/pessimistic self-beliefs and, more recently, trait biases in behavioral tendencies to weight one’s past positive versus negative experiences more heavily each predict the valence of people’s typical expectations. However, these two biases do not correlate, suggesting limits on a purely individual-differences approach to predicting people’s expectations. We hypothesize that, because these two biases appear to operate via distinct processes (with self-beliefs operating top-down and valence weighting bias operating bottom-up), to predict a person’s expectations on a given occasion, it is also critical to consider situational factors influencing processing style. To test this hypothesis, we investigated how an integral part of future thinking that influences processing style—mental imagery—determines each bias’s influence. Two experiments measured valence weighting biases and optimistic/pessimistic self-beliefs, then manipulated whether participants formed expectations using their own first-person visual perspective (which facilitates bottom-up processes) or an external third-person visual perspective (which facilitates top-down processes). Expectations corresponded more with valence weighting biases from the first-person (vs. third-person) but more with self-beliefs from the third-person (vs. first-person). Two additional experiments manipulated valence weighting bias, demonstrating its causal role in shaping expectations (and behaviors) with first-person, but not third-person, imagery. These results suggest the two biases operate via distinct processes, holding implications for interventions to increase optimism. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".