Wanting, liking, and preference construction.
Bibliographic record
Abstract
According to theories on preference construction, multiple preferences result from multiple contexts (e.g., loss vs. gain frames). This implies that people can have different representations of a preference in different contexts. Drawing on Berridge's (1999) distinction between unconscious liking and wanting, we hypothesize that people may have multiple representations of a preference toward an object even within a single context. Specifically, we propose that people can have different representations of an object's motivational value, or incentive value, versus its emotional value, or likability, even when the object is placed in the same context. Study 1 establishes a divergence between incentive value and likability of faces using behavioral measures. Studies 2A and 2B, using self-report measures, provide support for our main hypothesis that people are perfectly aware of these distinct representations and are able to access them concurrently at will. We also discuss implications of our findings for the truism that people seek pleasure and for expectancy-value theories.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".