Wanting, liking, and preference construction.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it