Reward as a facet of word meaning: Ratings of motivation for 8,601 English words
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
Semantic representations arise from a distillation of multiple sources of information, including sensory, motor, affective, interoceptive, linguistic and cognitive experience. Experience of reward is a highly salient aspect of many human activities, and yet its contribution to semantic processing is not well understood. To address this, the present study took a psycholinguistic approach to measuring and evaluating associations with reward as a facet of word meaning. Behavioural and neurophysiological data suggest that reward processing involves multiple stages and mechanisms. For instance, systems associated with the experience and anticipation of pleasure in response to a reward appear distinct from motivational processes that underlie the pursuit of a stimulus. We sought to collect a novel set of word ratings that capture the full extent of reward-related experience. Initial explorations revealed that reward/pleasure ratings are highly correlated with existing norms of emotional valence. Ratings of association with motivation, however, were only moderately correlated with valence, suggesting they capture distinct semantic information. We therefore conducted a preregistered large-scale study to obtain motivation ratings for 8,601 words. Our analyses suggest these ratings capture aspects of word meaning which are distinct from other semantic dimensions, such as concreteness and valence. Moreover, they explain unique variance in participant performance on lexical, semantic, and recognition memory tasks. We combined motivation and emotional valence ratings to provide a composite measure that might approximate a more general 'reward' construct. However, this did not explain additional variance compared to the individual variables. We discuss the implications of these results for neurocognitive theories of semantics.
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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 | 0.008 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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