Reward as a Facet of Word Meaning: Ratings of Motivation for 8601 English Words
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. Behavioral and neurophysiological data suggest 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 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 8601 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 was worse at explaining task performance than the individual variables. We discuss implications of these results for neurocognitive theories of semantics.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".