The Influence of Affect on Cognitive Breadth
Bibliographic record
Abstract
Affect plays a critical role in how broadly one processes and thinks about information. Breadth of cognition is shown to relate to, and be influenced by, affect that varies in valence (negative and positive), arousal (high/activated and low/deactivated) and motivational intensity (approach and withdrawal). While extensive work has shown support for the influence of affect on breadth of attention (attentional breadth), there is less research on affect and breadth of thought (conceptual breadth). The present dissertation investigates: 1) the relationship between various measures of conceptual breadth, 2) how individual differences in naturally occurring affect relate to conceptual breadth, 3) how anticipating and experiencing gains and losses influence conceptual breadth, 4) how differences in trait behavioral approach (BAS) and inhibition (BIS) relate to conceptual breadth in a monetary incentive paradigm, and 5) how individual differences in affect relate to filtering of irrelevant information. In Study 1, three varied conceptual breadth tasks appropriately estimated a conceptual breadth latent variable. Individual differences in naturally occurring affect were shown to relate to the common conceptual breadth variability where those who had low arousal positive affect showed greater conceptual breadth. In Study 2 conceptual breadth scores did not differ when anticipating gains and losses versus experiencing gains and losses. However, BAS, but not BIS, modulated the effect of large incentives on cognitive categorization where those low in BAS had higher conceptual breath following large losses and those high in BAS had larger conceptual breadth following large gains. In Study 3, individual differences in naturally occurring positive affect did not relate to the tendency to bind irrelevant and relevant information into memory (hyper-binding) in a meaningful way across four studies, However, hyper-binding was found in all studies including age groups where hyper-binding has not typically been shown before. Evidence from the current dissertation supports the significant role of affect in conceptual breadth, whether affect is naturally occurring or influenced by incentives, and provides evidence that individual differences in affect do not underlie individual differences in hyper-binding.
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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".