Interaction of Emotion and Cognition in the Processing of Textual Material
Bibliographic record
Abstract
Cognitive psychology and cognitive science have only recently come to acknowledge that human beings are not “pure” cognitive systems, and that emotions may be more than simply another form of cognition. This paper presents recent theoretical issues on the interaction of cognition with emotion, drawing on findings from evolutionary, neurobiological and cognitive research. These findings indicate that emotions have a fundamental and, often, universal importance for human cognitive functioning. Advanced cognitive processing, such as the processing required for text comprehension and translation, most of the time follows after a first, primary appraisal of the emotional impact of the information on the reader. This type of appraisal is momentary, non-conscious and non-cognitive, and is carried out by some system in the organism that functions with its own distinctive rules, different from those of the cognitive system. Emotional appraisal of the information sets the mode in which the organism (including its cognitive processes) will operate. Evidence suggests that negative emotions can instantly and non-consciously increase processing effort and time and decrease cognitive capacity, while on the other hand, positive emotions generally increase cognitive resources and expand attention and creativity. This implies that both cognitive processing of textual information, as well as its outcome, are influenced not only by the interpreters cognitive skill or by the emotional features of the text per se (the emotional impact that the writer has attempted to generate), but also (and perhaps most importantly) by the subjective emotional significance that the information has for each individual interpreter.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".