Effects of Emotional Experience in Lexical Decision
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
Previous research has examined the effects of emotional experience (i.e., the ease with which words evoke emotion information) in semantic categorization, word naming, and Stroop tasks (Moffat, Siakaluk, Sidhu, & Pexman, 2015; Newcombe, Campbell, Siakaluk, & Pexman, 2012; Siakaluk, Knol, & Pexman, 2014). However, to date there are no published reports on whether emotional experience influences performance in the lexical decision task (LDT). In the present study, we examined the influence of emotional experience in LDT using three different stimulus sets. In Experiment 1 we used a stimulus set used by both Kousta, Vinson, and Vigliocco (2009; Experiment 1) and Yap and Seow (2014) that is comprised of 40 negative, 40 positive, and 40 neutral words; in Experiment 2 we used a stimulus set comprised of 150 abstract nouns; and in Experiment 3 we used a stimulus set comprised of 373 verbs. We observed facilitatory effects of emotional experience in each of the three experiments, such that words with higher emotional experience ratings were associated with faster response latencies. These results are important because the influence of emotional experience: (a) is observed in stimulus sets comprised of different types of words, demonstrating the generalizability of the effect in LDT; (b) accounts for LDT response latency variability above and beyond the influences of valence and arousal, and is thus a robust dimension of conceptual knowledge; (c) suggests that a richer representation of emotional experience provides more reliable evidence that a stimulus is a word, which facilitates responding in LDT; and (d) is consistent with grounded cognition frameworks that propose that emotion information may be grounded in bodily experience with the world (Barsalou, 2003, 2009; Vigliocco, Meteyard, Andrews, & Kousta, 2009).
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) | 0.001 | 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