A DERIVED TRANSFER OF ELICITING EMOTIONAL FUNCTIONS USING DIFFERENCES AMONG ELECTROENCEPHALOGRAMS AS A DEPENDENT MEASURE
Bibliographic record
Abstract
Emotional responses have specific electroencephalographic (EEG) signatures that arise within a few hundred milliseconds post-stimulus onset. In this experiment, EEG measures were employed to assess for transfer of emotional functions across three 3-member equivalence classes in an extension of Dougher, Auguston, Markham, Greenway, & Wulfert's (1994) seminal work on the transfer of arousal functions. Specifically, 12 human participants were trained in the following match-to-sample performances A1 = B1, A2 = B2, A3 = B3 and B1 = C1, B2 = C2, B3 = C3. After successfully testing for the emergence of symmetry relations (B1 = A1, B2 = A2, B3 = A3 and C1 = B1, C2 = B2, C3 = B3), visual images depicting emotionally positive and emotionally negative content were presented with A1 and A3, respectively, using a mixed stimulus pairing-compounding procedure. A2 was paired with emotionally neutral images. Next, EEG data were recorded as participants were exposed to a forced-choice recognition task with stimuli A1, B1, C1, A2, B2, C2, A3, B3, C3 and three novel stimuli A4, B4 and C4. Results yielded differential EEG effects for stimuli paired directly with emotional versus neutral images. Critically, differential EEG effects were also recorded across the C stimuli that were equivalently related to the A stimulus set. The EEG data coincide with previous reports of emotion-specific EEG effects, indicating that the initial emotional impact of a stimulus may emerge based on direct stimulus pairing and derived stimulus relations.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".