The Impact of Self-Relevance and Valence on Word Processing: an ERP study
Bibliographic record
Abstract
Social-cognition implicates a unique processing mechanism for self-relevant information, which has life-long adaptive outcomes. The well-established self-referential encoding task (SRET) specifically probes this self-referential bias, as well as the equally adaptive positivity bias. These two biases have primarily been examined in isolation, separately demonstrating improved endorsement and memory for positive (vs. negative), and self-relevant (vs. other-relevant) trait adjectives. The goal of the current study was to extend this research by simultaneously examining the effect of valence and self-relevance on behavioral indices of memory, and Event Related Potential (ERP) indices of attention and emotion processing at encoding. Using a within-subjects block design, participants viewed and endorsed (or not) positive and negative trait adjectives in terms of themselves (self-relevant block) or Harry Potter (other-relevant block). ERPs were time-locked to word onset and analyses focused on both the early Late Positive Potential (eLPP, 400-600 ms) reflecting sustained attention, and its late counterpart (lLLP, 600-1200ms) reflecting emotional processing. Following the SRET, participants completed unexpected recall and recognition tasks. Consistent with past studies, participants displayed a positivity bias, endorsing and remembering more positive (vs. negative) words. Additionally, participants displayed a self-referential bias, endorsing and remembering more self-relevant (vs. other-relevant) words. The ERP findings paralleled this behaviour, with larger amplitude for self- (vs. other) relevant items from 400-800ms, spanning the eLPP and part of the lLPP. Valence affected only the lLPP with an increased amplitude for positive (vs. negative) trait-adjectives from 600-1000ms. These results suggest the self-referencing and positivity biases might be discrete cognitive processes that do not interact. Self-referential processing seems to start earlier than valence processing, although both overlap around 600-800ms. Additionally, the positivity bias does not appear specific to self-relevant processing, but applies generally across social processing conditions. It appears these two biases have uniquely adaptive roles within social cognition. Meeting abstract presented at VSS 2018
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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 | 0.000 | 0.001 |
| 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 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".