Evaluative conditioning using virtual reality events
Bibliographic record
Abstract
Evaluative conditioning (EC) is observed when a neutral stimulus is paired with an emotionally charged unconditioned stimulus (US), resulting in a change in the pleasantness or liking of the CS. Few studies have focused on this effect within an episodic memory context (unique single-trial learning of US-CS pairings). Moreover, most studies involve US-CS pairings presented on a computer screen, but few studies have examined EC under more naturalistic conditions. We sought to fill these gaps, using a novel virtual reality (VR) paradigm. A sample of 74 participants experienced a series of negative and neutral environments in VR wherein they encountered US-CS pairs only once. They then provided ratings of pleasantness and completed a cued recall task, to assess EC and episodic memory, respectively. We successfully replicated the EC effect and did not find an association between EC and episodic memory. This latter pattern diverges from a prior study in our laboratory [Palombo, D. J., Elizur, L., Tuen, Y. J., Te, A. A., & Madan, C. R. (2021). Transfer of negative valence in an episodic memory task. Cognition, 217, 104874] and may provide insights into contextual factors not captured in the previous work. Together, our results point to the importance and effectiveness of using more naturalistic and diverse paradigms to investigate and replicate cognitive phenomena. Moreover, they may shed further light on the factors shaping the formation of affective attitudes from experiences.
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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 | 0.002 |
| 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) | 0.003 | 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".