Evaluative conditioning using virtual reality events
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
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.
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 | 0.001 | 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) | 0.005 | 0.001 |
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