Interindividual differences in Pavlovian influence on learning are consistent
Bibliographic record
Abstract
Pavlovian influences impair instrumental learning. It is easier to learn to approach reward-predictive signals and avoid punishment-predictive cues than their contrary. Whether the interindividual variability in this Pavlovian influence is consistent across time has been examined by a number of recent studies and met with mixed results. Here we introduce an open-source, web-based instance of a well-established Go-NoGo paradigm for measuring Pavlovian influence. We closely replicated the previous laboratory-based results. Moreover, the interindividual differences in Pavlovian influence were consistent across a two-week time window at the level of (i) raw measures of learning (i.e. performance accuracy), (ii) linear, descriptive estimates of Pavlovian bias (test-retest reliability: 0.40), and (iii) parameters obtained from reinforcement learning model fitting and model selection (test-retest reliability: 0.25). Nonetheless, the correlations reported here are still lower than the standards (i.e. 0.7) employed in psychometrics and self-reported measures. Our results provide support for trusting Pavlovian bias as a relatively stable individual characteristic and for using its measure in the computational understanding of human mental health.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".