Symmetrical “super learning”: Enhancing causal learning using a bidirectional probabilistic outcome.
Bibliographic record
Abstract
In a learning environment, with multiple predictive cues for a single outcome, cues interfere with or enhance each other during the acquisition process (e.g., Baker et al., 1993). Previous experiments have focused on cues that signal the presence or absence of binary outcomes. This introduces a perceptual and perhaps motivational asymmetry between excitatory and inhibitory learning. Here, using a bidirectional outcome, we asked whether learning about both generative (incremental positive outcome) and preventative (incremental negative outcome) causal cues show similar enhancement effects in opposite directions. In three experiments with humans using predictive learning tasks, participants (N = 133) were exposed to probabilistic predictive cues for opposite polarity events. Generative cues caused an increase in outcome likelihood, while preventative cues decreased it. An analysis of explicit predictive ratings found evidence for symmetrical learning and enhanced learning for both generative and preventative cues. The results are discussed in relation to super learning, an effect derived from theories of competitive learning based on error correction and theories of contrasting probability estimates. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".