The ABC of social learning: Affect, behavior, and cognition.
Bibliographic record
Abstract
Debates concerning social learning in the behavioral and the developmental cognitive sciences have largely ignored the literature on social influence in the affective sciences despite having arguably the same object of study. We argue that this is a mistake and that no complete model of social learning can exclude an affective aspect. In addition, we argue that including affect can advance the somewhat stagnant debates concerning the unique characteristics of social learning in humans compared to other animals. We first review the two major bodies of literature in nonhuman animals and human development, highlighting the fact that the former has adopted a behavioral approach while the latter has adopted a cognitive approach, leading to irreconcilable differences. We then introduce a novel framework, affective social learning (ASL), that studies the way we learn about value(s). We show that all three approaches are complementary and focus, respectively, on behavior toward; cognitions concerning; and feelings about objects, events, and people in our environment. All three thus contribute to an affective, behavioral, and cognitive (ABC) story of knowledge transmission: the ABC of social learning. In particular, ASL can provide the backbone of an integrative approach to social learning. We argue that this novel perspective on social learning can allow both evolutionary continuity and ontogenetic development by lowering the cognitive thresholds that appear often too complex for other species and nonverbal infants. Yet, it can also explain some of the major achievements only found in human cultures. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".