The ABC of social learning: Affect, behavior, and cognition.
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
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).
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 | 0.000 | 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) | 0.003 | 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 it