Uniquely Human Aspects of Social Learning: The Role of Transmission Fidelity and Group Membership
Bibliographic record
Abstract
In recent years, humanity's extraordinary social, ecological, and technological success has been increasingly attributed to the capacity for social learning in general and cumulative cultural evolution (CCE) in particular.This thesis consists of four chapters aimed at investigating the nature and origin of several uniquely human aspects of this ability.Chapter 1 consists of an extensive review of what forms social learning takes (section 1.1), how these processes give rise to culture (section 1.2), how culture evolves over time (section 1.3), and why cultural evolution becomes cumulative in some cases but not others (section 1.4).This review highlights similarities and differences between humans and other animals, with a special emphasis on complex forms of social learning, such as imitation and teaching, which are especially pronounced in humans.Chapter 2 examines why humans alone seem to have benefited so extensively from CCE.Because CCE probably depends far more heavily on how reliably information is preserved than on how efficiently it is refined, one possible reason that CCE appears diminished or absent in other species is that it requires accurate but specialized forms of social learning at which humans are uniquely adept.Using a Bayesian model, this chapter contrasts the evolution of high-fidelity social learning, which supports CCE, against low-fidelity social learning, which does not.This model reveals that high-fidelity transmission evolves under a considerably different set of conditions than less accurate social learning: It requires social and individual learning to be relatively inexpensive and abundant, cultural traits to be complex, and adaptive problems to be difficult.If these conditions are relatively difficult to meet, then this could explain why social learning is common, but CCE is rare.Chapter 3 explores whether social learning is affected by the unique ways in which humans form and delineate between social groups, namely by using arbitrary and symbolic group markers.Although previous studies have shown that people prefer to copy in-group members, these have failed to resolve whether group membership itself affects who is copied or whether membership merely correlates with other known factors, such as similarity and familiarity, which nonhuman animals also employ.This chapter presents a pair of experiments aimed at disentangling these explanations inquisitive nature has been an inspiration.I am also grateful to my committee members, Dr. Simon
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".