Conformity bias in the cultural transmission of music sampling traditions

Mason Youngblood

arxiv: 1906.11928 · v1 · pith:RJ44PZ6Tnew · submitted 2019-06-27 · 📊 stat.AP · q-bio.PE· stat.ML

Conformity bias in the cultural transmission of music sampling traditions

Mason Youngblood This is my paper

Pith reviewed 2026-05-25 13:36 UTC · model grok-4.3

classification 📊 stat.AP q-bio.PEstat.ML

keywords conformity biascultural transmissionmusic samplingfrequency-based biasapproximate Bayesian computationcultural evolutionagent-based modeling

0 comments

The pith

Sampling patterns at the population-level are most consistent with conformity bias.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how individual-level sampling choices in music scale up to generate population-level patterns, using a longitudinal dataset of sampling events across three decades. It first compares turnover rates of popular samples against expectations under neutral evolution, then applies agent-based simulations inside an approximate Bayesian computation framework to estimate the strength of frequency-based bias. The results indicate that the observed patterns align best with conformity bias, even though anecdotal reports had pointed toward novelty bias. This matters for understanding how cultural transmission rules operate in creative domains where copying is documented and widespread.

Core claim

Despite anecdotal evidence of novelty bias, sampling patterns at the population-level are most consistent with conformity bias.

What carries the argument

Agent-based simulations within an approximate Bayesian computation framework used to infer the level of frequency-based bias that generated the observed sampling data.

Load-bearing premise

The agent-based simulations within the approximate Bayesian computation framework correctly represent the underlying processes of cultural transmission and sampling choice.

What would settle it

A finding that the observed sampling frequencies or turnover rates are better explained by simulations assuming novelty bias or neutral evolution would falsify the conclusion.

Figures

Figures reproduced from arXiv: 1906.11928 by Mason Youngblood.

**Figure 2.** Figure 2: The observed turn-over rates (z ) for top-lists up to size 142, compared to those expected under neutral conditions according to Bentley [15] (in blue) and Evans and Giometto [25] (in orange). The x -axis is the size of the top lists for which z, on the y-axis, was calculated [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

read the original abstract

One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that frequency-based bias (e.g. conformity and novelty) drives large-scale cultural diversity in different ways across domains and levels of analysis. Music sampling is an ideal research model for this process because samples are known to be culturally transmitted between collaborating artists, and sampling events are reliably documented in online databases. The aim of the current study was to determine whether frequency-based bias has played a role in the cultural transmission of music sampling traditions, using a longitudinal dataset of sampling events across three decades. Firstly, we assessed whether turn-over rates of popular samples differ from those expected under neutral evolution. Next, we used agent-based simulations in an approximate Bayesian computation framework to infer what level of frequency-based bias likely generated the observed data. Despite anecdotal evidence of novelty bias, we found that sampling patterns at the population-level are most consistent with conformity bias.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper infers conformity bias from music sampling data via ABC but the abstract gives almost no model checks or quantitative outputs.

read the letter

The main takeaway is that population-level sampling patterns fit a conformity model better than neutral or novelty bias, based on turnover rates and ABC inference from three decades of documented sampling events. The work applies standard cultural evolution models to a new longitudinal dataset where transmission is traceable through artist collaborations, which is a reasonable domain choice. It also checks turnover against neutral expectations before moving to the bias inference, which is a sensible first step. The abstract supplies no numbers, no summary statistics details, no recovery tests on the ABC pipeline, and no discussion of potential confounders such as genre effects or collaboration structure. That leaves the stress-test concern intact: if the agent-based simulations omit those factors or use uninformative statistics, the posterior favoring conformity could be an artifact. The citation pattern looks standard for the field and the data source is external, which helps. This is the kind of applied paper that cultural evolution people might want to see in full, but only if the methods section actually shows the validation steps that are missing from the abstract. I would bring it to a reading group for the dataset and the modeling approach, but I would not cite it until the full methods are checked. A serious editor should send it to review rather than desk reject, mainly to get the model diagnostics on record.

Referee Report

2 major / 2 minor

Summary. The manuscript investigates frequency-based biases (conformity vs. novelty) in the cultural transmission of music sampling using a longitudinal dataset of sampling events over three decades. It first tests whether turnover rates of popular samples deviate from neutral evolution expectations, then applies agent-based simulations inside an approximate Bayesian computation (ABC) framework to infer the most likely bias strength, concluding that observed population-level sampling patterns are most consistent with conformity bias.

Significance. If the ABC inference is robust, the work supplies a quantitative link between individual-level frequency bias and macro-level patterns in a domain where transmission events are directly observable, strengthening cultural evolution research. The combination of turnover-rate tests with ABC parameter inference is a methodological strength when properly validated.

major comments (2)

[Abstract / Methods (ABC section)] Abstract and Methods: the claim that sampling patterns are 'most consistent with conformity bias' rests on the ABC posterior; however, no recovery tests, prior predictive checks, or cross-validation of the agent-based model against known neutral or novelty regimes are described, leaving open the possibility that the inferred conformity parameter arises from unmodeled factors (genre, collaboration structure, or temporal trends) rather than frequency bias itself.
[Results (turnover-rate analysis)] Results: the turnover-rate comparison to neutral expectations is load-bearing for ruling out pure neutrality, yet the manuscript supplies no quantitative effect size, confidence interval, or power analysis for this test; without these, it is unclear whether the deviation is large enough to require a bias parameter in the subsequent ABC step.

minor comments (2)

Clarify the precise set of summary statistics passed to the ABC (turnover rates plus any others) and report their sensitivity to the conformity parameter.
Add a table or figure showing posterior distributions or model comparison metrics (e.g., Bayes factors) across the neutral, conformity, and novelty models.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which highlight important aspects of model validation and quantitative reporting. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract / Methods (ABC section)] Abstract and Methods: the claim that sampling patterns are 'most consistent with conformity bias' rests on the ABC posterior; however, no recovery tests, prior predictive checks, or cross-validation of the agent-based model against known neutral or novelty regimes are described, leaving open the possibility that the inferred conformity parameter arises from unmodeled factors (genre, collaboration structure, or temporal trends) rather than frequency bias itself.

Authors: We agree that explicit validation of the ABC inference is necessary to support the claim. The original submission did not report recovery tests or prior predictive checks. In the revised manuscript, we will add parameter recovery tests by generating simulated datasets under known neutral, conformity, and novelty regimes and recovering the parameters via the same ABC procedure. We will also include prior predictive checks to verify that the model can generate data consistent with the observed patterns. To address potential unmodeled factors, we will expand the Discussion to explicitly consider how genre effects, collaboration networks, and temporal trends could influence the inferred bias parameter and note this as a limitation of the current model. revision: yes
Referee: [Results (turnover-rate analysis)] Results: the turnover-rate comparison to neutral expectations is load-bearing for ruling out pure neutrality, yet the manuscript supplies no quantitative effect size, confidence interval, or power analysis for this test; without these, it is unclear whether the deviation is large enough to require a bias parameter in the subsequent ABC step.

Authors: We concur that quantitative measures are needed to evaluate the strength of the deviation from neutrality. The turnover-rate analysis compared observed rates against neutral simulations but omitted explicit effect sizes, confidence intervals, and power analysis. In the revised Results, we will report standardized effect sizes for the difference between observed and expected turnover, bootstrap-derived confidence intervals around the observed turnover rates, and a power analysis using the simulation framework to quantify the test's sensitivity to detect non-neutral patterns. These additions will clarify the magnitude of the deviation and better motivate the ABC step. revision: yes

Circularity Check

0 steps flagged

No circularity: standard ABC model comparison on independent summary statistics

full rationale

The derivation proceeds by (1) computing empirical turnover rates from the sampling database, (2) generating synthetic datasets under neutral, conformity, and novelty agent-based models, and (3) using ABC to obtain posterior support for the conformity parameter. None of these steps defines the target quantity in terms of itself, renames a fitted parameter as a prediction, or relies on a self-citation chain for the uniqueness of the model. The mapping from individual bias parameter to population pattern is external to the data and is tested via simulation recovery; therefore the central claim does not reduce to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no details on free parameters, axioms, or invented entities used in the simulations or inference procedure.

pith-pipeline@v0.9.0 · 5693 in / 881 out tokens · 35963 ms · 2026-05-25T13:36:05.005635+00:00 · methodology

discussion (0)

Reference graph

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[1] [1]

Savage, P. E. Cultural evolution of music. Palgrave Communica- tions 5. doi:10.1057/s41599-019-0221-1f5z3 (2019)

work page doi:10.1057/s41599-019-0221-1f5z3 2019

[2] [2]

M., Mauch, M., Burt, A

MacCallum, R. M., Mauch, M., Burt, A. & Leroi, A. M. Evolution of music by public choice. Proceedings of the National Academy of Science 109, 12081–12086 (2012)

work page 2012

[3] [3]

E., Brown, S., Sakai, E

Savage, P. E., Brown, S., Sakai, E. & Currie, T. E. Statistical univer- sals reveal the structures and functions of human music.Proceedings of the National Academy of Sciences 112, 8987–8992 (2015)

work page 2015

[4] [4]

& Baggio, G

Lumaca, M. & Baggio, G. Cultural Transmission and Evolution of Melodic Structures in Multi-generational Signaling Games.Artiﬁcial Life 23, 406–423 (2017)

work page 2017

[5] [5]

& Kirby, S

Ravignani, A., Thompson, B., Grossi, T., Delgado, T. & Kirby, S. Evolving building blocks of rhythm: how human cognition creates music via cultural transmission. Annals of the New York Academy of Sciences 1423, 176–187 (2018)

work page 2018

[6] [6]

O., Acerbi, A

Brand, C. O., Acerbi, A. & Mesoudi, A. Cultural evolution of emo- tional expression in 50 years of song lyrics. SocArXiv, 1–14 (2019)

work page 2019

[7] [7]

Cavalli-Sforza, L. L. & Feldman, M. W. Cultural Transmission and Evolution: A Quantitative Approach (Princeton University Press, Princeton, NJ, 1981)

work page 1981

[8] [8]

Leonard, R. D. & Jones, G. T. Elements of an inclusive evolutionary model for archaeology. Journal of Anthropological Archaeology 6, 199–219 (1987)

work page 1987

[9] [9]

Durham, W. H. Advances in Evolutionary Culture Theory. Annual Review of Anthropology 19, 187–210 (1990)

work page 1990

[10] [10]

& Richerson, P

Boyd, R. & Richerson, P. J. in History and Evolution (eds Nitecki, M. H. & Nitecki, D. V.) 179–209 (State University of New York Press, Albany, NY, 1992)

work page 1992

[11] [11]

Ormrod, R. K. Adaptation and Cultural Diﬀusion. Journal of Ge- ography 91, 258–262 (1992)

work page 1992

[12] [12]

Descent with modiﬁcation and the archaeological record

Shennan, S. Descent with modiﬁcation and the archaeological record. Philosophical Transactions of the Royal Society B: Biological Sci- ences 366, 1070–1079 (2011)

work page 2011

[13] [13]

Neiman, F. D. Stylistic Variation in Evolutionary Perspective: In- ferences from Decorative Diversity and Interassemblage Distance in Illinois Woodland Ceramic Assemblages. American Antiquity 60, 7–36 (1995)

work page 1995

[14] [14]

Bentley, R. A. & Shennan, S. J. Cultural Transmission and Stochas- tic Network Growth. American Antiquity 68, 459–485 (2003)

work page 2003

[15] [15]

A., Lipo, C

Bentley, R. A., Lipo, C. P., Herzog, H. A. & Hahn, M. W. Regular rates of popular culture change reﬂect random copying. Evolution and Human Behavior 28, 151–158 (2007)

work page 2007

[16] [16]

& Lindenfors, P

Isaksson, S., Funcke, A., Envall, I., Enquist, M. & Lindenfors, P. A Novel Method to Analyze Social Transmission in Chronologically Sequenced Assemblages, Implemented on Cultural Inheritance of the Art of Cooking. PLOS ONE 10. doi:10.1371/journal.pone.0122092 (2015)

work page doi:10.1371/journal.pone.0122092 2015

[17] [17]

O’Dwyer, J. P. & Kandler, A. Inferring processes of cultural trans- mission: The critical role of rare variants in distinguishing neutrality from novelty biases. Philosophical Transactions of the Royal Soci- ety B: Biological Sciences 372. doi:10.1098/rstb.2016.0426 (2017)

work page doi:10.1098/rstb.2016.0426 2016

[18] [18]

& Richerson, P

Boyd, R. & Richerson, P. J. Culture and the Evolutionary Process (The University of Chicago Press, Chicago, 1985)

work page 1985

[19] [19]

& Kaneko, K

Nakamura, E. & Kaneko, K. Statistical Evolutionary Laws in Music Styles, 1–23 (2018)

work page 2018

[20] [20]

& Bentley, R

Acerbi, A. & Bentley, R. A. Biases in cultural transmission shape the turnover of popular traits. Evolution and Human Behavior 35, 228–236 (2014)

work page 2014

[21] [21]

Cultural transmission modes of music sampling tra- ditions remain stable despite delocalization in the digital age

Youngblood, M. Cultural transmission modes of music sampling tra- ditions remain stable despite delocalization in the digital age. PLOS ONE 14, e0211860 (2019)

work page 2019

[22] [22]

Premo, L. S. Cultural Transmission and Diversity in Time-Averaged Assemblages. Current Anthropology 55, 105–114 (2014)

work page 2014

[23] [23]

R., Edinborough, K., Kerig, T

Crema, E. R., Edinborough, K., Kerig, T. & Shennan, S. J. An Ap- proximate Bayesian Computation approach for inferring patterns of cultural evolutionary change. Journal of Archaeological Science 50, 160–170 (2014)

work page 2014

[24] [24]

& Kandler, A

Wilder, B. & Kandler, A. Inference of Cultural Transmission Modes Based on Incomplete Information. Human Biology 87, 193–204 (2015)

work page 2015

[25] [25]

Evans, T. S. & Giometto, A. Turnover Rate of Popularity Charts in Neutral Models. arXiv: 1105.4044 (2011)

work page internal anchor Pith review Pith/arXiv arXiv 2011

[26] [26]

& Crema, E

Kandler, A. & Crema, E. R. Analysing cultural frequency data: neu- tral theory and beyond. doi: 10.31235/OSF.IO/KVMZU (2018)

work page doi:10.31235/osf.io/kvmzu 2018

[27] [27]

& Powell, A

Kandler, A. & Powell, A. Generative inference for cultural evolution. Philosophical Transactions of the Royal Society B: Biological Sci- ences 373. doi:10.1098/rstb.2017.0056 (2018)

work page doi:10.1098/rstb.2017.0056 2017

[28] [28]

R., Kandler, A

Crema, E. R., Kandler, A. & Shennan, S. Revealing patterns of cultural transmission from frequency data: Equilibrium and non- equilibrium assumptions. Scientiﬁc Reports 6, 1–10 (2016)

work page 2016

[29] [29]

& Shennan, S

Kandler, A. & Shennan, S. A generative inference framework for analysing patterns of cultural change in sparse population data with evidence for fashion trends in LBK culture. Journal of the Royal Society Interface 12. doi:10.1098/rsif.2015.0905 (2015)

work page doi:10.1098/rsif.2015.0905 2015

[30] [30]

& Austerlitz, F

Thouzeau, V., Aﬀholder, A., Mennecier, P., Verdu, P. & Austerlitz, F. Inferring linguistic transmission between generations at the scale of individuals. bioRxiv. doi:10.1101/441246 (2018)

work page doi:10.1101/441246 2018

[31] [31]

& Calude, A

Pagel, M., Beaumont, M., Meade, A., Verkerk, A. & Calude, A. Dom- inant words rise to the top by positive frequency-dependent selec- tion. Proceedings of the National Academy of Sciences 116, 7397– 7402 (2019)

work page 2019

[32] [32]

Simpson, E. H. Measurement of Diversity. Nature 163. doi:10.1038/ 163688a0 (1949)

work page 1949

[33] [33]

& Dumoulin, N

Jabot, F., Faure, T. & Dumoulin, N. EasyABC: performing eﬃ- cient approximate Bayesian computation sampling schemes using R. Methods in Ecology and Evolution 4, 684–687 (2013). 6

work page 2013

[34] [34]

& Blum, M

Csillery, K., Francois, O. & Blum, M. G. B. abc: an R package for approximate Bayesian computation (ABC).Methods in Ecology and Evolution. doi:10.1111/j.2041-210X.2011.00179.x (2012)

work page doi:10.1111/j.2041-210x.2011.00179.x 2041

[35] [35]

Shennan, S. J. & Wilkinson, J. R. Ceramic Style Change and Neu- tral Evolution : A Case Study from Neolithic Europe. American Antiquity 66, 577–593 (2001)

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