Conformity bias in the cultural transmission of music

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In the last several decades researchers have begun to explore how these kinds of transmission 2. processes can be inferred from large scale cultural datasets This meme s eye view approach 12. royalsocietypublishing org journal rsos, originally pioneered by archaeologists studying ceramics 13 14 has since been applied to dog breeds. 15 cooking ingredients 16 and baby names 17 In music this approach has revealed that. frequency based biases like conformity and novelty in which the probability of adopting a variant. disproportionately depends on its commonness or rarity 18 vary across domains and levels of. analysis For example there is some evidence that dissonant intervals in Western classical music are. subject to novelty bias 19 rhythms in Japanese enka music are subject to conformity bias 19 and. popular music at the level of albums 15 and artists 20 is subject to random copying 1. Music sampling or the use of previously recorded material in a new composition is an ideal model. for investigating frequency based bias in the cultural evolution of music because 1 samples are known. to be culturally transmitted between collaborating artists and 2 sampling events are reliably. documented in online databases 21 For researchers music sampling is a rare case where process is. understood and pattern is accessible In the current study we aim to use longitudinal sampling data. R Soc open sci 6 191149, to determine whether frequency based bias has played a role in the cultural transmission of music. sampling traditions Earlier manifestations of the meme s eye view approach based on diversity and. progeny distributions are time averaged and more susceptible to type I and II error respectively. 17 22 23 In the current study we use two more recent methods turn over rates and generative. inference that better capture the temporal dynamics that result from transmission processes 24. The turn over rate of a top list of cultural variants ranked by descending frequency is simply the. number of new variants that appear at each timepoint 15 Examples of top lists in popular culture. include the Billboard Hot 100 music chart and the IMDb Top 250 movies chart By comparing the. turn over rates z of top lists of different lengths y we can gain insight into whether or not the data. are consistent with neutral evolution i e random copying The turn over profile for a particular. cultural system can be described with the following function. zy A yx 1 1, where A is a coefficient depending on population size and x indicates the level of frequency based bias. 20 25 26 Simulation studies indicate that at neutrality x 0 86 20 25 Under conformity bias turn over. rates are relatively slower for shorter top lists leading to a convex turn over profile x 0 86 Likewise. under novelty bias turn over rates are relatively faster for shorter top lists leading to a concave turn over. profile x 0 86 20, Generative inference is a powerful simulation based method that uses agent based modelling and. approximate Bayesian computation ABC to infer underlying processes from observed data 27. Agent based modelling allows researchers to simulate a population of interacting agents that. culturally transmit information under certain parameters With a single cultural transmission model. this method can be used to infer the parameter values that likely generated the observed data. 23 26 28 29 With competing models assuming different forms of bias this method can be used to. choose the model that is most consistent with the observed data 23 28 30 31 In the current study we. use the basic rejection form of ABC for parameter inference and a random forest machine learning. form of ABC for model choice,2 1 Data collection, Sampling data were collected from WhoSampled https www whosampled com on 18 February.
2019 The analysis was restricted to drum breaks because artists typically only use one drum break. per composition whereas vocal and instrumental samples are combined more flexibly For each. sample source tagged as a drum break we compiled the release years and artist names for every. sampling event that occurred between 1987 and 2018 Previous years had fewer than 82 cultural. variants and were excluded from the analysis Collectively this yielded 1463 sample sources used. 38 500 times by 14 387 unique artists The release years were used to construct a frequency table in. which each row is a year each column is a sample and each cell contains the number of times that. Under certain conditions The transmission of popular artists on Last fm is consistent with random copying in generalist groups of. users and conformity in more niche groups of users 20. Table 1 Notable sampling events for the ve most sampled drum breaks used in the current study The number of times each 3. drum break has been sampled was collected from WhoSampled on 27 June 2019. royalsocietypublishing org journal rsos,original sample sampled notable sampling events. Amen Brother by The Winstons 1969 3225 Straight Outta Compton by N W A 1988. King of the Beats by Mantronix 1988,I Want You Forever by Carl Cox 1991. Think About It by Lyn Collins 1972 2251 It Takes Two by Rob Base DJ E Z Rock 1988. Alright by Janet Jackson 1989,Come on My Selector by Squarepusher 1997. Funky Drummer by James Brown 1970 1517 Fight the Power by Public Enemy 1989. R Soc open sci 6 191149,I Am Stretched on Your Grave by Sin ad O Connor. Pop Corn by Caustic Window 1992, Funky President People It s Bad by 865 Eric B Is President by Eric B Rakim 1986.
James Brown 1974 Hip Hop Hooray by Naughty by Nature 1993. Wontime by Smif N Wessun 1995, Impeach the President by The Honey 785 The Bridge by MC Shan 1986. Drippers 1973 Mr Loverman by Shabba Ranks 1992,The Flute Tune by Hidden Agenda 1995. particular sample was used in that year Notable sampling events for the five most sampled drum breaks. are shown in table 1 and the frequencies of 10 common and 10 rare samples through time are shown in. 2 2 Turn over rates, Turn over rates were calculated using the HERAChp KandlerCrema package in R 26 x was calculated. from top lists up to size 142 the minimum number of cultural variants present in a given year across all. years The observed distribution of turn over rates was compared to those expected under neutral. conditions according to Bentley 15 and Evans Giometto 25. 2 3 Agent based modelling, Simulations were conducted using the agent based model of cultural transmission available in the. HERAChp KandlerCrema package in R 26 This transmission model generates a population of N. individuals with different cultural variants and simulates the transmission of those variants between. timepoints given a particular innovation rate and level of frequency based bias b As departures. from neutrality can only be reliably detected after equilibrium has been reached this model. incorporates a warm up period that is excluded from the rest of the analysis Negative values of b. correspond to conformity bias while positive values correspond to novelty bias The output of this. model includes turn over rates and the Simpson s diversity index at each timepoint Simpson s. diversity index D is the probability that any two randomly selected cultural variants are of the same. type where values closer to 0 indicate high diversity and values closer to 1 indicate low diversity 32. 2 4 Parameter inference, Parameter inference was conducted with the rejection algorithm of ABC using the EasyABC 33 and abc.
34 packages in R in three basic steps, 1 100 000 iterations of the model were run to generate simulated summary statistics for different values. of b within the prior distribution,royalsocietypublishing org journal rsos. R Soc open sci 6 191149, 1 2 3 4 5 6 7 8 9 10 501 502 503 504 505 506 507 508 509 510. samples ranked by use, Figure 1 Violin plots showing the frequencies of samples ranked by overall use from 1980 to 2019 The x axis is the rank of each. sample and the y axis is the year To the left of the dotted line are samples 1 10 while to the right are samples 501 510 More. common samples on the left appear to be much more stable over time than rarer ones The high popularity of the more common. samples in the late 80s and early 90s is likely due to the rapid expansion of sample based hip hop and dance music triggered by. increased access to digital samplers and more relaxed copyright enforcement during that period. 2 The Euclidean distance between the simulated and observed summary statistics was calculated for. each iteration, 3 The 1000 iterations with the smallest distances from the observed data determined by the tolerance.
level 0 01 were used to construct the posterior distribution of b. The exponent of the turn over function x and the mean Simpson s diversity index D were used as. summary statistics for parameter inference Population size N 729 innovation rate 0 037 and. warm up time t 200 were kept constant for all models and a uniform prior distribution was used. for b 0 2 0 2 Population size was calculated from the mean number of unique artists involved in a. sampling event at each timepoint in the observed dataset Innovation rate was calculated from the. mean number of new sample types per total number of samples at each timepoint in the observed. dataset according to Shennan Wilkinson 35 The warm up time was determined by running 1000. iterations of a neutral model with the observed innovation rate over 500 timepoints 23 and. estimating when observed diversity reaches equilibrium see electronic supplementary material figure. S1 The bounds of the uniform prior distribution for b adapted from Crema et al 23 were reduced. based on observed levels of frequency based bias in other cultural systems 26 27 29 Each model was. run for 32 timepoints which corresponds to the number of years in the observed dataset. 2 5 Model choice, Model choice was conducted with the random forest algorithm of ABC using the abcrf 36 package in. R Random forest is a form of machine learning in which a set of decision trees are trained on bootstrap. samples of variables and used to predict an outcome given certain predictors 37 Traditional ABC. methods function optimally with fewer summary statistics 38 requiring researchers to reduce the. dimensionality of their data We chose to use random forest for model choice because it appears to be. robust to the number of summary statistics 36 and does not require the exclusion of potentially. informative variables The random forest algorithm of ABC was conducted with the following steps. 1 50 000 iterations of each model conformity novelty and neutrality were run to generate simulated. summary statistics for different values of b within the prior distributions. royalsocietypublishing org journal rsos,R Soc open sci 6 191149. 0 35 70 105 140,top list size, 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 Giometto 25 in orange The x axis is the size of the top lists for which z. on the y axis was calculated, 2 The results of these three models were combined into a reference table with the simulated summary. statistics and calculated LDA2 axes as predictor variables and the model index as the outcome. 3 A random forest of 1000 decision trees was trained with bootstrap samples from the reference table. 150 000 rows each, 4 The trained forest was provided with the observed summary statistics and each decision tree voted.
for the model that the data were likely generated by. 5 The posterior probability of the model with the majority of the votes was calculated using the out of. bag data that did not make it into the bootstrap training samples. The details of this process are outlined by Pudlo et al 36 The following 178 summary statistics were. used for model choice the exponent of the turn over function x the mean turn over rate z y. for each list size up to 142 the Simpson s diversity index for each timepoint D up to 32 the. mean Simpson s diversity index D and the two LDA axes Population size N 729 innovation rate. 0 037 and warm up time t 200 were kept constant for all models Uniform prior distributions. were used for b in both the conformity 0 2 0 and novelty 0 0 2 models whereas b was kept. constant at 0 for neutrality, The observed turn over rates as well as those expected under neutral conditions can be seen in figure 2. Kolmogorov Smirnov tests found that the observed distribution of turn over rates is significantly. different from the neutral expectations of both Bentley 15 p 0 001 and Evans Giometto 25. p 0 001 The value of the exponent x see equation 1 1 for the observed data is 1 13 which is. indicative of conformity bias, The posterior probability distribution of the level of frequency based bias b constructed with the. basic rejection algorithm of ABC is shown in figure 3 Based on the parameter estimation of b the. observed data are most consistent with weak but significant conformity bias median 0 012 95. HDPI 0 019 0 0020 A goodness of fit test n 1000 0 01 indicates that the model is a good. fit for the data p 0 47 see electronic supplementary material figure S2 39 and leave one out. Linear discriminant analysis LDA is a method of dimensionality reduction similar to PCA that compresses multiple variables onto. two axes while maximizing the separation between classes. royalsocietypublishing org journal rsos,posterior probability density<. In the last several decades researchers have begun to explore how these kinds of transmission processes can be inferred from large scale cultural datasets

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