The phenomenon of online diffusion of misattributed quotes is so widespread that got its own dedicated meme. You may have seen a picture of Abraham Lincoln, the 16th President of the United States, that warns: ‘Don’t believe everything you read on the Internet just because there’s a picture with a quote next to it’. Lincoln, apparently nicknamed ‘Honest Abe’ when young, was assassinated in 1865, which makes it unlikely he had opinions about the Internet, and he is one of the historical celebrities most quoted (often incorrectly) on the web. Lincoln shares this questionable honour with the likes of Mark Twain and Albert Einstein. ‘The definition of insanity is doing the same thing over and over again and expecting a different result’ is one of the most famous quotes of Albert Einstein. Except that is not: the earliest known exact match of the quote appears in a Narcotics Anonymous information pamphlet in 1981, some 25 years after Einstein’s death.
The next EHBEA conference in Paris will include a “satellite meeting” on cultural attraction theory: Cultural Evolution by Cultural Attraction: Empirical Issues
I will give a talk titled Three predictions for cultural attraction theory. Below the (tentative) slides. If you cannot wait, the three predictions are:
- lo-fi copying is more significant than hi-fi copying in cultural transmission
- domain-general social influence (context-biases) is not very important
- culture is a matter of global, often neutral, traditions, more than local, generally adaptive, differences
[The first part is here]
In a successive series of models, published in Scientific Reports, we considered whether other individual-level mechanisms could potentially be mistaken for conformity, generating relations between frequency of a trait and probability to copy it that looked like sigmoids. We choose a few simple and plausible mechanisms (you can refer to the paper for details) and we found that two of them – on a total of seven tested, plus three controls – generated relations for which a sigmoid function produced a better fit than a linear one (see figure below). The codes for running all simulations (written in Matlab) are available through the Open Science Framework.
I recently did some modelling work, in collaboration with Edwin van Leeuwen and others, exploring possible confounds in conformity research. As I discussed in a post some time ago, “conformity”, in cultural evolution, has a precise meaning as a disproportionate tendency to copy the majority. “Disproportionate” here means that the probability to copy a popular cultural trait should be higher than the frequency of the trait itself. In other words, if 60% of your friends wear read, and 40% wear blue, not only you should be more likely to also wear read (this would happen also by copying at random), but your probability to wear read should be higher than 60%. Why is this important? Conformity, in this technical sense, allows majority behaviours to be resistant to random fluctuations, or to changes in population, like migrations, etc. This, in turn, contributes to maintain stable cultural differences between groups.
Andrew Buskell is organising an excellent two-day (13-14 December) conference in Cambridge, New Directions in Evolutionary Social Sciences, exploring current developments and debates in cultural evolution.