Evolution of Social Learning with Payoff and Content Bias

There has been much theoretical work aimed at understanding the evolution of social learning; and in most of it, individual and social learning are treated as distinct processes. A number of authors have argued that this approach is faulty because the same psychological mechanisms underpin social and individual learning. In previous work, we analyzed a simple model in which both individual and social learning are the result of a single learning process. Here, we extend this approach by showing how payoff and content biases evolve. We show that payoff bias leads to higher average fitness when environments are noisy and change rapidly. Content bias always evolves when the expected fitness benefits of alternative traits differ.

Cultural transmission bias in the spread of voter fraud conspiracy theories on Twitter during the 2020 US election

During the 2020 US presidential election, conspiracy theories about large-scale voter fraud were widely circulated on social media platforms. Given their scale, persistence, and impact, it is critically important to understand the mechanisms that caused these theories to spread so rapidly. The aim of this study was to investigate whether retweet frequencies among proponents of voter fraud conspiracy theories on Twitter during the 2020 US election are consistent with frequency bias, demonstrator bias, and/or content bias. To do this, we conducted generative inference using an agent-based model of cultural transmission on Twitter and the VoterFraud2020 dataset. The results show that the observed retweet distribution is consistent with a strong content bias and demonstrator bias, likely targeted towards negative emotion and follower count, respectively. Based on the confounding effects of the timeline algorithm and population structure, we are most confident in concluding that the differential spread of voter fraud claims among proponents of voter fraud conspiracy theories on Twitter during and after the 2020 US election was partly driven by a content bias causing users to preferentially retweet tweets with more negative emotion.

Content bias in the cultural evolution of house finch song

In this study, we used a longitudinal dataset of house finch (Haemorhous mexicanus) song recordings spanning four decades in the introduced eastern range to assess how individual-level cultural transmission mechanisms drive population-level changes in birdsong. First, we developed an agent-based model (available as a new R package called TransmissionBias) that simulates the cultural transmission of house finch song given different parameters related to transmission biases, or biases in social learning that modify the probability of adoption of particular cultural variants. Next, we used approximate Bayesian computation and machine learning to estimate what parameter values likely generated the temporal changes in diversity in our observed data. We found evidence that strong content bias, likely targeted towards syllable complexity, plays a central role in the cultural evolution of house finch song in western Long Island. Frequency and demonstrator biases appear to be neutral or absent. Additionally, we estimated that house finch song is transmitted with extremely high fidelity. Future studies should use our simulation framework to better understand how cultural transmission and population declines influence song diversity in wild populations.

Skeptical appeal: the source-content bias

Radical skepticism is the view that we know nothing, or at least next to nothing. Nearly no one actually believes that skepticism is true. Yet it has remained a serious topic of discussion for millennia and it looms large in popular culture. What explains its persistent and widespread appeal? How does the skeptic get us to doubt what we ordinarily take ourselves to know? I present evidence from two experiments that classic skeptical arguments gain potency from an interaction between two factors. First, people evaluate inferential belief more harshly than perceptual belief. Second, people evaluate inferential belief more harshly when its content is negative (i.e. that something is not the case) than when it’s positive (i.e. that something is the case). It just so happens that potent skeptical arguments tend to focus our attention on negative inferential beliefs, and we are especially prone to doubt that such beliefs count as knowledge. That is, our cognitive evaluations are biased against this specific combination of source and content. The skeptic sows seeds of doubt by exploiting this feature of our psychology.