Generalized Morality Culturally Evolves as an Adaptive Heuristic in Large Social Networks

Jackson, J. C., Halberstadt, J., et al.

(2023, March 22).

https://doi.org/10.31234/osf.io/sx4rt

Abstract

Why do people assume that a generous person should also be honest? Why can a single criminal conviction destroy someone’s moral reputation? And why do we even use words like “moral” and “immoral”? We explore these questions with a new model of how people perceive moral character. According to this model, people can vary in the extent that they perceive moral character as “localized” (varying across many contextually embedded dimensions) vs. “generalized” (varying along a single dimension from morally bad to morally good). This variation might be at least partly the product of cultural evolutionary adaptations to predicting cooperation in different kinds of social networks. As networks grow larger and more complex, perceptions of generalized morality are increasingly valuable for predicting cooperation during partner selection, especially in novel contexts. Our studies show that social network size correlates with perceptions of generalized morality in US and international samples (Study 1), and that East African hunter-gatherers with greater exposure outside their local region perceive morality as more generalized compared to those who have remained in their local region (Study 2). We support the adaptive value of generalized morality in large and unfamiliar social networks with an agent-based model (Study 3), and experimentally show that generalized morality outperforms localized morality when people predict cooperation in contexts where they have incomplete information about previous partner behavior (Study 4). Our final study shows that perceptions of morality have become more generalized over the last 200 years of English-language history, which suggests that it may be co-evolving with rising social complexity and anonymity in the English-speaking world (Study 5). We also present several supplemental studies which extend our findings. We close by discussing the implications of this theory for the cultural evolution of political systems, religion, and taxonomical theories of morality.

General Discussion

The word“moral” has taken a strange journey over the last several centuries. The word did not yet exist when Plato and Aristotle composed their theories of virtue. It was only when Cicero translated Aristotle’s Nicomachean Ethics that he coined the term “moralis” as the Latin translation of Aristotle’s “ēthikós”(Online Etymology Dictionary, n.d.).It is an ironic slight to Aristotle—who favored concrete particulars in lieu of abstract forms—that the word has become increasingly abstract and all-encompassing throughout its lexical evolution, with a meaning that now approaches Plato’s “form of the good.” We doubt that this semantic drift isa coincidence.

Instead, it may signify a cultural evolutionary shift in people’s perceptions of moral character as increasingly generalized as people inhabit increasingly larger and more unfamiliar social networks. Here we support this perspective with five studies. Studies 1-2 find that social network size correlates with the prevalence of generalized morality. Studies 1a-b explicitly tie beliefs in generalized morality to social network size with large surveys.  Study 2 conceptually replicates this finding in a Hadza hunter-gatherer camp, showing that Hadza hunter-gatherers with more external exposure perceive their campmates using more generalized morality. Studies 3-4 show that generalized morality can be adaptive for predicting cooperation in large and unfamiliar networks. Study 3 is an agent-based model which shows that, given plausible assumptions, generalized morality becomes increasingly valuable as social networks grow larger and less familiar. Study 4 is an experiment which shows that generalized morality is particularly valuable when people interact with unfamiliar partners in novel situations. Finally, Study 5 shows that generalized morality has risen over English-language history, such that words for moral attributes (e.g., fair, loyal, caring) have become more semantically generalizable over the last two hundred years of human history.

The research is here.

Network Structure Impacts the Synchronization of Collective Beliefs

Vlasceanu, M., Morais, M. J., & Coman, A. 

(2021). Journal of Cognition and Culture.

Abstract

People’s beliefs are influenced by interactions within their communities. The propagation of this influence through conversational social networks should impact the degree to which community members synchronize their beliefs. To investigate, we recruited a sample of 140 participants and constructed fourteen 10-member communities. Participants first rated the accuracy of a set of statements (pre-test) and were then provided with relevant evidence about them. Then, participants discussed the statements in a series of conversational interactions, following pre-determined network structures (clustered/non-clustered). Finally, they rated the accuracy of the statements again (post- test). The results show that belief synchronization, measuring the increase in belief similarity among individuals within a community from pre-test to post-test, is influenced by the community’s conversational network structure. This synchronization is circumscribed by a degree of separation effect and is equivalent in the clustered and non- clustered networks. We also find that conversational content predicts belief change from pre-test to post-test.

From the Discussion

Understanding the mechanisms by which collective beliefs take shape and change over time is essential from a theoretical perspective (Vlasceanu, Enz, Coman, 2018), but perhaps even more urgent from an applied point of view.  This urgency is fueled by recent findings showing that false news diffuse farther, faster, deeper, and more broadly than true ones in social networks (Vosoughi, Roy, Aral, 2018), and that news can determine what people discuss and even change their beliefs (King, Schneer, White, 2017). And given that beliefs influence people’s behaviors (Shariff & Rhemtulla, 2012; Mangels, Butterfield, Lamb, Good, Dweck, 2006; Ajzen, 1991; Hochbaum, 1958), understanding the dynamics of collective belief formation is of vital social importance as they have the potential to affect some of the most impending threats our society is facing from pandemics (Pennycook, McPhetres, Zhang, Rand, 2020) to climate change (Benegal & Scruggs, 2018). Thus, policy makers could use such findings in designing misinformation reduction campaigns targeting communities (Dovidio & Esses, 2007; Lewandowsky et al., 2012). For instance, these findings suggest such campaigns be sensitive of the conversational network structures of their targeted communities. Knowing how members of these communities are connected, and leveraging the finding that people synchronize their beliefs mainly with individuals they are directly connected to, could inform intervention designers how communities with different connectivity structures might respond to their efforts. For example, when targeting a highly interconnected group, intervention designers could expect that administering the intervention to a few well-connected individuals will have a strong impact at the community level. In contrast, when targeting a less interconnected group, intervention designers could administer the intervention to more central individuals for a comparable effect. 

The research is here.

Popularity is linked to neural coordination: Neural evidence for an Anna Karenina principle in social networks

Baek, E. C.,  et al. (2021)

https://doi.org/10.31234/osf.io/6fj2p

Abstract

People differ in how they attend to, interpret, and respond to their surroundings. Convergent processing of the world may be one factor that contributes to social connections between individuals. We used neuroimaging and network analysis to investigate whether the most central individuals in their communities (as measured by in-degree centrality, a notion of popularity) process the world in a particularly normative way. More central individuals had exceptionally similar neural responses to their peers and especially to each other in brain regions associated with high-level interpretations and social cognition (e.g., in the default-mode network), whereas less-central individuals exhibited more idiosyncratic responses. Self-reported enjoyment of and interest in stimuli followed a similar pattern, but accounting for these data did not change our main results. These findings suggest an “Anna Karenina principle” in social networks: Highly-central individuals process the world in exceptionally similar ways, whereas less-central individuals process the world in idiosyncratic ways.

Discussion

What factors distinguish highly-central individuals in social networks? Our results are consistent with the notion that popular individuals (who are central in their social networks) process the world around them in normative ways, whereas unpopular individuals process the world around them idiosyncratically. Popular individuals exhibited greater mean neural similarity with their peers than unpopular individuals in several regions of the brain, including ones in which similar neural responding has been associated with shared higher-level interpretations of events and social cognition (e.g., regions of the default mode network) while viewing dynamic, naturalistic stimuli. Our results indicate that the relationship between popularity and neural similarity follows anAnna Karenina principle. Specifically, we observed that popular individuals were very similar to each other in their neural responses, whereas unpopular individuals were dissimilar both to each other and to their peers’ normative way of processing the world.  Our findings suggest that highly-central people process and respond to the world around them in a manner that allows them to relate to and connect with many of their peers and that less-central people exhibit idiosyncrasies that may result in greater difficulty in relating to others.

The research is here.

You are a network

Kathleen Wallace

aeon.com

Originally published

Here is an excerpt:

Social identities are traits of selves in virtue of membership in communities (local, professional, ethnic, religious, political), or in virtue of social categories (such as race, gender, class, political affiliation) or interpersonal relations (such as being a spouse, sibling, parent, friend, neighbour). These views imply that it’s not only embodiment and not only memory or consciousness of social relations but the relations themselves that also matter to who the self is. What philosophers call ‘4E views’ of cognition – for embodied, embedded, enactive and extended cognition – are also a move in the direction of a more relational, less ‘container’, view of the self. Relational views signal a paradigm shift from a reductive approach to one that seeks to recognise the complexity of the self. The network self view further develops this line of thought and says that the self is relational through and through, consisting not only of social but also physical, genetic, psychological, emotional and biological relations that together form a network self. The self also changes over time, acquiring and losing traits in virtue of new social locations and relations, even as it continues as that one self.

How do you self-identify? You probably have many aspects to yourself and would resist being reduced to or stereotyped as any one of them. But you might still identify yourself in terms of your heritage, ethnicity, race, religion: identities that are often prominent in identity politics. You might identify yourself in terms of other social and personal relationships and characteristics – ‘I’m Mary’s sister.’ ‘I’m a music-lover.’ ‘I’m Emily’s thesis advisor.’ ‘I’m a Chicagoan.’ Or you might identify personality characteristics: ‘I’m an extrovert’; or commitments: ‘I care about the environment.’ ‘I’m honest.’ You might identify yourself comparatively: ‘I’m the tallest person in my family’; or in terms of one’s political beliefs or affiliations: ‘I’m an independent’; or temporally: ‘I’m the person who lived down the hall from you in college,’ or ‘I’m getting married next year.’ Some of these are more important than others, some are fleeting. The point is that who you are is more complex than any one of your identities. Thinking of the self as a network is a way to conceptualise this complexity and fluidity.

Let’s take a concrete example. Consider Lindsey: she is spouse, mother, novelist, English speaker, Irish Catholic, feminist, professor of philosophy, automobile driver, psychobiological organism, introverted, fearful of heights, left-handed, carrier of Huntington’s disease (HD), resident of New York City. This is not an exhaustive set, just a selection of traits or identities. Traits are related to one another to form a network of traits. Lindsey is an inclusive network, a plurality of traits related to one another. The overall character – the integrity – of a self is constituted by the unique interrelatedness of its particular relational traits, psychobiological, social, political, cultural, linguistic and physical.

The info is here.