1.1. The social evolution of human cognition and language

Humans are exceptional among primates in several respects, but among the most salient are the ability to use language and the large size of our brains. The human brain is over three times larger than the brain of a primate of similar body size (Marino, Reference Marino1998), which comes with several types of costs. In terms of energy expenditure, the brain accounts for up to 50% of the basal metabolic rate during childhood (Armstrong, Reference Armstrong1983; Milton, Reference Milton, Byrne and Whiten1988) and ca. 20% in adulthood, still higher than for other organs (Aiello & Dunbar, Reference Aiello and Dunbar1993), and the brain’s high susceptibility to oxygen deprivation necessitates a stable energy supply (Byrne, Reference Byrne2000). Developmentally, very large postnatal growth required for the brain to reach adult size makes humans unusually helpless at birth and exceptionally dependent on parental or alloparental care. There are also allometric costs related to the much larger head in proportion to the rest of the body in humans, both in neonates (e.g. complications during labour) and in adults (e.g. complicating balance in bipedal walking – Abitbol, Reference Abitbol1993). In sum, we would expect such traits to be selected against unless their high costs trade off against equally weighty fitness advantages; in the case of human brain size, these are typically interpreted in the context of cognitive specialisation (Byrne, Reference Byrne2000).

Historically, theories of human cognitive evolution leading to high encephalisation highlighted ecological challenges, such as tool use (Vaesen, Reference Vaesen2012), social technology transmission (Nicol, Reference Nicol1995; see Shilton, Reference Shilton2019, for review) and food acquisition benefits (DeLouize et al., Reference DeLouize, Coolidge and Wynn2017). Spatial cognition (Epstein et al., Reference Epstein, Patai, Julian and Spiers2017), hunting (Speth, Reference Speth2010) and extractive foraging (Bickerton, Reference Bickerton, Hurford, Studdert-Kennedy and Knight1998; see also Byrne, Reference Byrne2000) were also proposed as potential drivers of increased brain size in the hominin lineage. Since the advent of the Machiavellian intelligence hypothesis (Whiten & Byrne, Reference Whiten, Byrne, Byrne and Whiten1988), these ecological explanations have been gradually complemented by a social perspective, in which the social environment was as important as the physical environment in shaping primate cognitive evolution. The literature also indicates differences in social communication: men speak more and louder during arguments (Kimble & Musgrove, Reference Kimble and Musgrove1988), while women use gossip more for competition (Buss & Dedden, Reference Buss and Dedden1990) and engage in more affiliative speech, especially with children (Leaper & Ayres, Reference Leaper and Ayres2007; Leaper & Smith, Reference Leaper and Smith2004). Hyde and Linn’s (Reference Hyde and Linn1988) meta-analysis suggests women might have slightly higher verbal ability.

The research by Dunbar (see, e.g., Dunbar, Reference Dunbar2009) has been particularly important in spearheading this point of view. The influential social brain hypothesis (e.g. Dunbar, Reference Dunbar2010) holds that the selection pressures for the increased neocortex size in hominins reflected the high cognitive demands of dealing with increasingly complex social groups. This is because successful survival and reproduction in large and intricately organised primate groups require the capacity to understand and navigate fluid social hierarchies, form and break short- and long-term alliances and coalitions and keep a balance between cooperation and conflict. Since the amount of relevant social information grows exponentially with group size, its efficient processing places high computational demands on the brain, particularly its neocortical areas. The requirements of dealing with social (rather than ecological) information would thus have been the main driver of the evolution of human cognition. This includes the ability to use language, which might have evolved as an all-purpose social tool to transmit social information (Dahmardeh & Dunbar, Reference Dahmardeh and Dunbar2017; Mesoudi et al., Reference Mesoudi, Whiten and Dunbar2006; Pleyer, Reference Pleyer2023; Redhead & Dunbar, Reference Redhead and Dunbar2013; Wacewicz, Reference Wacewicz, Dor, Knight and Lewis2015; see also Dunbar & Shultz, Reference Dunbar and Shultz2023; Shultz & Dunbar, Reference Shultz and Dunbar2022).

1.2. The study by Dunbar, Marriott, and Duncan, Reference Dunbar, Marriott and Duncan1997

Dunbar et al. (Reference Dunbar, Marriott and Duncan1997) analysed the content of 45 casual conversations held in public settings by manually recording the general topic of speakers’ utterances. The content was analysed speaker by speaker. The authors used scan sampling, which generally provides accurate estimates of the amount of time devoted to an activity. They sampled the topic at a specific moment in time and repeated the process every 30 seconds. The topics were classified into several thematic categories, with a subset of them later grouped as concerning social topics. While groundbreaking in using linguistic analysis to inform evolutionary considerations, and despite being further confirmed in another study that showed that the proportion of time devoted to social topics was 76% (Dahmardeh & Dunbar, Reference Dahmardeh and Dunbar2017), the study had several limitations related to its dataset and approach.

From today’s perspective, some limitations of the study by Dunbar et al. (Reference Dunbar, Marriott and Duncan1997) derive from the size and composition of the sample. While 45 conversations are similar to other self-collected corpora of casual conversations of the time (e.g. Gumperz & Tannen, Reference Gumperz and Tannen1979; Tannen & Wallat, Reference Tannen, Wallat, Fisher and Todd1983) and may be sufficient for qualitative analyses (cf. Saville-Troike, Reference Saville-Troike, Lange and Paige2003), quantitative analyses now tend to rely on much larger datasets. The advent of the Digital Age at the beginning of the 20th century allowed researchers to collect rich conversational data in various formats (text, audio and video) and use computational tools to quantify these data (Yeomans et al., Reference Yeomans, Boland, Collins, Abi-Esber and Brooks2023). Furthermore, the sample was heavily biased towards young urban adults with academic backgrounds: over three-quarters of the conversations were held by students in two university cafeterias (in Liverpool and London, UK). Finally, the conversations were acquired in open public environments, which might not be representative of the content of conversations held in completely private settings (cf. Heritage & Stivers, Reference Heritage, Stivers, Sidnell and Stivers2012, for the role of socioeconomic variables in conversation analysis).

The design of the study implied several logistic challenges. First, as surreptitious eavesdroppers, the coders might not have heard the full content of at least some conversations, and second, they had to identify and record the topics in real time, simultaneously listening to the conversations. Dunbar and colleagues acknowledge that the process of assigning a topic to a subject area could be difficult and that, in some cases, the annotators had to rely on their interpretation of the speaker’s intentions to make a single choice. Notably, these classifications were based on a single coder’s intuition at one of three locations (Dunbar et al., Reference Dunbar, Marriott and Duncan1997, p. 236), which may prompt questions about the reliability of the results reported in the study (Luginbühl et al., Reference Luginbühl, Mundwiler, Kreuz, Müller-Feldmeth and Hauser2021). While Dunbar et al.’s data suggest high coder agreement, it did not involve the now-standard procedure of achieving consensus agreement among at least two coders or measuring inter-rater agreement for at least part of the coded material (Stolarova et al., Reference Stolarova, Wolf, Rinker and Brielmann2014).

Finally, and most importantly for the current analysis, the study by Dunbar et al. (Reference Dunbar, Marriott and Duncan1997) had a much broader focus than classifying the content of talk into social vs. non-social. For this reason, it did not provide an explicit operationalisation of these two categories or the difference between them; instead, the amount of speaking time taken up by social topics was a sum of the time devoted to lower-level topics that were jointly grouped as ‘social’. The authors’ original classification had fourteen subject areas, which were later reclassified into ten major categories: personal relationships, personal experiences, future social activity, future non-social activity, sport/leisure, culture/art/music, politics, religion/morals/ethics, work/academic and technical/instructional. Each subject area contained several topics; e.g., the category ‘personal relationships’ included personal experiences arising from social events, social relationships and actual behaviour in social situations, as well as the emotional experiences involved. The authors explain that they ‘delineated the topics a priori to reflect functionally relevant categories’, i.e. subject categories based on their functions (1997: 235).

1.3. What language use counts as ‘social’?

‘Social’ is an everyday language word with a very broad range of meanings, which makes ‘talking about social matters’ challenging to operationalise. In this study, our approach is directly guided by an evolutionary perspective: we investigate current patterns of language use because we take them to be informative on the nature of human-evolved cognition. This is in line with the basic tenets of evolutionary psychology (Buss, Reference Buss2019; Tooby & Cosmides, Reference Tooby, Cosmides, Barkow, Cosmides and Tooby1992) and aligns with research in environmental aesthetics (Kaplan, Reference Kaplan, Barkow, Cosmides and Tooby1992; Orians & Heerwagen, Reference Orians, Heerwagen, Barkow, Cosmides and Tooby1992), Darwinian literary theory (Carroll, Reference Carroll1995, Reference Carroll2011) or evolutionary psychology of the media (Szlendak & Kozłowski, Reference Szlendak and Kozłowski2008; Tooby & Cosmides, Reference Tooby and Cosmides2001), all of which assume that the types of content that humans find interesting reflect main adaptive challenges, i.e. recurrent adaptive problems of our ancestors. Similar to Dunbar et al. (Reference Dunbar, Marriott and Duncan1997), we assume that the broad categories of content that the conversants find worthwhile speaking about reflect the relative importance of the social and ecological challenges in our evolutionary past.

In line with the above, we were interested in the proportion of linguistic information conveyed in conversation that pertains to social versus non-social matters. Similar to Dunbar et al. (Reference Dunbar, Marriott and Duncan1997), our focus was not on the organisation of the talk, for example the development and sequencing of topics (Heritage, Reference Heritage, Sidnell and Stivers2012). Rather, we treated utterances as blocks of static text as if they were single-voice documents (a standalone document created by one person; Yeomans et al., Reference Yeomans, Boland, Collins, Abi-Esber and Brooks2023) and identified their content as either social or non-social (see below). Accordingly, we labelled instances of language use as ‘social’ when their content was related to managing or navigating social situations rather than non-social situations (cf. Section 2). So construed, ‘social content’ is a very broad category that encompasses all utterances related to one’s social connections within a group, containing information primarily relevant to and useful for managing one’s social relations. Conversely, ‘non-social’ content relates more closely to other aspects of human life and may be more useful in contexts outside of human relationshipsFootnote ¹.

In our study, we specified three categories with increasing social distance to the individuals being discussed in the content of the conversation (see Table 1). The first category of ‘Social’ concerns individuals participating in the conversation. Speaking about oneself might not intuitively be considered to count as ‘social’ in the sense that it may not directly involve other people. Indeed, in our preliminary studies (e.g. Szala et al., Reference Szala, Placiński, Poniewierska, Szczepańska, Wacewicz, Ravignani, Asano, Valente, Ferretti, Hartmann, Hayashi, Jadoul, Martins, Oseki, Rodrigues, Vasileva and Wacewicz2022), contra Dunbar et al. (Reference Dunbar, Marriott and Duncan1997), we decided to exclude the subcategory of ‘personal experiences’ as not exemplifying the category ‘social’. However, the present grounding of our categories in terms of types of content domains related to this content’s evolutionary relevance made us reconsider this decision. From this perspective, self-disclosure plays a key role in building social relationships, both through self-promotion and as an invitation for the other speaker to do likewiseFootnote ². More broadly, sharing information about oneself belongs to the content domain of ‘information about people’ and constitutes a key mechanism for building one’s own reputation. When accompanied by others, people tend to share information about the intensity of their emotional reactions (Stefanczyk et al., Reference Stefanczyk, Lizak, Kowal and Sorokowska2022) or sexual desires (Stefanczyk, Reference Stefanczyk2024) that deviate from reality for the sake of their image in the eyes of others. Furthermore, people vary in what traits they decide to highlight when put in the context of a romantic date (Stefanczyk et al., Reference Stefanczyk, Conroy-Beam, Ujma, Walter, Zborowska and Sorokowska2024). Thus, what and when we disclose about ourselves may be considered a tool we use to navigate social interactions successfully.

Table 1. Coding instructions and examples from the database per line of text for five categories

The second category of ‘Social’ concerns sharing information about people not physically present during the conversation but belonging to the extended social circle of at least one of the conversants. The ‘extended social circle’ stands for the range of people with whom we have or potentially could have some form of relationship: people whom we either actually know personally or could realistically get to know personally. In other words, my ‘extended social circle’ encompasses people with whom I can realistically have future non-transient social interactions, and information on those people can potentially be useful in guiding those interactions (cf. also Krems et al., Reference Krems, Dunbar and Neuberg2016, on forming mental models of absent individuals).

We distinguish this category from the previous one for two reasons. First, this category is instrumental in extending the reputation-building of individuals beyond the interacting dyad. It provides a powerful source of information on third parties that is alternative or complementary to one’s own first-hand experience of interactions. Access to this additional, non-direct source of information is quite fundamental for indirect reciprocity (Nowak & Sigmund, Reference Nowak and Sigmund2005), whereby individual A helps individual B even without B directly reciprocating the help; instead, A can reliably count on being ‘helped back’ by other individuals in the social network. Indirect reciprocity is one of the key mechanisms implicated in the origin of human cooperative norms (including moral norms), which are at the core of a number of approaches to the evolution of human cooperation (e.g. Ohtsuki & Iwasa, Reference Ohtsuki and Iwasa2006). Second, this discrimination aligns well with the most common understanding of the term ‘gossip’, i.e. ‘informal and evaluative talk (…) about another member (…) who is not present’ (Kurland & Pelled, Reference Kurland and Pelled2000), or to put it simply, a private transmission between ‘A and B talking about C’ (Hannerz, Reference Hannerz1967).

The third category of ‘Social’ concerns instances of language use that contain information on people outside of the conversants’ extended social circle; that is, individuals with whom one is highly unlikely (or impossible) to have social interactions in the foreseeable future. As such, this type of information – a reputation of people that we will not meet anyway – does not have direct social value in the specific sense described above. However, exchanging social information, such as details about the lives of movie stars, likely engages the same information-processing mechanisms that we use when processing information about members of our own social circle; indeed, this is a standard explanation of the popularity of celebrities suggested in evolutionary media studies (e.g. Szlendak & Kozłowski, Reference Szlendak and Kozłowski2008). Also, information on unknown and unknowable individuals still contains explicit or implicit general advice on handling social situations. This is again visible in evolutionarily inspired research on the content of oral and written literature (e.g. Carroll, Reference Carroll2012; Gottschall, Reference Gottschall2005; Saunders, Reference Saunders, Shackelford and Hansen2015), which has been argued to have fitness-enhancing value in the sense of transmitting information useful in dealing with recurring adaptive challenges.

To further refine our analysis, we divided Category 3 into subcategories 3A and 3B to differentiate between general information about people (3A) and information specifically related to their social lives (3B). This distinction was influenced by our preliminary study (Szala et al., Reference Szala, Placiński, Poniewierska, Szczepańska, Wacewicz, Ravignani, Asano, Valente, Ferretti, Hartmann, Hayashi, Jadoul, Martins, Oseki, Rodrigues, Vasileva and Wacewicz2022), and it allows us to capture the difference between these two approaches to coding ‘social’: broadly, any information about people, and narrowly, information specifically related to their social lives. By comparing 3 versus 3A or 3B, we can identify the proportion of social information that is general versus that which is socially specific within this category, which may reflect this proportion across all our ‘social’ categories.