Gossip drives vicarious learning and facilitates robust social connections - OSF
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Running head: GOSSIP SOCIAL LEARNING 1 2 3 4 5 6 7 Gossip drives vicarious learning and facilitates robust social 8 connections 9 10 Jolly, Eshin and Chang, Luke J. 11 12 13 14 15 16 17 18 19 20 21 22 Computational Social Affective Neuroscience Laboratory 23 Department of Psychological and Brain Sciences 24 Dartmouth College 25 Hanover, NH, 03755 26 Corresponding author (eshin.jolly.gr@dartmouth.edu) 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
GOSSIP SOCIAL LEARNING 43 Complex language and communication is one of the unique hallmarks that distinguishes 44 humans from most other animals. Interestingly, the overwhelming majority of our communication 45 consists of social topics involving self-disclosure and discussions about others, broadly 46 construed as gossip. Yet the precise social function of gossip remains poorly understood as 47 research has been heavily influenced by folk intuitions narrowly casting gossip as baseless 48 trash talk. Using a novel empirical paradigm that involves real interactions between a large 49 sample of participants we provide evidence that gossip is a rich, multifaceted construct, that 50 plays a critical role in vicarious learning and social bonding. We demonstrate how the visibility or 51 lack thereof of others’ behavior shifts conversational content between self-disclosure and 52 discussions about others. Social information acquired through gossip aids in vicarious learning, 53 directly influencing future behavior and impression formation. At the same time, conversants 54 come to influence each other, form more similar impressions, and build robust social bonds. 55 Consistent with prior work, gossip also helps promote cooperation in groups without a need for 56 formal sanctioning mechanisms. Altogether these findings demonstrate the rich and diverse 57 social functions and effects of this ubiquitous human behavior and lay the groundwork for future 58 investigations. 59 60 61 62 63 64 65 66
GOSSIP SOCIAL LEARNING 67 In a typical day, humans speak about 16,000 words (Mehl et al., 2007) and at least 65% 68 of these conversations involve discussing “social topics” (Dunbar, 2004). These social topics 69 primarily consist of self-disclosure (i.e. exchanges concerning the conversing parties) or 70 discussions about others (i.e. concerning absent third parties) (Dunbar et al., 1997; N. Emler, 71 1994). These types of exchanges have been long hypothesized to serve critical functions 72 including information exchange, cultural learning, entertainment, influence, and bonding 73 (Baumeister et al., 2004; Dunbar, 1998; Foster, 2004; Stirling, 1956). Yet, despite the ubiquity of 74 this behavior and its distinction from more general forms of communication (e.g. the exchange 75 of facts, instruction or non-personal exchanges), our scientific understanding of it is severely 76 limited. A key factor contributing to this stymied progress is the difficulty of establishing a 77 consensus definition amongst researchers (Wert & Salovey, 2004b). 78 At a broad level, previous work has considered social topics in conversation as a form of 79 gossip (Foster, 2004). However, this operationalization is at odds with folk intuitions, which 80 reflect the belief that gossip is limited to primarily negative evaluative commentary about absent 81 individuals, and hence generally taboo (Nicholas Emler, 1990). Talking negatively about an 82 absent third party may be a sufficient indicator of gossip, but is not a strictly necessary one 83 (Foster, 2004). Such a narrow definition fails to capture the diverse social value gossip can 84 provide (Bloom, 2004) and has hampered scientific progress. In a recent naturalistic study 85 investigating the base rates of gossip using an Electronically Activated Recorder, Robbins and 86 Karan (2019) found that gossip comprised approximately 14% of people’s daily conversations 87 and was primarily neutral in content rather than positively or negatively valenced. 88 In a general sense, “chit chat” or “idle talk,” in which individuals self-disclose provides an 89 opportunity for rewarding social connection, both because self-disclosure is intrinsically 90 rewarding (Tamir & Mitchell, 2012) and because it provides an opportunity to learn about others,
GOSSIP SOCIAL LEARNING
91 establish norms, trust, and culture (Baumeister et al., 2004; Dunbar, 1998). Observational
92 studies on school and workplace relationships provide descriptive accounts of gossip’s role in
93 building and configuring social ties (Shaw et al., 2011; Wittek & Wielers, 1998). Self-report
94 measures and attitudinal surveys such as the “need for gossip” scale (Nevo et al., 1994), have
95 identified a reliable association between the quality of interpersonal relationships and gossip.
96 For example, persistent self-reported gossip over time has been linked to increased friendship
97 and trust between gossipers (Ellwardt et al., 2012).
98 At the same time, gossip consisting of discussions about absent individuals provides an
99 opportunity to learn through the experiences of others (Sommerfeld et al., 2007). Far from
100 baseless commentary, evaluations of others can be seen as a form of meta-communication, in
101 which the topic of discussion is actually the implied acceptability of behaviors (i.e. norm
102 violations, morals) (Baumeister et al., 2004; Rosnow, 2001). In turn, these meta-discussions can
103 serve as a tool for reputation management and social regulation of group behavior. This has
104 been the primary focus of empirical research on gossip. In typical experimental paradigms,
105 individuals make decisions that can be selfish or cooperative and are directed to share
106 evaluative information about others, or made aware that others can share evaluative information
107 about them1. These conditions are sufficient to motivate individuals to behave more
108 cooperatively, even when compared to more direct sanctioning mechanisms such as costly
109 punishment (Wu et al., 2015, 2016). Evaluative commentary can inform group members’
110 decisions to ostracize selfish individuals through partner selection, thereby improving outcomes
111 for all remaining group members (Feinberg et al., 2014). In general, humans are motivated to
112 maintain a positive reputation amongst their social group because it attracts altruistic behavior
113 from others (Milinski, 2016). For this reason, when gossip consists of discussions about absent
114 1
In reality other “players” are often simulated agents or confederatesGOSSIP SOCIAL LEARNING 115 individuals, it provides an exchange of reputational information and a cheap means of social 116 control of behavior (Beersma & Van Kleef, 2011; Feinberg, Willer, et al., 2012; Giardini & Conte, 117 2012; Piazza & Bering, 2008/5). 118 Because empirical research has focused on this latter interpretation of gossip, much of 119 the psychological and economic literature has provided a limited perspective for our scientific 120 understanding. Indeed, in daily life, “social policing” has been estimated to comprise a mere 5% 121 of naturally occuring private conversations (Dunbar et al., 1997). Instead operationalizing gossip 122 more generally as conversations about social topics, provides a much richer foundation for 123 empirical research and can resolve the ambiguity that has limited scientific progress in this area, 124 highlighted in a topical issue in the Review of General Psychology nearly two decades ago 125 (Wert & Salovey, 2004b). 126 In the present work, we explore the complexity of gossip by focusing on understanding 127 several key aspects: (a) the conditions under which it emerges; (b) its role in information 128 transmission and vicarious learning; (c) its role in impression formation, impression sharing, and 129 social bonding; (d) and its influence on cooperative group behavior. To do so, we designed a 130 large online experiment in which individuals played one of four variants of a live and interactive 131 10-round repeated public-goods game in groups of six (Fehr & Gächter, 2002). We employed a 132 2x2 between-subjects factorial design to experimentally manipulate: (a) the amount of 133 information available about other players’ behavior and (b) whether or not it was possible to 134 gossip via private communication with a partner throughout the duration of the game (Figure 1). 135 Groups were designed such that participants were either able to see the actions made by all of 136 their neighbors (complete information), or only a subset of their neighbors (incomplete 137 information). Under these conditions, participants were sometimes able to privately exchange 138 two short, free-form messages with another individual during each round of the game.
GOSSIP SOCIAL LEARNING 139 Therefore, over the course of each game, repeated interactions occurred between the same 140 network of participants, enabling individuals to develop extended conversations, engage in 141 long-term learning, as well as potentially develop social relationships. 142 Because situational factors (in addition to conversational content) have been proposed 143 as one of the key features that distinguishes gossip from other forms of communication (Foster, 144 2004), this design allowed us to test the prediction that the mixture of social topics (i.e. 145 discussions about others vs chit-chat) would change based upon the visibility and shared 146 knowledge of others’ behavior. When social topics involved increased discussions about others’, 147 we hypothesized that they functioned to aid in vicarious learning by changing a conversant’s 148 own behavior and their impressions of others. On the other hand, when social topics involved 149 more chit-chat or idle-talk between participants, we expected that conversants would influence 150 each others’ behavior and increase the strength of their social connection as reflected by an 151 increased mutual affinity. 152 Methods 153 Participants 154 Participants (n=2,373) were recruited from Amazon’s Mechanical Turk marketplace 155 (Buhrmester et al., 2011) and consented to participation in accordance with the Committee for 156 the Protection of Human Subjects CPHS) at Dartmouth College. Because of the interactive 157 nature of the experiment, it was critical to recruit groups of participants that fully understood the 158 experiment, had no technical difficulties (i.e. were able to behave interactively in real-time), and 159 did not leave the experiment prematurely. To ensure these criteria were met, we employed a 160 rigorous vetting procedure (see supplementary materials for details). As a result, a smaller 161 subset of 954 participants (382 females; 79 not indicated; Mage= 33.74 years, SD= 9.80 years) 162 successfully completed the screening quiz, matched with other participants, and completed all
GOSSIP SOCIAL LEARNING 163 rounds of the experiment and are therefore included in all reported analyses. Individuals 164 participated in groups of six for the experiment, and were paid $1.50 as a baseline payment and 165 earned a bonus ($4.50 max) based on the decisions made by the group (M = $2.65; SD = 166 $0.62). Games lasted an average of 12 minutes, yielding an average total hourly pay-rate of 167 $20.75. 168 Each group played one of four variants of an iterated, 10 round public-goods style game. 169 Unlike previous work, games did not employ between-round rematching or shuffling (Feinberg 170 et al., 2014; Fowler & Christakis, 2010). Because the focus of this experiment was on modeling 171 real interactions between individuals it was necessary to build custom software that enabled 172 synchronous interactions between multiple participants over the web. In order to achieve this, 173 the experiment was built using the MeteorJS open-source framework in conjunction with 174 TurkServer (Parkes et al., 2012), an open-source platform for building web-based behavioral 175 experiments and interfacing with Mturk. Due to the inevitability of connection and participant 176 drop-out issues that arise during synchronous group experiments, all analyses were only 177 conducted on games with 10 complete rounds of data and no participant dropouts (Thomas & 178 Clifford, 2017). While formal power analyses were not conducted prior to data collection, the 179 authors were sensitive to issues surrounding small sample sizes and underpowered inferences 180 in psychological science. For this reason, to improve uncertainty estimates and achieve 181 adequate power, data collection aimed to achieve a useable sample size between three to four 182 times that of similar previous research (N = 954) (Feinberg et al, 2014, N = 216; Wu et al, 2016, 183 N = 265; Fehr & Gachter, 2002, N = 240; Fowler & Christakis, 2010; N = 240). For further 184 discussion of how participant recruitment and attrition was handled please see the 185 supplementary materials.
GOSSIP SOCIAL LEARNING 186 Figure 1 | Experimental Design. 187 a. Network structure for each group from the perspective of a focal participant (the cow in this instance). 188 Participants were always able to see the contributions of their immediate neighbors, but could only see 189 contributions from all other players in complete information games. In games with communication, 190 participants were able to exchange messages with a single other player. In incomplete information 191 games, this communication partner acted as a remote neighbor because their visibility of others’ 192 contributions did not overlap with the focal participant; in an incomplete information game, the cow would 193 see contributions from the bird and the lion, whereas the bee would instead see contributions from the cat 194 and the pig. b. The temporal ordering of events each game round. Private message exchanges (blue) 195 only occurred in games with communication. 196 197 Procedure 198 Design. Each participant was randomly assigned to one of four experimental conditions varying 199 along two crossed dimensions: information (complete/incomplete), communication (gossip/no 200 gossip). Participants were first presented with a series of instructions describing the basic rules 201 of a public goods game (Fehr & Gächter, 2002) and asked to pass a comprehension quiz 202 demonstrating they understood the rules of the game and the details of the condition in which 203 they were participating. Only participants who passed the comprehension quiz within two 204 attempts were permitted to participate. Games began when 6 eligible participants were found. 205 After participants completed all 10 rounds, they rated their desire for repeat play (affinity) with
GOSSIP SOCIAL LEARNING 206 every other participant in their group. 207 At the start of the game, participants were given animal avatars (used from: 208 https://github.com/niavlys/memoryKivy) to help them more easily identify themselves and other 209 individuals during the experiment. Each group of participants was organized as a static “ring 210 network” and proceeded in one of four variants. In the complete information without gossip 211 variant (NGames = 41; NParticipants = 246), participants played a canonical public goods game as 212 described below. In the incomplete information without gossip variant (NGames = 41; NParticipants = 213 246), participants were only able to see the contributions made by their immediate neighbors, 214 although group earnings were computed based on all participants’ contributions. In the complete 215 information with gossip variant (NGames = 37; NParticipants = 222), participants were able to send and 216 receive two 140 character private messages to their remote neighbor once per round after 217 seeing other individuals’ contributions. Unlike previous research, participants were free to say 218 anything they wanted (or nothing at all) and always communicated with the same individual 219 across all rounds (Feinberg et al., 2014; Sommerfeld et al., 2007; Wu et al., 2016). Critically, 220 these messages were private dyadic communication observable only to communicators and not 221 other group members. Finally, in the incomplete information with gossip variant (NGames = 40; 222 NParticipants = 240), participants were only able to see contributions made by their immediate 223 neighbors, but were still able to privately communicate with their remote neighbor, despite being 224 unable to see their behavior. 225 Therefore, from the perspective of a focal participant (cow) (Fig. 1a), each group 226 member fell into one of three different types of neighbors: (a) immediate - those whose 227 contribution behavior was always directly observed (bird and lion); (b) distant - those whose 228 contribution behavior could only be observed during complete information games (cat and pig); 229 (c) remote - those whose contribution behavior could only be observed during complete
GOSSIP SOCIAL LEARNING 230 information games and with whom they communicated during games that permitted 231 communication (bee). Using this group structure, remote neighbors (i.e. communication partners 232 in games where gossip was possible) were set up such that their immediate and distant 233 neighbors were reversed: the immediate neighbors of one participant were the distant neighbors 234 of another, and vice versa. Upon each game’s conclusion, participants rated their affinity for 235 every other participant by indicating their desire for repeat play with each of these neighbors 236 (Fig. 1b). 237 Public Goods Game. Each game consisted of 10 rounds with the same basic structure (Fig 238 1b): 1) participants received an initial endowment of 100 points and decided how many points to 239 contribute to a group account versus keep for themselves, 2) participants saw the decisions 240 made by other individuals in the group based on their condition, 3) players saw the earnings of 241 other individuals after contributions were summed, multiplied by 1.5 and evenly distributed to all 242 players. 4) If a game included communication, participants sent and received two messages 243 (one at a time) from another participant. At the end of the game a random round was selected to 244 calculate participant bonuses at the rate of 1 point = 2 cents. 245 Behavioral Analyses. All data were organized and restructured using the python data analysis 246 library (pandas) (McKinney, 2010) and visualizations were made using plotnine (Kibirige et al., 247 2018). All statistics were computed using the R statistical package (R Core Team, 2013) and 248 scientific tools implemented in python (Jones et al., 2001--). All linear mixed models were fit 249 using the lme4 package (Bates et al., 2015) and corrected marginal contrasts (i.e. minimizing 250 family-wise-error rates) were computed using the lsmeans package (Lenth, 2016) both 251 implemented via Pymer4 (Jolly, 2018). P-values for linear mixed-effects models were computed 252 using the lmerTest package (Kuznetsova et al., 2017) via Satterthwaite approximation for 253 degrees of freedom calculations, which has been demonstrated to produce reliable Type 1 error
GOSSIP SOCIAL LEARNING 254 rates (Luke, 2017). 255 Measuring communicative content. To measure communicative content, an additional sample 256 of 1,454 individuals was recruited from Amazon’s Mechanical Turk marketplace during June of 257 2016 to read and apply pre-specified labels to each message sent during the main experiment. 258 In order to ensure quality and consistency of labeling, individuals were provided with detailed 259 instructions that provided background context for the mechanics of a public-goods-game, in 260 addition to information about how to use each of 10 labels (see Supplementary Materials). 261 Individuals were also required to pass a comprehension quiz that ensured they understood the 262 meaning of each label type and were required to provide a complete set of labels for every 263 message in a single game. As a result of this rigorous procedure, only 381 individuals (179 264 females; 2 not indicated; Mage = 33.09 years, SD = 10.16 years), successfully consented, 265 passed our comprehension quiz, and provided a complete set of labels for a given game. Each 266 game was therefore labeled by several different individuals (M = 4.94, SD = 0.87). Participants 267 were paid a base payment ($0.50) for accepting the HIT and attempting the comprehension 268 quiz, and were paid a bonus ($2.50) for completing the full labeling task. 269 In order to derive an index of communicative content, we first counted how often each 270 label was applied to any given message and converted this to a proportion aggregating across 271 responses from all individuals. Second, we applied the label comprising the highest proportion 272 as the label for a particular message. Finally, using these labels, we computed the proportion of 273 each label occurrence across all messages and games and submitted these values to a 274 chi-square test of independence. This method allowed us to capture the multifaceted content 275 that may comprise gossip and observe how this content changes based on situational factors. 276 Time-lagged analyses. Time-lagged effects were estimated using linear mixed-effects models 277 separately for games with complete and incomplete information. Each model estimated an
GOSSIP SOCIAL LEARNING 278 individual’s contribution on Roundt+1, as a function of each of their neighbor’s contributions at 279 Roundt, controlling for their own contribution at Roundt, along with a contrast code for 280 communication. That is, each model contained fixed-effects for centered game round, 281 participants’ centered contribution at Roundt, a categorical contrast code for communication, the 282 centered contributions of each of three neighbors (immediate, distant, and remote) at Roundt, 283 along with interaction terms between neighbor contributions, round, and communication. Prior to 284 modeling, contributions for each pair of immediate and distant neighbors were averaged such 285 that only three types of neighbors were entered into the model. To model variance across 286 participants, random intercepts and slopes across game rounds, were estimated for each 287 individual. More complex random-effects structures yielded unstable model estimates that failed 288 to achieve convergence during estimation. Parameterized in this way, the interaction terms 289 between specific neighbor contributions and communication represented the contrast between 290 games with and without gossip. Complete model estimates can be found in supplementary 291 Tables 1 and 2. 292 Affinity. Subsequent post-game affinity ratings were modeled in three ways. For analyses one 293 and two, prior to modeling, ratings for each pair of immediate and distant neighbors were 294 averaged such that neighbor role was a factor containing only three levels (one for each 295 neighbor type). First, to test for overall differences in affinity, a linear mixed effects model was 296 estimated in which ratings were modeled as a function of orthogonally coded fixed effects of 297 game type (four levels), neighbor type (three levels), and their interaction. To model variance 298 across games and participants in each analysis, random intercepts were estimated for individual 299 games and individual participants. More complex random-effects structures yielded unstable 300 model estimates that failed to achieve convergence during estimation. Analysis of Variance 301 tests were computed on each fixed effect to estimate overall differences between factor levels.
GOSSIP SOCIAL LEARNING 302 To examine differences between specific neighbor types, least-squares pairwise comparisons 303 were conducted, correcting for multiple-comparisons by controlling familywise error rates at 304 alpha = 0.05. A full list of these comparisons is available in supplementary Table 3. 305 Second, to test for interactions with behavior, a separate linear mixed effects model was 306 estimated in which ratings were modeled as a function of fixed effects for centered average 307 contributions across all game rounds, game type, neighbor type, and the interaction between all 308 three. To model variance across games and participants in each analysis, random intercepts 309 were estimated for individual games and individual participants. Analysis of Variance tests were 310 computed on each fixed effect to estimate overall differences between factor levels. 311 Subsequently, specific least-squares pairwise comparisons were conducted, correcting for 312 multiple-comparisons by controlling familywise error rates at alpha = 0.05. A full list of these 313 comparisons is available in supplementary Table 4. 314 To test for inter-individual similarity of affinity ratings, we computed the similarity between 315 affinity ratings made by focal participants’ and the same ratings made by their remote 316 neighbors. Specifically, we computed the euclidean distance between vectors of length four for 317 each pair of such participants. For focal participants, this vector included affinity ratings of their 318 distant neighbors (left and right) and their immediate neighbors (left and right). For the matching 319 remote neighbor this vector included affinity ratings of their immediate neighbors (right and left) 320 and their distant neighbors (right and left). We used this distance metric because (a) it reflects 321 interpretable values on the original 100 point rating scale used by participants (b) it is calculable 322 and interpretable even when participants rate all neighbors identically (i.e. if vectors have zero 323 variance). Distance scores were contrasted between games where communication was and 324 was not possible separately for full information games and incomplete information games. 325 Inference was performed via permuting group labels and recomputing means 5000 times to
GOSSIP SOCIAL LEARNING 326 build null distributions. We also examined the relationship between inter-individual similarity of 327 affinity ratings and remote neighbors ratings of each other. Specifically, we correlated the mean 328 affinity of each pair of remote neighbors with their inter-individual similarity ratings (reported in 329 main text), as well as the absolute value difference of affinity of each pair of remote neighbors 330 with their inter-individual similarity ratings. These analyses were performed separately for 331 incomplete (Fig S2) and complete information games (Fig S4) and all inference was performed 332 via permuting each vector of inter-individual similarity ratings and re-running the correlation 333 5000 times to build null distributions. 334 Group Contribution Behavior. Differences in group contributions were estimated using a linear 335 mixed effects model in which contributions were modeled as a function of fixed effects for 336 centered game round, categorical contrast codes for gossip and information, and interactions 337 between all three. To model variance across games and participants, random intercepts and 338 round slopes were estimated for individual games and individual participants. A full list of model 339 estimates can be found in supplementary Table 5. 340 Results 341 Visibility of others’ behavior specifically changes the content of gossip 342 Consistent with the idea that situational factors play a key role in distinguishing types of 343 gossip (Foster, 2004), we found that games with incomplete information contained proportionally 344 more spontaneous discussions about others’ behavior relative to games with complete 345 information (17% vs 10% of all messages), X2 (1, N = 9,240) = 94.52, p < 0.001 (Figure 2). On 346 the other hand, we found that casual chit-chat and positive affirmations (statements indicating 347 agreement) occurred more often in games with complete information than in games with 348 incomplete information (27.5% vs 25% of all messages), (X2 (1, N = 9,240) = 7.20, p = 0.007) 349 and (14.6% vs 12.9% of all messages), (X2 (1, N = 9,240) = 5.62, p = 0.018) respectively. In
GOSSIP SOCIAL LEARNING 350 other words, in circumstances where direct observation of others’ was not possible, participants 351 shifted the content of their discussion to focus more on how other individuals behaved while 352 instead focusing on more self-relevant topics when social visibility was not restricted. Similar to 353 real world statistics, these social discussion topics made up the majority of all conversations 354 (~54%) that participants had (Dunbar, 2004; Robbins & Karan, 2019). No other conversational 355 topics differed based on visibility of others’ behavior. 356 Figure 2 | Communicative Content. Communicative content labels applied to exchanges in games with 357 communication (see supplemental materials for details on how coding was performed). Casual chit-chat, 358 positive affirmations, discussions about others, and discussing strategy were the most common topics in 359 communicative exchanges. Participants discussed others’ behavior significantly more in games with 360 incomplete information relative to games with complete information. Participants also exchanged 361 significantly more chit-chat and positive affirmations in games with complete information relative to games 362 with incomplete information. No other types of communication differed between game types. *p < 0.05 363 364 Gossip facilitates vicarious learning in the absence of direction observation 365 To test the prediction that gossip functions as a mechanism for vicarious learning, we 366 examined how conversation impacted participants’ behavior by evaluating how a focal 367 participant’s future behavior was influenced by each of their neighbor’s past behavior when this 368 behavior was not visible (Fowler & Christakis, 2010) and contrasted these estimates between
GOSSIP SOCIAL LEARNING
369 games with and without communication.
370 Despite a lack of direct observation (Fig 3A), both distant and remote neighbors’
371 contributions from past rounds were significantly more predictive of focal participants’
372 contributions in future rounds in games where gossip was possible b = 0.10, t(3273.76) = 2.77,
373 p = 0.006, b = 0.07, t(3466.83) = 2.60, p = 0.009, respectively. Importantly, the influence of
374 distant neighbors was unlikely to be indirectly mediated through immediate neighbors as our
375 model explicitly controls for their behavior. Additionally, the influence of participants’ immediate
376 (visible) neighbors was marginally lower when participants could communicate relative to when
377 they could not b = -0.07, t(2957.40) = -1.89, p = 0.058, demonstrating a decreased reliance on
378 the only behavior participants were able to directly observe (full model results appear in
379 Supplemental Table 1). Given the deliberate circular arrangement of each game’s network, a
380 focal participant’s unobservable distant neighbors were the observable immediate neighbors of
381 their remote neighbor (communication partner) (Fig. 1A). Therefore, the only way in which a
382 focal participant could come to be influenced by individuals they could not directly observe
383 (distant neighbors), was via information transmitted through another person (remote neighbor)
384 who could observe those individuals.
385 Interestingly, we also found that vicarious learning influenced participants’ impressions of
386 individuals they could not observe. Participants’ affinity ratings for both distant and remote
387 neighbors were consistently predicted by their unobservable behavior in games where gossip
388 was possible, b = 0.38, t(1699.10) = 4.53, p < 0.001, b = 0.30, t(1997.40) = 4.48, p < 0.001,
389 respectively (Fig. 3C). Furthermore, impressions of these unobserved players were likely
390 directly influenced by social discussions, as there was a significant increase in similarity2 of
391 affinity ratings between conversing individuals, M = -10.75, p < 0.001 (Fig. 3B). In turn,
392 2
Mean change in euclidean distance between ratings. Smaller values indicate decreased distance, i.e.
393 increased similarity. Inference via permutation testing, see Methods for details.GOSSIP SOCIAL LEARNING
394 impression similarity between conversants was associated with higher mutual affinity ratings3, r
395 = 0.20, p = .002, (Figure S2).
396 Taken together these results provide strong evidence that discussions about other
397 individuals provide a learning signal in situations where direct observation is not possible. When
398 individuals cannot observe the members of their social group, they turn to others to vicariously
399 learn about their social environment through dialogue. Information transmitted via gossiping was
400 the only way in which any information about unobserved neighbors was available to
401 participants. Critically, the information gleaned through gossip is adaptive: participants not only
402 change their own behavior, but also change their social impressions by adopting the views of
403 their conversation partner.
404 Gossip increases social connection
405 Because building social bonds has been proposed as another function of gossip, we
406 expected that conversants would influence each other to a greater degree when it was
407 unnecessary to discuss the actions of others (i.e. complete information games). Consistent with
408 this idea, we found that remote neighbors’ past contributions were significantly more predictive
409 of focal participants’ future contributions in complete information games where gossip was
410 possible relative to games where it was not b = 0.09, t(3486.55) = 3.40, p < 0.001 (Fig. 3a).
411 Conversely, immediate neighbors’ past contributions were significantly less predictive in games
412 where gossip was possible relative to when it was not, b = -0.08, t(3361.67) = -2.26, p = 0.024
413 (Table S2 contains full model results). In this same context, conversing participants reported the
414 highest affinity for each other (M = 81.12, SD = 30.58) relative to all other group members
415 (remote neighbors no-communication M = 57.93, SD = 38.14; immediate neighbors M = 64.63,
416 3
Correlation between affinity euclidean distance (Fig 3B) and average affinity rating of each pair of
417 communicators. Inference via permutation testing, see Methods for details.
418GOSSIP SOCIAL LEARNING 419 420 Figure 3 | Vicarious Learning and Information Transmission without Direct Observation. 421 A. Time-lagged influence contrasts between incomplete information games with and without 422 communication from the perspective of a focal participant. Each arrow represents the test-statistic on the 423 contrast (communication versus no communication) between how much a specific neighbor’s past 424 contribution predicted a focal participant’s future contribution. Warmer colors indicate a stronger degree of 425 influence (higher parameter estimate) in games with communication and cooler colors represent a 426 stronger degree of influence in games without communication for that type of neighbor. Despite a lack of 427 direct observation, in games where gossip was possible, the past behaviors of a focal participant’s distant 428 and remote neighbors were more predictive of their future behavior while the past behaviors of their 429 immediate neighbors were less predictive of their future behavior. B. Focal participants and their remote 430 neighbors’ affinity ratings of other players were more similar (decreased euclidean distance) in games 431 where gossip was possible. C. The relationship between a focal participant’s affinity toward their neighbor 432 and their neighbor’s average contribution. Despite a lack of direct observation this relationship was 433 stronger for games where gossip was possible for both remote (b = 0.34) and distant neighbors (b = 434 0.52), relative to games where gossip was not possible: remote (b = 0.04), distant (b = 0.14). Immediate 435 neighbors were always visible and never communicated with and therefore demonstrated no difference 436 between game contexts. All error bars and bands represent standard errors (SEM). 437 438 SD = 31.53; distant neighbors M = 64.11, SD = 32.18) t(205.22) = 5.05, p < 0.001, Cohen’s d = 439 0.67, t(1900) = 7.86, p < 0.001, Cohen’s d = 0.53, t(1900) = 8.13, p < 0.001, Cohen’s d = 0.54,
GOSSIP SOCIAL LEARNING 440 respectively (Table S3; Figure S3). At the same time, conversing participants had more similar 441 impressions of others M = -9.71, p < 0.0081 (Fig 4B), and those conversants who felt the most 442 positive about each other showed the greatest degree of similarity, r = 0.28, p < .0012. 443 Furthermore, conversation moderated the importance of behavior on the impressions that 444 conversants formed of each other b = -0.22, t(1535.74) = -3.42, p = 0.004 (Fig 4C left panel). 445 These results speak to the impact that gossip can have on individuals’ behaviors and 446 impressions when social topics focus less on discussions about others. Specifically, this form of 447 social dialogue makes conversants influence each other’s actions and social impressions, while 448 building a social bond that is robust to deviant behavior. This provides support for the idea that 449 gossip serves not only as a mechanism to learn about the social world at large, but also as a 450 means for bringing individuals closer together via self-disclosure and learning directly about 451 each other (Dunbar et al., 1997). 452 The opportunity to gossip motivates cooperative behavior 453 Consistent with prior work, the analysis of each group as a whole revealed that the 454 opportunity for gossip increased overall cooperation. Like prior public goods dilemmas 455 (Chaudhuri, 2010), average group contributions declined over the course of each game b = 456 -2.15, t(155) = -7.75, p < 0.001, but critically, games with the potential for gossip (b = -1.22) 457 exhibited less of a decline than games without (b = -3.08), b = 1.86, t(155) = 3.35, p = 0.001 458 (Figure 4). On average, participants contributed significantly more when they could gossip (M = 459 61.31; SD = 6.26) relative to when they could not (M = 51.20; SD = 9.44), b = 10.22, t(155) = 460 2.52, p = 0.013. Without complete information, participants contributed marginally less on 461 average (M = 52.60; SD = 6.54) relative to when they had complete information (M = 59.74; SD 462 = 7.66), b = 7.26, t(155) = 1.79, p = 0.075. Other interactions were not significant (all ts < 1, all 463 ps > 0.50). These results replicate prior work demonstrating how communication, and
GOSSIP SOCIAL LEARNING 464 465 Figure 4 | Social Influence and Affinity with Direct Observation. A. Time-lagged influence contrasts 466 between complete information games with and without communication from the perspective of a focal 467 participant. Each arrow represents the test-statistic on the contrast (communication versus no 468 communication) between how much a specific neighbor’s past contribution predicted a focal participant’s 469 future contribution. Warmer colors indicate a stronger degree of influence (higher parameter estimate) in 470 games with communication and cooler colors represent a stronger degree of influence in games without 471 communication for that type of neighbor. In games where gossip was possible, the past behavior of a 472 focal participant’s remote neighbor was significantly more predictive of their future behavior, while the past 473 behavior of a focal participant’s immediate neighbors were less predictive of their future behavior. B. 474 Focal participants and their remote neighbors’ affinity ratings of other players were more similar 475 (decreased euclidean distance) in games where gossip was possible. C. The relationship between a focal 476 participant’s affinity toward their neighbor and their neighbor’s average contribution. This relationship was 477 stronger for remote neighbors in games without communication (b = 0.80) and was moderated as a 478 consequence of being able to communicate (b = 0.58). No differences were observed for distant or 479 remote neighbors across game contexts. All error bars and bands represent standard error (SEM). 480 481 specifically gossip, can be an effective social means to sustain cooperation without the need for 482 additional interventions (Balliet, 2010; Feinberg et al., 2014; Wu et al., 2016). Interestingly, we 483 observe that this effect is not because all individuals cooperate more, but rather that a subset of
GOSSIP SOCIAL LEARNING 484 individuals become more cooperative while others remain self-interested (bimodal distribution 485 Figure 5B Left). This is consistent with the idea that not all individuals are equally motivated by 486 reputational concerns, but rather may exhibit variations in their individual preferences (Nevo et 487 al., 1994; Wert & Salovey, 2004a). 488 Discussion 489 In the present work, we explored the complexity of gossip by focusing on: (a) the 490 conditions under which it emerges; (b) its role in information transmission and vicarious 491 learning; (c) its role in impression formation, impression sharing, and social bonding; (d) and its 492 influence on cooperative group behavior. A novel contribution of our approach was building a 493 method to facilitate real social interactions with financial consequences, in which individuals 494 were able to have largely unrestricted conversations. Key to our investigation was the 495 manipulation of information visibility (whether or not participants were privy to actions of all or a 496 subset of their group members) and the ability to communicate (exchange of private dyadic 497 communication). 498 Across all games in which communication was possible, social topics (discussions about 499 others, chit-chat, and affirmations) constituted the majority of what individuals discussed, yet the 500 makeup of these topics differed based on information visibility. Participants more frequently 501 discussed others’ behavior when it was not directly observable. Unlike the folk view of gossip as 502 baseless “trash talk,” these exchanges served a clear social function: vicarious learning. 503 Participants adjusted their future behavior based on knowledge of others’ unobserved past 504 actions reported by their conversation partner. Participants also adjusted their social judgments, 505 utilizing communicated information about behavior when forming impressions of others. These 506 findings provide direct evidence that situational factors influence what gossip ultimately looks 507 like and demonstrate that gossip can provide a rich source of information to aid in navigating the
GOSSIP SOCIAL LEARNING 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 Figure 5 | Gossip and Group Behavior. a. Average group contributions across game rounds (left). 528 Group contributions show less of a decline effect over multiple rounds when gossiping is possible. Games 529 with complete information also show less of a decline effect over rounds relative to games with 530 incomplete information. Mean contribution is higher on average in game with communication (right). All 531 error bars and bands represent standard error (SEM). b. Distribution of contribution behavior over rounds 532 displays a different pattern in games where gossip is or is not possible. Contribution behavior converges
GOSSIP SOCIAL LEARNING 533 to a stable bimodal distribution when participants can communicate (left), yet displays a prominent zero 534 contribution skew when they cannot (right). 535 536 social environment. 537 These findings complement and extend previous work on information transmission in the 538 absence of direct observation (Sommerfeld et al., 2007). Unlike previous work, our participants 539 were not instructed or incentivized in any way to discuss particular topics or exchange specific 540 messages. Rather, like real world conversations (Dunbar, 2004) gossip emerged spontaneously 541 and influenced how participants behaved in the game despite a lack of mechanisms like 542 ostracization or punishment (Feinberg et al., 2014; Wu et al., 2016). One interpretation of this 543 finding, is that discussing others’ behavior served as form of meta-communication whereby 544 conversants talked about the implied acceptability of certain behaviors (e.g. “What they did was 545 bad, right?”) thereby facilitating the construction or maintenance of moral rules or social norms 546 (Baumeister et al., 2004). In this way, leveraging and checking-in with the experiences of others 547 provided an efficient mechanism for participants to improve the quality of their future outcomes. 548 Gossip also played a key role in bringing individuals closer together. In particular, 549 conversational content shifted more towards chit-chat and positive affirmation when each 550 participant’s behavior was visible to all group members. Social topics that consist of 551 self-disclosures, constitute a different dimension of gossip than evaluations of absent others, 552 and have been long thought to be the primary ingredients of social bonding (Dunbar, 1998). 553 Consistent with this idea, we found that conversational partners influenced each other to a 554 greater degree than other group members, felt more positive about each other compared to 555 other group members, and came to share similar impressions of other players by the end of the 556 game. Interestingly, these latter two effects were related. Participants who felt the most 557 positively about each other also tended to have the most similar impressions of others. This
GOSSIP SOCIAL LEARNING 558 alignment of impressions may provide a distinct mechanism by which gossip can bring 559 individuals closer together. Prior work has suggested that telegraphing trust by exchanging 560 personal information through gossip can aid in building alliances between individuals (Foster, 561 2004; Hannerz, 1967; Merry, 1997). We speculate that participants established a sense of 562 commonality with one another, creating a “shared reality” that served to influence each other’s 563 behavior and perspectives (Echterhoff et al., 2009) while satisfying each other’s inherent desire 564 for social connection (Baumeister & Leary, 1995). This idea is consistent with observational 565 work demonstrating the strong relationship between workplace gossip and friendship over time 566 (Ellwardt et al., 2012). 567 At the group level, the possibility of gossip also sustained cooperative behavior for 568 longer. Replicating previous research, individuals tended to contribute more money when they 569 had the opportunity to gossip with one another (Beersma & Van Kleef, 2011; Feinberg et al., 570 2014; Wu et al., 2016). This led to a higher mean group contribution at the end of the game, but 571 upon closer examination, we note that the mean contribution did not appear to fully characterize 572 the underlying behavioral dynamics over time. Without the possibility of gossip, groups gradually 573 switch to investing all of their money in the beginning of the game to keeping it all at the end 574 (Figure 5B right blue distributions), similar to the highly replicated unraveling of cooperation 575 observed in traditional public goods games (Fehr & Gächter, 2002; Feinberg et al., 2014). In 576 games where gossip is possible, contribution behavior appeared to stabilize into a bimodal 577 distribution consisting of extreme cooperators and self-interested players (Figure 5B left red 578 distributions). While it's possible that the cooperation would eventually unravel if participants 579 played more rounds, another possibility is that gossip may have been sufficient to disrupt the 580 self-interested equilibrium in the game. This could be facilitated through the building of social 581 bonds over time or alternatively through reputation management by providing a cheap form of
GOSSIP SOCIAL LEARNING
582 social sanctioning (Feinberg, Cheng, et al., 2012; Milinski et al., 2002; Piazza & Bering, 2008/5).
583 While we cannot adjudicate between these possibilities with our design, this presents an
584 interesting opportunity for future research. By varying the amount and channels of
585 communication between group members (e.g. discussions between more than two players to
586 form coalitions), it may be possible to stabilize this bimodal equilibrium and possibly lead more
587 groups to adopt a maximal contribution strategy.
588 Altogether, our findings provide a novel characterization of gossip and its social
589 functions. The ability to glean knowledge about one’s social world appears to be a key factor
590 that changes the makeup of social topics in conversation. Entertainment, influence, and social
591 bonding may emerge as consequences from gossip consisting more heavily of “idle-talk,”
592 “chit-chat,” and self-disclosure, because the primary communicative purpose is connecting with
593 another individual. This is consistent with the “social grooming hypothesis” (Dunbar, 1998), the
594 idea that communicative exchanges between individuals provide the building blocks of social
595 bonds akin to physical grooming in other primate species. Our data demonstrate that
596 unacquainted individuals can form robust social bonds through private discussions in a shared
597 social context4. Interestingly, much like real friendships, gossip partners’ affinity towards one
598 another was robust to how each partner actually behaved, indicating that their relationship was
599 based more on their communicative behavior rather than how they played the game.
600 When communication serves as a tool for vicarious learning, gossip may align more
601 closely with our folk intuitions, i.e. evaluative discussions of other people. This is readily
602 apparent with the advent of the term “whisper network” popularized by the #MeToo movement
603 and later additional social movements, whereby individuals shared private information about
604 4
As an extreme example, one of our participants used the MTurk reviewing platform, Turktopticon
605 (turkopticon.ucsd.edu) to try to reach out and send a positive message to their communication partner
606 from the game, even after they had finished participating in our study.
607GOSSIP SOCIAL LEARNING 608 dangerous individuals to be avoided. This is consistent with a coalitional view of gossip (Wittek 609 & Wielers, 1998), in which bonds form between individuals who are engaged in private 610 discourse with each other about an absent third-party. By sharing the experiences of others, 611 gossip enables communities to grow and protect their members from having negative first-hand 612 experiences. 613 Given the incredible complexity of gossip, future research could benefit by adopting a 614 less narrow definition and move away from the influence of folk intuitions about its defamatory 615 and taboo nature. Defamatory comments comprise a relatively small proportion of real world 616 gossip (Robbins & Karan, 2019) and there are many more features of gossip that contribute to 617 social connection. Instead investigating how humans use gossip as means to establish culture, 618 build trust, learn vicariously, and influence one another is a vast and largely unexplored avenue 619 that can grow our scientific understanding of this ubiquitous behavior. 620 621 622 623 624 625 626 627 628 629 630 631
GOSSIP SOCIAL LEARNING 632 Open Practices Statement. The custom online platform to run the experiment is available at: 633 https://github.com/cosanlab/PGG_meteor. Data and analysis code that support the findings, and 634 a preprint of this manuscript are available at: https://psyarxiv.com/qau5s/ 635 636 Acknowledgements. 637 We would like to thank Emma Templeton, Jin Hyun Cheong, Antonia Hoidal, and Alec Smith for 638 helpful feedback about this manuscript. We would also like to thank Andrew Mao and Lili 639 Dworkin for software assistance during initial development of our experiment platform. 640 641 Author contributions. 642 E.J. and L.J.C designed the study. E.J. built the experiment platform, collected, and analyzed 643 the data. E.J. and L.J.C. wrote the manuscript. 644 645 Competing financial interests. 646 The authors declare no competing financial interests. 647
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