Network-based diffusion analysis: a new method for detecting social learning.

Although social learning capabilities are taxonomically widespread, demonstrating that freely interacting animals (whether wild or captive) rely on social learning has proved remarkably challenging. Network-based diffusion analysis (NBDA) offers a means for detecting social learning using observational data on freely interacting groups. Its core assumption is that if a target behaviour is socially transmitted, then its spread should follow the connections in a social network that reflects social learning opportunities. Here, we provide a comprehensive guide for using NBDA. We first introduce its underlying mathematical framework and present the types of questions that NBDA can address. We then guide researchers through the process of selecting an appropriate social network for their research question; determining which NBDA variant should be used; and incorporating other variables that may impact asocial and social learning. Finally, we discuss how to interpret an NBDA model's output and provide practical recommendations for model selection. Throughout, we highlight extensions to the basic NBDA framework, including incorporation of dynamic networks to capture changes in social relationships during a diffusion and using a multi-network NBDA to estimate information flow across multiple types of social relationship. Alongside this information, we provide worked examples and tutorials demonstrating how to perform analyses using the newly developed nbda package written in the R programming language.

There has been relatively little research on the psychological mechanisms of social learning. This may be due, in part, to the practice of distinguishing categories of social learning in relation to ill-defined mechanisms (Davis, 1973; Galef, 1988). This practice both makes it difficult to identify empirically examples of different types of social learning, and gives the false impression that the mechanisms responsible for social learning are clearly understood. It has been proposed that social learning phenomena be subsumed within the categorization scheme currently used by investigators of asocial learning. This scheme distinguishes categories of learning according to observable conditions, namely, the type of experience that gives rise to a change in an animal (single stimulus vs. stimulus-stimulus relationship vs. response-reinforcer relationship), and the type of behaviour in which this change is detected (response evocation vs. learnability) (Rescorla, 1988). Specifically, three alignments have been proposed: (i) stimulus enhancement with single stimulus learning, (ii) observational conditioning with stimulus-stimulus learning, or Pavlovian conditioning, and (iii) observational learning with response-reinforcer learning, or instrumental conditioning. If, as the proposed alignments suggest, the conditions of social and asocial learning are the same, there is some reason to believe that the mechanisms underlying the two sets of phenomena are also the same. This is so if one makes the relatively uncontroversial assumption that phenomena which occur under similar conditions tend to be controlled by similar mechanisms. However, the proposed alignments are intended to be a set of hypotheses, rather than conclusions, about the mechanisms of social learning; as a basis for further research in which animal learning theory is applied to social learning. A concerted attempt to apply animal learning theory to social learning, to find out whether the same mechanisms are responsible for social and asocial learning, could lead both to refinements of the general theory, and to a better understanding of the mechanisms of social learning. There are precedents for these positive developments in research applying animal learning theory to food aversion learning (e.g. Domjan, 1983; Rozin & Schull, 1988) and imprinting (e.g. Bolhuis, de Vox & Kruit, 1990; Hollis, ten Cate & Bateson, 1991). Like social learning, these phenomena almost certainly play distinctive roles in the antogeny of adaptive behaviour, and they are customarily regarded as 'special kinds' of learning (Shettleworth, 1993).(ABSTRACT TRUNCATED AT 400 WORDS)

Experiments in captivity have provided evidence for social learning, but it remains challenging to demonstrate social learning in the wild. Recently, we developed network-based diffusion analysis (NBDA; 2009) as a new approach to inferring social learning from observational data. NBDA fits alternative models of asocial and social learning to the diffusion of a behavior through time, where the potential for social learning is related to a social network. Here, we investigate the performance of NBDA in relation to variation in group size, network heterogeneity, observer sampling errors, and duration of trait diffusion. We find that observation errors, when severe enough, can lead to increased Type I error rates in detecting social learning. However, elevated Type I error rates can be prevented by coding the observed times of trait acquisition into larger time units. Collectively, our results provide further guidance to applying NBDA and demonstrate that the method is more robust to sampling error than initially expected. Supplemental materials for this article may be downloaded from http://lb.psychonomic-journals.org/content/supplemental.

Group living animals use social and asocial cues to predict the presence of reward or punishment in the environment through associative learning. The degree to which social and asocial learning share the same mechanisms is still a matter of debate. We have used a classical conditioning paradigm in zebrafish, in which a social (fish image) or an asocial (circle image) conditioned stimulus (CS) have been paired with an unconditioned stimulus (US=food), and we have used the expression of the immediate early gene c-fos to map the neural circuits associated with each learning type. Our results show that the learning performance is similar to social and asocial CSs. However, the brain regions activated in each learning type are distinct and a community analysis of brain network data reveals segregated functional submodules, which seem to be associated with different cognitive functions involved in the learning tasks. These results suggest that, despite localized differences in brain activity between social and asocial learning, they share a common learning module and social learning also recruits a specific social stimulus integration module. Therefore, our results support the occurrence of a common general-purpose learning module, that is differentially modulated by localized activation in social and asocial learning.

A critical consequence of joining social groups is the possibility of social transmission of information related to novel behaviours or resources. Network‐based diffusion analysis (NBDA) has emerged as a leading frequentist framework for inferring and quantifying social transmission, particularly in non‐human animal populations. NBDA has been extended several times to account for multiple diffusions, multiple networks, individual‐level variables and complex transmission functions. Bayesian versions of NBDA have been proposed before, although these implementations have seen limited usage and have not kept pace with the evolving ecosystem of Bayesian methods. There is not yet a user‐friendly package to implement a Bayesian NBDA. Here, we present a unified framework for performing Bayesian analysis of social transmission using NBDA‐type models, implemented in the widely used Stan programming language. We provide a user‐friendly R package ‘STbayes’ (ST: social transmission) for other researchers to easily use this framework. STbayes accepts user‐formatted data, but can also import data directly from the existing NBDA R package. Based on the data users provide, STbayes automatically generates multi‐network, multi‐diffusion models that allow for covariates that may influence transmission and varying (random) effects. Using simulated data, we demonstrate that this model can accurately differentiate the relative contribution of individual and social learning in the spread of information through networked populations. We illustrate how incorporating upstream uncertainty about the relationships between individuals can improve model fit. Our framework can be used to infer complex transmission rules, and we describe a numerically stable parametrization of frequency‐dependent transmission. Finally, we introduce support for dynamic transmission weights and a ‘high‐resolution’ data mode, which allows users to make use of fine‐scale data collected by contemporary automated tracking methods. These extensions increase the set of contexts that this type of model may be used for.

Cumulative cultural evolution has been claimed to be a uniquely human phenomenon pivotal to the biological success of our species. One plausible condition for cumulative cultural evolution to emerge is individuals’ ability to use social learning to acquire know-how that they cannot easily innovate by themselves. It has been suggested that chimpanzees may be capable of such know-how social learning, but this assertion remains largely untested. Here we show that chimpanzees use social learning to acquire a skill that they failed to independently innovate. By teaching chimpanzees how to solve a sequential task (one chimpanzee in each of the two tested groups, n = 66) and using network-based diffusion analysis, we found that 14 naive chimpanzees learned to operate a puzzle box that they failed to operate during the preceding three months of exposure to all necessary materials. In conjunction, we present evidence for the hypothesis that social learning in chimpanzees is necessary and sufficient to acquire a new, complex skill after the initial innovation.

The use of information provided by others to tackle life's challenges is widespread, but should not be employed indiscriminately if it is to be adaptive. Evidence is accumulating that animals are indeed selective and adopt ‘social learning strategies’. However, studies have generally focused on fish, bird and primate species. Here we extend research on social learning strategies to a taxonomic group that has been neglected until now: otters (subfamily Lutrinae). We collected social association data on captive groups of two gregarious species: smooth-coated otters (Lutrogale perspicillata), known to hunt fish cooperatively in the wild, and Asian short-clawed otters (Aonyx cinereus), which feed individually on prey requiring extractive foraging behaviours. We then presented otter groups with a series of novel foraging tasks, and inferred social transmission of task solutions with network-based diffusion analysis. We show that smooth-coated otters can socially learn how to exploit novel food sources and may adopt a ‘copy when young’ strategy. We found no evidence for social learning in the Asian short-clawed otters. Otters are thus a promising model system for comparative research into social learning strategies, while conservation reintroduction programmes may benefit from facilitating the social transmission of survival skills in these vulnerable species.

Culture is an outcome of both the acquisition of knowledge about behaviour through social transmission, and its subsequent production by individuals. Acquisition and production are often discussed or modelled interchangeably, yet to date no study has explored the consequences of their interaction for cultural diffusions. We present a generative model that integrates the two, and ask how variation in production rules might influence diffusion dynamics. Agents make behavioural choices that change as they learn from their productions. Their repertoires may also change, and the acquisition of behaviour is conditioned on its frequency. We analyse the diffusion of a novel behaviour through social networks, yielding generalizable predictions of how individual-level behavioural production rules influence population-level diffusion dynamics. We then investigate how linking acquisition and production might affect the performance of two commonly used inferential models for social learning; network-based diffusion analysis, and experience-weighted attraction models. We find that the influence that production rules have on diffusion dynamics has consequences for how inferential methods are applied to empirical data. Our model illuminates the differences between social learning and social influence, demonstrates the overlooked role of reinforcement learning in cultural diffusions, and allows for clearer discussions about social learning strategies.

In nature, animals often ignore socially available information despite the multiple theoretical benefits of social learning over individual trial-and-error learning. Using information filtered by others is quicker, more efficient and less risky than randomly sampling the environment. To explain the mix of social and individual learning used by animals in nature, most models penalize the quality of socially derived information as either out of date, of poor fidelity or costly to acquire. Competition for limited resources, a fundamental evolutionary force, provides a compelling, yet hitherto overlooked, explanation for the evolution of mixed-learning strategies. We present a novel model of social learning that incorporates competition and demonstrates that (i) social learning is favoured when competition is weak, but (ii) if competition is strong social learning is favoured only when resource quality is highly variable and there is low environmental turnover. The frequency of social learning in our model always evolves until it reduces the mean foraging success of the population. The results of our model are consistent with empirical studies showing that individuals rely less on social information where resources vary little in quality and where there is high within-patch competition. Our model provides a framework for understanding the evolution of social learning, a prerequisite for human cumulative culture.

Human cognition is distinguished by our ability to adapt to different environments and circumstances. Yet the mechanisms driving adaptive behavior have predominantly been studied in separate asocial and social contexts, with an integrated framework remaining elusive. Here, we use a collective foraging task in a virtual Minecraft environment to integrate these two fields, by leveraging automated transcriptions of visual field data combined with high-resolution spatial trajectories. Our behavioral analyses capture both the structure and temporal dynamics of social interactions, which are then directly tested using computational models sequentially predicting each foraging decision. These results reveal that adaptation mechanisms of both asocial foraging and selective social learning are driven by individual foraging success (rather than social factors). Furthermore, it is the degree of adaptivity—of both asocial and social learning—that best predicts individual performance. These findings not only integrate theories across asocial and social domains, but also provide key insights into the adaptability of human decision-making in complex and dynamic social landscapes.

Individual learners often show a tendency to engage in self-reinforcing avoidance, a pattern referred to as a learning trap. Across five experiments, we investigated the extent to which previously trapped learners can escape via social observational learning. While social observational learning did help a significant number of trapped learners escape, the majority of trapped learners remained trapped even after observing a partner demonstrate an optimal decision rule. Across several follow-up experiments, we unpack possible factors which limited the effectiveness of social observational learning. Overall, the results suggest that social decision rule inference (inferring a partner's decision rule from observed choices) was a key bottleneck for observational learning. Simulations show that these results were unanticipated by a leading model of social reward learning, and highlight a central role for inference in social learning.

Little is known about how multiple social learning strategies interact and how organisms integrate both individual and social information. Here we combine, in a wild primate, an open diffusion experiment with a modeling approach: Network-Based Diffusion Analysis using a dynamic observation network. The vervet monkeys we study were not provided with a trained model; instead they had access to eight foraging boxes that could be opened in either of two ways. We report that individuals socially learn the techniques they observe in others. After having learnt one option, individuals are 31x more likely to subsequently asocially learn the other option than individuals naïve to both options. We discover evidence of a rank transmission bias favoring learning from higher-ranked individuals, with no evidence for age, sex or kin bias. This fine-grained analysis highlights a rank transmission bias in a field experiment mimicking the diffusion of a behavioral innovation.

Critical thinking is essential in education as it enhances students' cognitive abilities during learning activities. However, critical thinking has not yet become a prevalent habit among students, resulting in some students being passive in the learning process, hesitant to express opinions, or ask questions. The social inquiry learning model in Islamic Education subjects is an approach that adapts to students' experiences, enabling them to find answers independently. Consequently, the social inquiry learning model can foster critical thinking in students. The purpose of this research is to describe the impact of the social inquiry learning model in Islamic Education subjects on the development of students' critical thinking skills. This will help determine the extent of the relationship between the application of the social inquiry learning model in Islamic Education subjects (Variable X) and the development of students' critical thinking skills (Variable Y). The method used in this research is the inferential quantitative method, which involves collecting data or information about the phenomenon being studied. Data collection techniques include questionnaires distributed to 25 respondents, observations, and documentation studies. Data processing was conducted through statistical data analysis. The research results show that the social inquiry learning model in Islamic Education subjects achieved a score of 4.056, placing it within the 3.01-4.00 interval range, classified as good. Meanwhile, the development of students' critical thinking skills had an average score of 3.976, also within the 3.01-4.00 interval range, classified as good. Based on statistical tests, there is an influence of the social inquiry learning model in Islamic Education subjects at 38.59%, while other factors influencing students' critical thinking skills account for 61.41% but were not examined in this study. Keywords : Social Inquiry Learning Model, Islamic Education Subject, Students' Critical Thinking

PurposeWidespread concerns about excessive use of mobile instant messaging (MIM) have inspired research in different disciplines. However, the focus thus far tends to be on diagnostics and cognitive predictors. There is little understanding from the socio-technical perspective to capture the drivers of excessive use of MIM. To address this research gap, the authors aim to enrich existing literature by adopting a social structure and social learning model (SSSLM) to understand the excessive use of MIM. The authors argue that excessive MIM use is developed and reinforced in highly interactive online communication, through a social learning process.Design/methodology/approachThe authors conduct a cross-sectional online survey to validate our proposed research model on excessive use of mobile instant messaging (MIM). 368 valid responses are obtained from active MIM users in China.FindingsThe results suggest that highly interactive MIM creates a technology-based social structure that facilitates the social learning process of excessive technology use. The influence of perceived interactivity of MIM on excessive MIM use is mediated by a series of contextualized social learning factors. Furthermore, the influences of perceived interactivity on social learning factors are moderated by MIM use experience.Originality/valueThe authors contribute to literature in related fields by highlighting the crucial role of social learning in facilitating excessive technology use. The authors contribute to the social structure and social learning model by contextualizing it into the context of excessive MIM use. Design guidelines are provided with a purpose to inhibit excessive use of MIM.

The term 'diffusion' refers to the spread of a character, such as a novel behaviour, through a population. While such diffusions often result from social learning, there are other types of social influence, as well as non-social processes, which can account for this spread. Network-based diffusion analysis (NBDA) infers, and quantifies, the strength of social influence in a set of diffusion data by assessing the extent to which the pattern of spread follows a social network. Here the chapter illustrates the application of NBDA in a Bayesian context with the use of a simulated dataset. The chapter extends current NBDA models to incorporate random effects and facilitate model discrimination. The chapter employs a Reversible jump Markov Chain Monte Carlo algorithm to discriminate between models and determine which model provides the best fit to the data. This novel methodology is particularly useful to analyse datasets that include many covariates and thus can be fitted with a correspondingly large number of competing models.