Reasoning beyond available data is a ubiquitous feature of human cognition. But while the availability of first-hand data typically diminishes as the concepts we reason about become more complex, our ability to draw inferences seems not to. We may offset the sparsity of direct evidence by observing the statements of others, but such social meta-inference comes with challenges of its own. The strength of socially-provided evidence depends on multiple factors which themselves must be inferred, like the knowledge, social goals, and independence of the people providing the data. Here, we present the results of an experiment aimed at examining how people draw conclusions from information provided by others in the context of social media posts. By systematically varying the degree of consensus along with the number of people and distinct arguments involved we are able to assess how much each factor affects the conclusions reasoners draw. Across a range of topics we find that while people are influenced by the number of people on each side of an argument, the number of posts is the dominant factor driving belief revision. In contrast to well established findings in simpler domains, we find that people are largely insensitive to the diversity of the arguments made.

Sampling assumptions — the assumptions people make about how an example of a category or concept has been chosen — help us learn from examples efficiently. One context where sampling assumptions are particularly important is in social contexts, where a learner needs to infer the knowledge and intentions of the information provider and vice-versa. The pedagogical sampling assumptions model describes a Bayesian account of how learners and providers should behave given different assumptions they have about the other (e.g., is the provider trying to deceive or help me? Does the learner trust me?). In this study, we tested how well this model could describe learning behaviour in the rectangle game, where a fictional information provider revealed clues about the structure of a rectangle that the learner (a participant) needed to guess. Participants received clues from either a helpful information provider, a provider who was randomly sampling clues, or one of two kinds of unhelpful providers (who could mislead but could not lie). We found that people learned efficiently and in line with model predictions when the provider was helpful and that this was the case even when no cover story was provided. However, although participants could identify that unhelpful providers were not being helpful, they struggled to learn the strategy those providers were using.

How and why do people share opinions online? In research conducted offline, the social identity of the audience is a key factor: whether they are composed of one’s ingroup or outgroup affects what people share and why. Do people behave similarly and for similar reasons online? To test this, we put participants (N = 326) in imaginary forums belonging to their ingroup and outgroup. In each, people selected statements to share, along with reasons for doing so. The results showed a high degree of heterogeneity; people shared nearly all kinds of statements with both audiences, for a variety of reasons. However, there were also consistent patterns. Identity expression was the most common reason for sharing to both audiences, but this led to different things being shared to each. To the ingroup, people preferred to share statements expressing ingroup beliefs, while to the outgroup, they preferred statements expressing universal beliefs.

Making inferences about claims we do not have direct experience with is a common feature of everyday life. In these situations, it makes sense to consult others: an apparent consensus may be a useful cue to the truth of a claim. This strategy is not without its challenges. The utility of a consensus should depend in part on the sources of evidence that underlie it. If each person based their conclusion on independent data then the fact that they agree is informative. If, instead, everyone relied on the same primary source, the consensus is less meaningful. However, the extent to which people are actually sensitive to this kind of source independence is still unclear. Here, we present the results of three experiments that examine this issue in a social media setting, by varying the sources of primary data cited via retweets. In each experiment, participants rated their agreement with 12 different claims before and after reading four tweets that were retweeted on the basis of either the same or different primary data. We found that people were sensitive to source independence only when it was clear that the tweeters had relied on the primary data to reach their conclusion. Implications for existing research are discussed.

Communication relies on a shared understanding of word meaning; however, recent evidence suggests that individual variation in meaning exists even for common nouns. Understanding where and how this variation arises is therefore integral to circumnavigating misunderstandings and facilitating more efficient communication. This study investigated the degree to which men and women ascribe different meanings to the same words. Experiment 1 used a constrained word association task where participants generated three adjectives for each of 42 words. These data were used in Experiment 2, where a separate sample judged the association strength between word pairs. Both experiments investigated the role of gender in word meaning variation and found evidence for gender-specific meaning for a substantial fraction of the 42 words (Experiment 1: 12 or 29\%; Experiment 2: 13 or 31\%). Experiment 2 also investigated whether conceptual diversity can be explained by gender. Using Gaussian mixture modelling, we found evidence for 62 clusters (indicating concepts), with over 30\% of words mapping onto multiple concepts. Evidence for gender-specific concepts was found for nearly half (46\%) of the words with multiple clusters. Moreover, gender differences in meaning were not restricted to gender-connoted words but included apparently neutral words. Altogether, the results demonstrate how male and female speakers of the same language may have slightly different conceptual representations, even of common English nouns.

People have different intentions when sharing information online. However, are others able to interpret these motives and form accurate impressions of the poster? To investigate this, we put participants (N = 307) in imaginary opinion-based ingroup and outgroup online forums. In each, people were presented with different types of statements and asked for their impressions of the poster as well as of their own ingroup and outgroup. Negative impressions and intentions were more commonly linked to posters thought to be outgroup members, even when they exhibited similar behaviours to posters thought to be members of the ingroup. Notably, most types of contact increased people's liking of the ingroup and disliking of the outgroup. That said, a perceived effort to engage in genuine discussion over group matters by perceived outgroup posters appeared to shift outgroup impressions to be more positive. This highlights the potential benefit of deliberation in mitigating intergroup animosity.

In social contexts, learners need to infer the knowledge and intentions of the information provider and vice-versa. In this study, we tested how well participants could infer the intentions of different information providers in the rectangle game, where a fictional information provider revealed clues about the structure of a rectangle that the learner (a participant) needed to guess. Participants received clues from either a helpful information provider, a provider who was randomly sampling clues, or one of two kinds of unhelpful providers (who could mislead but could not lie). We found that people learned efficiently and in line with the predictions of a Bayesian pedagogical model when the provider was helpful. However, although participants could identify that unhelpful providers were not being helpful, they struggled to learn the strategy those providers were using, even when they had the opportunity to practise being a deceptive information provider.

A central question in cognitive science is how semantic information is mentally represented. Two dominant theories of semantic representation are language-based distributional semantic models (which suggest that word meaning is based on which words co-occur in language) and semantic networks based on word associations (which suggest that words are represented as a network in which words with closer meanings are more closely linked). We investigate the issue of semantic representation through the lens of core vocabulary -- the set of words that are most central in the mental lexicon -- which these two theories make different predictions about. We report on the results of an experiment that tests which measure of core vocabulary most closely aligns with human behaviour in a word-guessing game where the aim was to identify a target word given a set of semantically related words as hints. Target and hint words, which varied across trials, were generated from different core vocabulary lists corresponding to these different theories. Results revealed that the type of hint words did not affect performance, but that better performance was attained for target words derived from word associations than from natural language distributional statistics. Follow-up analyses ruled out several alternate explanations. Our results suggest that the semantic information reflected in word associations may be more involved in the efficient identification of lexical meaning.

High-frequency words are often assumed to be the most useful words for communication, as they provide the greatest coverage of texts. However, the relationship between text coverage and comprehension may not be straightforward -- some words may provide more information than others. In this study, we explore alternative methods of defining core vocabulary in addition to word frequency (e.g., words that are central hubs in semantic association networks). We report on the results of an empirical test of communicative utility using a text-based guessing game. We show that core words that reflect corpus-based distributional statistics (like frequency or co-occurrence centrality) were less useful for communication than others. This was evident both in terms of the size of the vocabulary that must be known and the proportion of the text that must be covered for successful communication.

In many situations where people communicate (e.g., Twitter, Facebook etc), people self-organise into ‘echo chambers’ of like-minded individuals, with different echo chambers espousing very different beliefs. Why does this occur? Previous work has demonstrated that such belief polarisation can emerge even when all agents are completely rational, as long as their initial beliefs are heterogeneous and they do not automatically know who to trust. In this work, we used agent-based simulations to further investigate the mechanisms for belief polarisation. Our work extended previous work by using a more realistic scenario. In this scenario, we found that previously proposed methods for reducing belief polarisation did not work but we were able to find a new method that did. However, this same method could be reversed by adversarial entities to increase belief polarisation. We discuss how this danger can be best mitigated and what theoretical conclusions be drawn from our findings.