When making decisions, humans often rely on information from their social networks through a process termed social sampling. Prior work suggests that when drawing social samples, people search through their contacts in a sequential manner based on structured social categories. We examined whether the problem domain impacts how one categorizes their social contacts and which social categories they sample from. In our study, participants answered questions about the relative popularity of national parks or social media platforms, respectively associated with offline activities and online activities. Participants then provided frequency information about the number of their contacts who have engaged in these activities. Adopting a hierarchical Bayesian modeling approach, we compared two social sampling models: one defining social groups based on closeness of social relations, and one defining social groups based on contact mode. Our findings suggest that people sample from different members of their social network depending on the type of decision they are making.

Analogy problems involving multiple ordered relations of the same type create mapping ambiguity, requiring some mechanism for relational integration to achieve mapping accuracy. We address the question of whether the integration of ordered relations depends on their logical form alone, or on semantic representations that differ across relation types. We developed a triplet mapping task that provides a basic paradigm to investigate analogical reasoning with simple relational structures. Experimental results showed that mapping performance differed across orderings based on category, linear order, and causal relations, providing evidence that each transitive relation has its own semantic representation. Hence, human analogical mapping of ordered relations does not depend solely on their formal property of transitivity. Instead, human ability to solve mapping problems by integrating relations relies on the semantics of relation representations. We also compared human performance to the performance of several vector-based computational models of analogy. These models performed above chance but fell short of human performance for some relations, highlighting the need for further model development.

While narratives have shaped cognition and cultures for centuries, digital media and online social networks have introduced new narrative phenomena. With increased narrative agency, networked groups of individuals can directly contribute and steer narratives that center our collective discussions of politics, science, and morality. We report the results of an online network experiment on narrative and hashtag generation, in which networked groups of participants interpreted a text-based narrative of a disaster event, and were incentivized to produce matching hashtags with their network neighbors. We found that network structure not only influences the emergence of dominant beliefs through coordination with network neighbors, but also impacts participants' use of causal language in their personal narratives.