We use data on a cultural fitness landscape, recently inferred from a large-scale cross-cultural survey of religious practices (6000+ years, 407 cultures), to provide new insights into the dynamics of cultural macroevolution. We report three main results. First, we observe an emergent distinction between the long-run fitness of a religious practice, and its short-term stability: in particular, some low-fitness practices are nonetheless highly stable. Second, despite the exponentially large size of the landscape, we find a small number of cultural attractors, and 70% of all observed configurations flow into just four, which we label "monastic", "evangelical", "indigenous", and "pre-Axial". Finally, we find large variation in the evolvability of different traits, with some (such as a belief in punishing gods) strongly fixed by context, and others (such as belief in reincarnation) much more fluid.

What cognitive abilities do we need to cooperate? Studies of simple cooperative games often focus on the role of reputation, reciprocity, and other-regarding preferences. Real-world social dilemmas, however, are characterized by the diverse capabilities and needs of the individuals within them. These create situations where reciprocity, for example, is not enough. A person who receives important help cannot directly return the favor. While diversity can present new ways to prosper, it may require different abilities to discover and maintain highly-structured cooperative "cycles". We present a multi-agent resource exchange game–the "Mars Colony game"–that captures the essence of these complexities. We study the impact of preferences (selfish and other-regarding) and strategies (direct and indirect reciprocity, and causal attribution) on emergent cooperation.

Prediction, explanation, and control are basic cognitive abilities. Here we show how they can arise, simultaneously, from underlying mental models built during unstructured, exploration-based learning. Our experimental paradigm, involving interaction with a symbolic "chatbot", allows us to vary the relative difficulty of the tasks, and to measure how participants leverage the Bayesian evidence of their mental models for decision-making. Our experimental manipulation focuses on hidden information and task complexity. With full information, there are significant differences between the three tasks: for example, people are more sensitive to Bayesian evidence in prediction than in control or explanation. When information is hidden, however, performance equalizes. Taken together, our results suggest that, while specific heuristics may lead to different levels of performance in cases with full information, more fundamental forms of reasoning, based on an underlying mental model, and less sensitive to the specific task, come into play when pieces are missing.

What kinds of arguments do people make, and what effect do they have on others? Normative constraints on argument-making are as old as philosophy itself, but little is known about the diversity of arguments made in practice. We use NLP tools to extract patterns of argument-making from the Reddit site "Change My View'' (r/CMV). This reveals six distinct argument patterns: not just the familiar deductive and inductive forms, but also arguments about definitions, relevance, possibility and cause, and personal experience. Data from r/CMV also reveal differences in efficacy: personal experience and, to a lesser extent, arguments about causation and examples, are most likely to shift a person's view, while arguments about relevance and presumption are the least. Finally, our methods reveal a gradient of argument-making preferences among users: a two-axis model, of "personal—impersonal'' and "concrete—abstract'', can account for nearly 80% of the strategy variance between individuals.

Asking and answering questions is a pervasive activity. Over and above the survival benefits it provides, it is one thatcan be intrinsically pleasurable. Word puzzles provide a window into this process that allow us to go beyond laboratoryinvestigations to capture how question-asking functions in the real world. Analysis of New York Times crosswords,and quiz-show Jeopardy questions allow us to tease apart two phenomena that make for difficult questions: opacity (theindirectness of cues within a clue), and obscurity (the rarity of the answer). Vector-space models of natural language revealhow synergistic cues aid the puzzle-solver, overcoming obscurity in ways that contemplation of cues in isolation can not,and show how these effects compete with the obscurity of the answer itself. Our methods provide new ways to measurethese phenomena in question-asking, and show how they operate in this most basic of behaviors.

Explanations are social, and when people try to explain something, they usually seek input from others. We present a simple theory of how people use the explanations they encounter as clues to the broader landscape of possible explanations, informing their decision to exploit what has been found or explore new possibilities. The challenge of coming up with novel explanations draws people to exploit or imitate appealing ones (information cascades); this draw increases as less appealing alternatives become more distant (the ``strawman'' effect). Conversely, pairs of low-quality explanations promote exploratory behavior or long-leaps away from observed attempts, and pairs of divergent high-quality explanations can lead to merging and syncretism. We use a transmission-chain experiment to test, and confirm, these predictions. Intriguingly, we also find that while people imitate good explanations, their imitations often fall short in quality. Our work provides new insight into how collective exploration can be promoted, or stalled, by implicit information about what is yet to be discovered.

Over their first years of life, children learn not just the words of their native languages, but how to use them to communicate. Because manual annotation of communicative intent does not scale to large corpora, our understanding of communicative act development is limited to case studies of a few children at a few time points. We present an approach to automatic identification of communicative acts using a Hidden Topic Markov Model, applying it to the CHILDES database. We first describe qualitative changes in parent-child communication over development, and then use our method to demonstrate two large-scale features of communicative development: (1) children develop a parent-like repertoire of our model's communicative acts rapidly, their learning rate peaking around 14 months of age, and (2) this period of steep repertoire change coincides with the highest predictability between parents' acts and children's, suggesting that structured interactions play a role in learning to communicate.