We don’t learn about each person we meet from scratch: Ourknowledge of social groups (e.g., cognitive scientists) shapesour expectations about new individuals (e.g., the reader). Herewe explore how 4- and 5-year-old children and adults use min-imal statistical evidence about social groups to support induc-tive inferences about individuals. Overall, we find that bothchildren and adults readily infer the preferences and groupmembership of new individuals when they have appropriateevidence to support these inferences. However, our resultsalso suggest that children and adults interpret this informa-tion in different ways. Adults’ responses align closely witha Bayesian model that assumes that each group’s preferencesare independent of one another. By contrast, we find prelimi-nary evidence that children’s inferences about the preferencesof new group members are sensitive to the composition (Exper-iment 1) and size (Experiment 2) of the opposing group. Ourwork provides insights into how people form structured, gen-eralizable representations of social groups from sparse data.

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. What drives people to disrupt the existing conceptual landscape and create new things? Here, we examine the decision to create new things under different levels of expected returns. We formalize innovation as stochastically recombining existing ideas, where successful and more complex combinations generate higher returns. This formalization allows us to cast innovation-seeking as a Markov decision process, and derive optimal policies under different settings. Data collected through an online behavioral experiment confirm our prediction that people should invest more time and effort in seeking innovations when they know the chances of success are high and the potential new ideas would be rewarding. However, people also deviate from being optimal, both innovating more and less than they should in different settings.

Social groups highlight latent structure in the social worldand support inductive inferences about individuals. In thepresent work, we examined children and adults’ intuitionsabout shared preferences within social groups. In Exp.1, 3- to5-year-old children treated preferences as a consistent propertyof social groups; that is, children expected members of a so-cial group to like the same toys that other members have liked.However, they did not treat preferences as diagnostic of socialgroups; they did not expect individuals to belong to a groupthat shares their preferences. By contrast, in Exp.2, adultsreadily treated preferences as both a consistent and diagnos-tic property of social groups. These results suggest that chil-dren’s inferences about social groups are asymmetric: Chil-dren readily infer preferences based on group membership, butnot group membership based on preferences.

Shared preferences are a critical component of social attrac-tion. Knowing that someone likes the same things as you do isindicative of broader underlying similarities that support suc-cessful social partnerships. However, not all overlaps in prefer-ences are equally informative. Here we propose that the rarityof overlaps in preferences may be a particularly salient cue forsocial affiliation. We find evidence that people are sensitiveto the rarity of overlaps in preferences and affiliate themselves(Experiment 1) or predict others’ affiliations (Experiment 2)with potential social partners who share a relatively rare pref-erence. Because preferences provide information about bothwhat people know and what they like, we also tested the ef-fect of overlaps in knowledge (without taste) and overlaps intaste (without knowledge) to understand why we are drawn topeople who share our preferences.

Children are keen explorers of the world: They systematically explore surprising findings and test hypotheses during play. However, less is known about whether toddlers are similarly driven to learn about the self. Here, we ask whether toddlers are intrinsically motivated to explore their own competence. In ongoing work, 2-year-olds (N = 12) play Montessori practical life games along with their parents; toys were verified to be equally appealing and challenging to toddlers in an independent norming experiment (N = 14). Within each pair, parents guide the toddler's hands while playing with one toy, which provides ambiguous information about the toddler's competence, and take turns playing with the other toy independently, which provides unambiguous information. Toddlers are then offered both toys to freely explore independently. Preliminary results show that toddlers explored the ambiguous toy first in 71% of the 31 trials, suggesting that toddlers seek out opportunities to learn about their competence.

Humans collaborate to improve productivity and collective outcomes, but people do not always exert maximal effort towards accomplishing collaborative goals. Instead, individuals often expend less effort in groups, a phenomenon known as social loafing that is traditionally viewed as detrimental to productivity. However, theories from distributed computer systems suggest that social loafing might be a rational response to the diminishing returns expected from division of labor when group size increases. Here, we examine how considerations of task efficiency affect the perceived acceptability of withholding effort during a collaborative task. We conducted experiments varying workload and group size across scenarios in which all group members except for one are actively contributing to a common goal. We then compare participant judgments to a model inspired by latency speed-up in distributed systems. We find that people are systematically influenced by task efficiency, in addition to social norms, when judging social loafing.

Cognitive capacities such as learning, reasoning, anddecision-making are often studied in tasks where a single par-ticipant acts in isolation. Yet humans don’t learn, reason, andmake decisions in a vacuum. Human cognition is distinc-tively social: Much of what we do influences—and is influ-enced by—other people.The goal of this workshop is to bring together diverse per-spectives on the interplay between human cognition and thedynamic, social environments we inhabit. The workshop isorganized around three key themes. Theme 1 lays out thecognitive tools that equip individuals to thrive in social en-vironments, including specialized mechanisms for teachingand learning from others. Theme 2 examines how the socialenvironment is itself shaped by the dynamic interactions be-tween multiple individuals, producing emergent behaviors atthe level of the collective. Finally, Theme 3 explores howhuman cognition responds to the demands of particular so-cial environments, including how cultural variability in socialcognition might emerge across development.Collectively, the research showcased in this workshopenriches this year’s conversation on “How to Develop aMind: Learning in Humans, Animals, and Machines”by highlighting the social and cultural context of learn-ing and development. In addition, our speakers representa broad cross-section of the conference, spanning multi-ple disciplines (computer science, anthropology, psychol-ogy), perspectives (computational, ecological, developmen-tal), and career stages (from research assistants to full pro-fessors). Below, we describe each theme and presentercontributions in detail. To take part in the workshop,visit cognitioncollectivesandculture.github.io forthe current schedule.

We often make decisions on behalf of others, such as pickingout gifts or making restaurant recommendations. Yet, withoutdirect access to others’ preferences, our choices on behalf ofothers depend on what we think they like. Across twoexperiments, we examined whether and how accuratelypeople are able to infer others’ preferences by observing theirchoices. Our results suggest that people are capable of makingreasonably accurate predictions about what others will choosenext, given what they have chosen before. These results laythe groundwork to systematically study how people makenovel predictions about others’ preferences, and whendifferent strategies might be appropriate.